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Bench2Byte

Appearance

404

PAGE NOT FOUND

But if you don't change your direction, and if you keep looking, you may end up where you are heading.
Take me home

Released under the MIT License.

Copyright © 2024-present Will Hannon

+ + + + \ No newline at end of file diff --git a/README.html b/README.html new file mode 100644 index 0000000..505c006 --- /dev/null +++ b/README.html @@ -0,0 +1,55 @@ + + + + + + Bench to Byte: A biologist's guide to crafting quality code | Bench to Byte + + + + + + + + + + + + + + +
Bench2Byte

Appearance

Bench to Byte: A biologist's guide to crafting quality code ​

This guide is still under active development.

I made this guide for biologists with moderate programming experience who want to improve the way they work. You'll learn how to organize your projects, use remote resources, create reproducible software environments, and build computational pipelines.

This guide was made with members of the Bloom lab in mind. In the future, I might expand this resource to apply to a more general audience.

Developing ​

To develop Bench to Byte, you'll need to set up a few things. Specifically, you'll need two pieces of software: Node.js and npm. Node.js is an environment that allows you to run Javascript code on your computer. npm is a package manager that handles the Javascript libraries necessary to create this website.

The instructions for installing Node and npm depend on your operating system and personal preference. For detailed instructions check out this website.

However, if you have conda installed, you can use conda to install node and npm using the environment.yml file in the repo.

bash
conda env create -f environment.yml
+conda activate bench-to-byte

Once Node and npm are installed, verify by running:

bash
node -v
+npm -v

You should see the node and npm versions that you installed on your system.

If this is the first time you're developing bench-to-byte, you'll need to install the packages in packages.json. To do this, run the following command from within the repository:

bash
npm install

With everything installed, you can now boot up a live preview of the website that automatically reflects your changes upon saving.

bash
npm run docs:dev

Now, there will be a local version of the website running at http://localhost:5173. Visit this URL in your browser to see a preview of the site.

Note, If you are developing bench-to-byte from a remote server (not your local machine), use

bash
npm run remote:docs:dev

and access the preview at the URL provided under Network

Contributing ​

Bench to Byte is based on the static site generator VitePress. This allows you to create fully functional web pages using Markdown files. To add a new page to the site, you simply create a markdown document in the correct directory and make a pull request.

For more details on writing markdown for a Vitepress website, see the markdown reference here.

Project Structure ​

bash
.
+├── README.md
+├── environment.yml
+├── index.md
+├── chapters
+│   ├── chatgpt-and-llms
+│   │   └── index.md
+│   ├── coding-best-practices
+│   │   └── index.md
+│   ├── creating-workflows-and-pipelines
+│   │   └── index.md
+│   ├── introduction
+│   │   └── index.md
+│   ├── managing-software-environments
+│   │   └── index.md
+│   ├── organizing-your-projects
+│   │   └── index.md
+│   ├── setting-up-your-ide
+│   │   └── index.md
+│   ├── tracking-your-code
+│   │   └── index.md
+│   ├── using-remote-resources
+│   │   └── index.md
+│   └── working-collaboratively
+│       └── index.md
+├── package-lock.json
+├── package.json
+└── public
+    └── logo.png

To add details to a specific chapter of the course, simply edit the index.md file under the corresponding chapters/<chapter-you-are-editing>/ directory. If you want to include images in your markdown, you can either use a link to a remotely hosted image or add the image to the public/ directory and reference it like so:

bash
![Alt Text](/my-image.png)

If you've contributed in some way to this guide, add your name and a link to a photo in public/contributors.json.

Suggestions ​

If you have any suggestions for topics that aren't covered in this giude, please suggest them in the corresponding Discussions thread.

Released under the MIT License.

Copyright © 2024-present Will Hannon

+ + + + \ No newline at end of file diff --git a/assets/README.md.hhc_Bc4e.js b/assets/README.md.hhc_Bc4e.js new file mode 100644 index 0000000..f70419d --- /dev/null +++ b/assets/README.md.hhc_Bc4e.js @@ -0,0 +1,31 @@ +import{_ as s,c as i,o as e,V as a}from"./chunks/framework.Cw5Ferw_.js";const F=JSON.parse(`{"title":"Bench to Byte: A biologist's guide to crafting quality code","description":"","frontmatter":{},"headers":[],"relativePath":"README.md","filePath":"README.md"}`),n={name:"README.md"},t=a(`

Bench to Byte: A biologist's guide to crafting quality code ​

This guide is still under active development.

I made this guide for biologists with moderate programming experience who want to improve the way they work. You'll learn how to organize your projects, use remote resources, create reproducible software environments, and build computational pipelines.

This guide was made with members of the Bloom lab in mind. In the future, I might expand this resource to apply to a more general audience.

Developing ​

To develop Bench to Byte, you'll need to set up a few things. Specifically, you'll need two pieces of software: Node.js and npm. Node.js is an environment that allows you to run Javascript code on your computer. npm is a package manager that handles the Javascript libraries necessary to create this website.

The instructions for installing Node and npm depend on your operating system and personal preference. For detailed instructions check out this website.

However, if you have conda installed, you can use conda to install node and npm using the environment.yml file in the repo.

bash
conda env create -f environment.yml
+conda activate bench-to-byte

Once Node and npm are installed, verify by running:

bash
node -v
+npm -v

You should see the node and npm versions that you installed on your system.

If this is the first time you're developing bench-to-byte, you'll need to install the packages in packages.json. To do this, run the following command from within the repository:

bash
npm install

With everything installed, you can now boot up a live preview of the website that automatically reflects your changes upon saving.

bash
npm run docs:dev

Now, there will be a local version of the website running at http://localhost:5173. Visit this URL in your browser to see a preview of the site.

Note, If you are developing bench-to-byte from a remote server (not your local machine), use

bash
npm run remote:docs:dev

and access the preview at the URL provided under Network

Contributing ​

Bench to Byte is based on the static site generator VitePress. This allows you to create fully functional web pages using Markdown files. To add a new page to the site, you simply create a markdown document in the correct directory and make a pull request.

For more details on writing markdown for a Vitepress website, see the markdown reference here.

Project Structure ​

bash
.
+├── README.md
+├── environment.yml
+├── index.md
+├── chapters
+│   ├── chatgpt-and-llms
+│   │   └── index.md
+│   ├── coding-best-practices
+│   │   └── index.md
+│   ├── creating-workflows-and-pipelines
+│   │   └── index.md
+│   ├── introduction
+│   │   └── index.md
+│   ├── managing-software-environments
+│   │   └── index.md
+│   ├── organizing-your-projects
+│   │   └── index.md
+│   ├── setting-up-your-ide
+│   │   └── index.md
+│   ├── tracking-your-code
+│   │   └── index.md
+│   ├── using-remote-resources
+│   │   └── index.md
+│   └── working-collaboratively
+│       └── index.md
+├── package-lock.json
+├── package.json
+└── public
+    └── logo.png

To add details to a specific chapter of the course, simply edit the index.md file under the corresponding chapters/<chapter-you-are-editing>/ directory. If you want to include images in your markdown, you can either use a link to a remotely hosted image or add the image to the public/ directory and reference it like so:

bash
![Alt Text](/my-image.png)

If you've contributed in some way to this guide, add your name and a link to a photo in public/contributors.json.

Suggestions ​

If you have any suggestions for topics that aren't covered in this giude, please suggest them in the corresponding Discussions thread.

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I've spent a lot of time helping people troubleshoot programming issues, do data analysis, and get set up computationally. If you interviewed people about this experience, a common theme would emerge; I spend a lot of time asking the internet for help. Whether it's a forum like Stack Overflow or an AI chatbot like ChatGPT, you can figure out anything with the internet.

When it comes to programming, my philosophy is that it's much more important to grasp a concept than commit specifics to memory. If you understand the concept behind what you're trying to accomplish, the internet will always be able to fill in the specifics. That's why this is the first section.

While you're following this guide, you're bound to run into the occasional issue or topic that's lacking detail. The internet will help, but how do you decide which resources are appropriate for your questions? Below, I'll address the main online resources that I use.

Note

Almost no resource is free of factual inaccuracies: LLMs 'hallucinate', people writing posts on Stack Overflow might have no idea what they're talking about, and even programmers writing documentation make mistakes.

I have a specific goal and lack any background knowledge ​

If you know vaguely what you want to accomplish but nothing about how to accomplish it, I'd recommend googling a recent tutorial. For example, if you're trying to figure out how to host a website with GitHub Pages, read their current documentation or watch a video tutorial rather than going to a forum or using an LLM. This is for two reasons: (1) Forums and LLMs will not always be up-to-date, and (2) they make mistakes that you won't be able to catch without a little background know-how.

I have a specific goal and have a little background knowledge ​

In this case, LLMs are almost always the best approach. LLM stands for Large Language Model. These are machine learning models that are trained on vast amounts of text, usually scraped from the internet, to understand, generate, and predict new text. They learn patterns, enabling them to perform a variety of tasks including proof-reading, generating, and answering questions about code.

Because LLMs are trained on data scraped from the internet, they tend to be better at answering questions with lots of examples to train on. For this reason, LLMs are great at answering common coding questions and writing code in popular languages like Python and Javascript. However, if your question is about something obscure, like a tool that's only used by the Bloom Lab, you won't get a useful answer.

Also, LLMs can 'hallucinate' and respond confidently with factually inaccurate answers. For this reason, I only use LLMs when I feel that I can identify these mistakes or I have a way of validating the response.

Helpful Resources ​

There are several different LLMs that people use for coding. Personally, I like Open AI's ChatGPT. It's a good generalist model that can answer coding questions as well as performing other tasks. I'd recommend the paid model, at the time of writing this it makes a significant difference.

Another popular LLM that theoretically performs coding tasks better than ChatGPT is Anthropic's Claude. At the time of writing it's well liked by several members of the lab.

Additionally, there are coding specific models like GitHub's Copilot. Copilot is nice because it's integrated directly into the development environment that you write code in giving you suggestions as you go. I use it, but mostly as intelligent autocomplete rather than a resource for asking questions. It's nice, but not as valuable as ChatGPT or Claude.

',14);function u(t,p,g,m,f,w){return s(),o("div",null,[e("h1",c,[n(r(t.$frontmatter.title)+" ",1),h]),d])}const y=a(l,[["render",u]]);export{k as __pageData,y as default}; diff --git a/assets/sections_chatgpt-and-llms_index.md.r93Rcp6X.lean.js b/assets/sections_chatgpt-and-llms_index.md.r93Rcp6X.lean.js new file mode 100644 index 0000000..09edf23 --- /dev/null +++ b/assets/sections_chatgpt-and-llms_index.md.r93Rcp6X.lean.js @@ -0,0 +1 @@ +import{_ as a,c as o,m as e,a as n,t as r,V as i,o as s}from"./chunks/framework.Cw5Ferw_.js";const k=JSON.parse('{"title":"Section 0: Coding Smarter with Online Tools","description":"","frontmatter":{"layout":"doc","title":"Section 0: Coding Smarter with Online Tools","sidebar":true,"aside":true},"headers":[],"relativePath":"sections/chatgpt-and-llms/index.md","filePath":"sections/chatgpt-and-llms/index.md"}'),l={name:"sections/chatgpt-and-llms/index.md"},c={id:"frontmatter-title",tabindex:"-1"},h=e("a",{class:"header-anchor",href:"#frontmatter-title","aria-label":'Permalink to "{{ $frontmatter.title }}"'},"​",-1),d=i("",14);function u(t,p,g,m,f,w){return s(),o("div",null,[e("h1",c,[n(r(t.$frontmatter.title)+" ",1),h]),d])}const y=a(l,[["render",u]]);export{k as __pageData,y as default}; diff --git a/assets/sections_coding-best-practices_index.md.Dta9vFSp.js b/assets/sections_coding-best-practices_index.md.Dta9vFSp.js new file mode 100644 index 0000000..bb99e45 --- /dev/null +++ b/assets/sections_coding-best-practices_index.md.Dta9vFSp.js @@ -0,0 +1,54 @@ +import{_ as a,c as n,m as s,a as e,t,V as l,o}from"./chunks/framework.Cw5Ferw_.js";const m=JSON.parse('{"title":"Section 8: Coding best practices","description":"","frontmatter":{"layout":"doc","title":"Section 8: Coding best practices","sidebar":true,"aside":true},"headers":[],"relativePath":"sections/coding-best-practices/index.md","filePath":"sections/coding-best-practices/index.md"}'),r={name:"sections/coding-best-practices/index.md"},h={id:"frontmatter-title",tabindex:"-1"},p=s("a",{class:"header-anchor",href:"#frontmatter-title","aria-label":'Permalink to "{{ $frontmatter.title }}"'},"​",-1),k=l(`

Hopefully, you're already aware of the importance of properly organizing your projects, tracking your code, and organizing complex analyses into automated workflows. In this final section, I'll highlight some important language-agnostic tips for improving your coding.

Following conventions ​

Each programming language has established conventions that cover aspects like code formatting, indentation, naming styles, and file organization. Adhering to these is crucial for writing clean, readable, and maintainable code.

There are often style guides that outline common conventions for programming languages. For examples, Python has the PEP 8 style guide, which recommends using 4 spaces for indentation, limiting lines to 79 characters, and using lowercase with underscores for function and variable names.

Remembering all of these recommendation while you're coding would be a huge pain. Thankfully, code formatters like black can automatically format code to adhere to a specific style.

Naming variables ​

Choosing the right names for your variables is like labeling boxes in the -80C: it helps you and others understand what's inside without digging through them. Here are some universal best practices for naming variables:

  • Don't Use Reserved Words: Most programming languages have 'reserved words' with special meanings (e.g. for, true, and if). Familiarize yourself these words so you don't accidentally name something with one. Typically, a good syntax highlighter or linter will catch these mistakes.
  • Use Descriptive Names: Use clear and descriptive names that convey the purpose of the variable. For example, if you have a data frame of gene expression data use gene_expression_df instead of just using df.
  • Avoid Abbreviations: Unless they are standard abbreviations that people reading your code are likely to understand.
  • Be Consistent: Stick to a naming convention and use it consistently. Each programming language has preferred naming conventions for variables, classes, and functions. For example, variables in Python are usually written in snake_case whereas variables in Javascript are written in camelCase.

Commenting code ​

Comments are annotations in your code that explain what's happening, but they should be used judiciously. It's tempting for a beginning programmer to comment every line of code. This practice can actually make the code harder to read. Generally, you want to say why code is the way it is, not what the code is doing. If you're following the advice mentioned in this section, your code should be pretty clear even in the absence of comments.

Here's an example of well commented code from a Python packaged developed by the Bloom lab called Polyclonal:

python
def corr_heatmap(
+    corr_df,
+    corr_col,
+    sample_cols,
+    *,
+    group_col=None,
+    corr_range=(0, 1),
+    columns=3,
+    diverging_colors=None,
+    scheme=None,
+):
+    """Plot a correlation matrix as heat map from a tidy data frame of correlations.
+
+    Parameters
+    ----------
+    corr_df : pandas.DataFrame
+        Data to plot.
+    corr_col : str
+        Column in \`corr_df\` with correlation coefficient.
+    sample_cols : str or list
+        Column(s) in corresponding to sample identifiers, suffixed by "_1" and "_2" for
+        the distinct samples. Should be entries for all pairs of samples.
+    group_col : str or None
+        Column in \`corr_df\` to facet plots on, or \`None\` if no facets.
+    corr_range : tuple or None
+        Range of heat map as \`(min, max)\`, or \`None\` to use data range. Typically
+        you will want to set to \`(0, 1)\` for :math:\`r^2\` and \`(-1, 1)\` for :math:\`r\`.
+    columns : int
+        Facet by \`group_col\` into this many columns.
+    diverging_colors : None or bool
+        If \`True\`, mid point of color scale is set to zero. If \`None\`, select \`True\`
+        if \`corr_range\` extends below 0.
+    scheme : None or str
+        Color scheme to use, see https://vega.github.io/vega/docs/schemes/.
+        If \`None\`, choose intelligently based on \`corr_range\` and \`diverging_colors\`.
+
+    Returns
+    -------
+    altair.Chart
+        Heatmap(s) of correlation coefficients.
+
+    """
+    corr_df = corr_df.copy()  # so we don't change input data frame
+
+    if corr_col not in corr_df:
+        raise ValueError(f"{corr_col=} not in {corr_df.columns=}")
+
+    if corr_range is None:
+        corr_range = (corr_df[corr_col].min(), corr_df[corr_col].max())
+    else:
+        if corr_range[0] > corr_df[corr_col].min():
+            raise ValueError(f"{corr_range[0]} > {corr_df[corr_col].min()=}")
+        if corr_range[1] < corr_df[corr_col].max():
+            raise ValueError(f"{corr_range[1]} < {corr_df[corr_col].max()=}")

Notice two things: (1) the function has a 'Docstring' that describes the function's purpose, inputs, and outputs. (2) The code is commented judiciously, and uses comments to explain aspects of the code that aren't self-explanatory.

Modularizing code ​

Breaking your code into smaller, reusable pieces makes it easier to manage and understand. Those pieces can be functions, classes, or modules depending on the conventions of the programming language. Generally, you don't want to have repetitive code. If you're copying and pasting code from one part of your script into another, that's a sign that you should be modularizing you code with functions or classes.

Logging, Errors, and Warnings ​

It's important to use logs, warnings, and errors to monitor what's going on with your code. You should have expectations about how your code is supposed to work. These features help ensure that those expectations are met. They prevent silent bugs and help make your code user-friendly.

Notebooks vs Scripts ​

Programming languages that are commonly used for data analysis, like Python and R, have interactive environments called 'Notebooks' that combine code execution, text, and visualizations in a single document. Notebooks are awesome tools for data analysis, but sometimes scripts are better suited to the task. How do you decide whether to use a Notebook or a script?

When to use a Notebook (i.e. Jupyter Notebooks):

Notebooks are awesome because they combine code, visualizations, and documentation in a single location. You can also run code interactively. I typically use Notebooks for:

  • Prototyping code: It's hard to beat the convenience of running code interactively.
  • Documenting an analysis: If I'm working on an analysis that I want to document for other people it's nice to have code, writing, and plots in a single location.

When to use a script:

Scripts a great way of modularizing code that you're going to run often. I typically use scripts over Notebooks in the following situations:

  • Frequently used code: Scripts are smaller, faster, and simpler to run than Notebooks. I use scripts to modularize code that functions as a tool I plan to run frequently.
`,25);function d(i,c,g,y,E,u){return o(),n("div",null,[s("h1",h,[e(t(i.$frontmatter.title)+" ",1),p]),k])}const f=a(r,[["render",d]]);export{m as __pageData,f as default}; diff --git a/assets/sections_coding-best-practices_index.md.Dta9vFSp.lean.js b/assets/sections_coding-best-practices_index.md.Dta9vFSp.lean.js new file mode 100644 index 0000000..df2da46 --- /dev/null +++ b/assets/sections_coding-best-practices_index.md.Dta9vFSp.lean.js @@ -0,0 +1 @@ +import{_ as a,c as n,m as s,a as e,t,V as l,o}from"./chunks/framework.Cw5Ferw_.js";const m=JSON.parse('{"title":"Section 8: Coding best practices","description":"","frontmatter":{"layout":"doc","title":"Section 8: Coding best practices","sidebar":true,"aside":true},"headers":[],"relativePath":"sections/coding-best-practices/index.md","filePath":"sections/coding-best-practices/index.md"}'),r={name:"sections/coding-best-practices/index.md"},h={id:"frontmatter-title",tabindex:"-1"},p=s("a",{class:"header-anchor",href:"#frontmatter-title","aria-label":'Permalink to "{{ $frontmatter.title }}"'},"​",-1),k=l("",25);function d(i,c,g,y,E,u){return o(),n("div",null,[s("h1",h,[e(t(i.$frontmatter.title)+" ",1),p]),k])}const f=a(r,[["render",d]]);export{m as __pageData,f as default}; diff --git a/assets/sections_creating-workflows-and-pipelines_index.md.CKk2bu3I.js b/assets/sections_creating-workflows-and-pipelines_index.md.CKk2bu3I.js new file mode 100644 index 0000000..46424e1 --- /dev/null +++ b/assets/sections_creating-workflows-and-pipelines_index.md.CKk2bu3I.js @@ -0,0 +1 @@ +import{_ as t,c as o,m as e,a as n,t as i,V as s,o as r}from"./chunks/framework.Cw5Ferw_.js";const b=JSON.parse('{"title":"Section 7: Creating reproducible workflows and pipelines","description":"","frontmatter":{"layout":"doc","title":"Section 7: Creating reproducible workflows and pipelines","sidebar":true,"aside":true},"headers":[],"relativePath":"sections/creating-workflows-and-pipelines/index.md","filePath":"sections/creating-workflows-and-pipelines/index.md"}'),l={name:"sections/creating-workflows-and-pipelines/index.md"},d={id:"frontmatter-title",tabindex:"-1"},c=e("a",{class:"header-anchor",href:"#frontmatter-title","aria-label":'Permalink to "{{ $frontmatter.title }}"'},"​",-1),p=s('

In biology, we often string together bits of code, scripts, and tools to complete a single analysis. Each step runs sequentially to produce some desired output. Instead of remembering the order and manually running each step, you should take advantage of programming tools to codify these steps in a pipeline. In this section, I'll describe what a pipeline is, when you should write one, and what tools are available to make it easier.

What's a pipeline? ​

Pipelines group together different computational steps one after the other. People often accomplish this with bash scripts or by manually running commands in sequence. But there's a better way. You can use a workflow language. These languages are designed to handle complex workflows, manage dependencies, and automate the execution of tasks. They're smart because they can:

  • Automatically detect dependencies: Determine which steps depend on others.
  • Optimize execution: Run independent tasks in parallel.
  • Resume from failures: Restart workflows from the point of failure without redoing completed tasks.
  • Ensure reproducibility: Keep a record of all steps for consistent results. Imagine being able to run a large complex analysis with a single command.

When should you write an analysis as a pipeline? ​

If your analysis is more complex than a single script or command, a pipeline can significantly improve efficiency and reliability. This is especially true when certain steps depend on the outputs of previous ones. You should use pipelines to manage these relationships efficiently.

What tools are available for writing pipelines? ​

There are several tools designed to help you write pipelines including Snakemake, Make, Nextflow, and CWL. Each tool has its strengths, but Snakemake and Nextflow are the most commonly used tools used in Bioinformatics.

Snakemake ​

We primarily use Snakemake in the Bloom lab. Snakemake is a workflow management system that uses a a Python-based language to define rules, inputs, outputs, and commands. In Snakemake, you define a series of rules in a file called a Snakefile. Each rule specifies:

  • Targets (Outputs): The files or results you want to produce.
  • Dependencies (Inputs): The files required to produce the targets.
  • Actions (Shell Commands or Scripts): The commands to execute.

Snakemake automatically builds a workflow based on these rules, figuring out the order of execution by analyzing the dependencies. The best way to learn Snakemake is by following the tutorial in it's documentation.

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I made this guide for biologists with moderate programming experience who want to improve the way they work. This guide is not intended to teach you a particular coding language (here's a quick aside about that). Instead, you'll learn how to organize your projects, use remote resources, create reproducible software environments, and build computational pipelines. My goal is that you not only learn the how but also the why behind each practice.

A Quick Note

This guide was made with members of the Bloom lab in mind. In the future, I might expand this resource to apply to a more general audience.

Outline ​

Although each section is useful in isolation, you'll get the most out of this guide if you follow it in order.

  1. Coding Smarter with Online Tools

    Taking advantage of online resources

  2. Setting up your IDE

    Using VSCode to streamline your coding workflow

  3. Organizing your projects

    Creating clearly named, well organized projects

  4. Using remote resources

    Increasing your computational capabilities with remote resources

  5. Managing software environments

    Using conda to isolate software environments

  6. Tracking your code

    Versioning your code with git

  7. Working collaboratively

    Collaborating with GitHub

  8. Reproducible workflows and pipelines

    Building reproducible pipelines with Snakemake

  9. Coding best practices

    Writing clear code

Learning to Program ​

Some basic programming knowledge is necessary to get the most out of this guide. Below, I'll give a quick overview of the programming languages that are widely used by biologists so you can pick the language that best suites your needs. ß Generally, programming languages are either compiled––you write a script and an intermediate software called a compiler transforms it into an executable program––or interpreted––you write a script that can be executed at any point without a compilation step. Compiled languages are fast and easy to run on different operating systems; however, they tend to be more difficult to learn, run, and debug. Interpreted languages are usually not as fast as compiled languages, but they're much easier to learn. Both types of language have a place in biological programming. Compiled languages are great for writing programs that run quickly on huge amounts of data (like an aligner). Interpreted languages are great for data analysis and plotting. Most biologists will want to learn an interpreted language.

Common compiled programming languages used by biologists are C/C++, Java, and Rust. They're used to write short-read aligners like BWA (C/C++), genome browsers like IGV (Java), and tools for single-cell genomics like cellranger (Rust). You're likely to interact with tools written in a compiled language, but you probably won't need to write your own.

Common interpreted languages are Python, R, Javascript, and Perl. Python and R are the most widely used by biologists. Perl was common among biologists, but has fallen out favor. Javascript is the principle programming language of the web and is occasionally used by biologists to build websites and interactive dashboards. If you're a biologist who want's to learn programming, stick to with Python or R.

When choosing whether to learn Python or R, consider the following factors:

1. What are my research needs?

Python and R share most of the same core features, but they have different strengths. Python is a better general programming language with many useful libraries for biological problems. R is geared towards statistics and data analysis, so programming for these scenarios is more natural. Additionally, R has more common packages for analyzing gene expression and single-cell data. Learn the language with the best set of tools for the type of research you're doing.

2. What are people around me using?

If possible, learn the language that people around you (in your lab or institute) are using. It makes it easier to collaborate and they'll be a valuable resource while you're learning.

TIP

Learn Python if you're a member of the Bloom lab.

Suggestions ​

If you have any suggestions for topics that aren't covered in this guide, please suggest them in the corresponding Discussions thread.

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Biological programming typically involves a combination of writing your own code and using tools developed by others. This can lead to projects with many dependencies, each of which has specific version requirements. It's crucial for reproducibility that these software environments are tracked and managed. Otherwise, you'd be the only one who can run your analysis pipeline. Managing this by hand would be difficult. Thankfully, there are tools like Conda for managing software environments.

What is Conda? ​

Conda is an open-source tool for managing both software packages. It allows you to install software and their dependencies in isolated virtual environments. This means you can easily switch between projects, each with its own set of package versions, without worrying about conflicts between dependencies.

With Conda, you can ​

  • Install the software you need (e.g., bioinformatics tools, Python packages) with all required dependencies
  • Easily switch between environments
  • Share environment "recipes" for reproducibility

We make extensive use of Conda in the Bloom Lab. However, there are alternative solutions for managing software environments that you might run into, the most common of which is Docker. I won't describe Docker in this guide, but it's worth reading about.

Conda vs. Mamba ​

Mamba is a re-implementation of Conda in C++ that offers the same functionality but is often faster, especially when resolving complex dependencies or handling large environments. I tend to use Mamba for building environments and Conda for quick operations like activating and deactivating an environment. However, this is just a habit, and Mamba's version of Conda's commands are basically identical.

Which should you use? Conda works fine for small, simple environments. Mamba can save you time for larger or more complex environments. If you're setting up Conda from scratch using the instructions below, use Mamba. If you've already been using Conda with an installation of miniconda, and you would need to install Mamba, stick with Conda.

Setting up Conda on Rhino ​

Conda isn't pre-installed on Rhino, so you'll have to set it up yourself. The easiest way to do this is by installing a lightweight version of Conda called miniconda or its open-source alternative, miniforge, which comes pre-configured with Mamba. If you're starting from scratch, I'd highly recommend installing miniforge instead of miniconda. If you've already installed miniconda, the process of uninstalling and re-installing a Conda distribution isn't worth the marginal advantages offered by miniforge.

Installing miniforge ​

TIP

A more comprehensive version of the following instructions is available here.

We're going to install Mamba and Conda on Rhino by downloading miniforge into our home directory (the default directory when you log into Rhino, i.e. /home/username).

Run the following command in home (/home/username) to fetch and run the latest miniforge build script from the web.

bash
curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
+bash Miniforge3-$(uname)-$(uname -m).sh

You should follow the prompts during the installation and accept all the default options. Say yes to accepting the license, use the default recommended installation location (~/miniforge3), and say yes to the final question about whether to initialize the environment.

The build script will edit your .bashrc (a script that runs every time you log into Rhino) file with code that activates Mamba. This happens automatically, but to trigger the activation of Mamba this first time without logging out, run the following command.

bash
source .bashrc

Now, instead of the usual prompt (username@rhino02:~$ ), you'll see a new prompt that looks like: (base) username@rhino02:~$ . The name inside of the parenthesis indicates the Conda environment that's activated. The default environment when you log into Rhino is always base.

Conda Basics ​

Conda has lots of commands and arguments, but you only need to know a few basics to get a lot of utility out of it. Note that conda and mamba are synonymous in the following sections.

Making a named environment ​

To create a new environment with a specific name, you can use the create command. By default, this will create an isolated environment where you can install any packages you need. This is a great option for making an environment for a specific task.

bash
conda create --name myenv

You can also specify the version of Python or any other package you want in the environment. For example, to create an environment called myenv with Python 3.9 installed:

bash
conda create --name myenv python=3.9

After creating the environment, you can activate it using:

bash
conda activate myenv

This switches your terminal into the isolated environment, allowing you to install packages specific to that project. You'll see the prompt change from something like (base) username@rhino02:~$ to (myenv) username@rhino02:~$ to reflect that myenv is active. To deactivate and return to your base environment:

bash
conda deactivate

Making an environment from a file ​

If someone has already created an environment and shared it with you, or if you want to share yours with others, you can easily set up an environment from an environment file (usually environment.yml). This is very common practice in the Bloom lab since we're often running analysis pipelines with similar dependencies.

To create an environment from a file, use:

bash
conda env create --file environment.yml

This will install all packages listed in the file and configure the environment exactly as defined.

The environment.yml file will look something like this:

yaml
name: myenv
+
+channels:
+  - conda-forge
+  - bioconda
+  - defaults
+
+dependencies:
+  - altair=5.3
+  - biopython=1.83
+  - mafft=7.525

It specifies the name of the environment, like places (channels) to search for each software, and the software (dependencies) and their versions. You activate the environment the same way described above.

Common issues ​

While Conda is a helpful tool, you're bound to run into issues occasionally. Below are some common issues you might encounter and how to troubleshoot them.

My environment is taking a very long time to solve ​

This is a common issue when Conda struggles to resolve dependencies. Here are a few ways to address this:

  • Use Mamba: Mamba is faster at resolving dependencies, especially in complex environments. Simply replace conda with mamba in your commands:
bash
mamba create --name myenv python=3.9

If you're using conda from an installation of miniconda, you can use mamba by installing it like any other dependency into your base environment.

  • Specify fewer package versions: Conda and Mamba struggle when too many version constraints are placed on packages. If possible, relax some of these constraints and let Conda handle version selection. In most cases, a dependency doesn't need to pinned at a specific minor version.

  • Split up a big environment into smaller environments: The main strength of conda is that you can create isolated environments. Use that to your advantage and split up big environments into small ones and activate them as needed.

I'm getting permissions issues ​

This is a specific issue that comes up in the Bloom lab. If you've configured Conda so that there are multiple versions available at the same time––a local version in /home/username and a Bloom lab version in /fh/fast/bloom_j/software––you will eventually run into this problem.

  • Edit your .bashrc so that you're not accessing both versions of Conda: In other words, only use your local version of Conda.

  • Check your $PATH variable: There is a variable on Rhino called $PATH that contains a list of paths that Rhino searches for software. If you type a command like python, Rhino searches sequentially through each path in $PATH until it finds the first mention of python. Depending on the order of paths, it might end up accessing python from a place you don't expect. The same is true for a command like conda or mamba. You can check the order of paths by entering the command echo $PATH. You can edit the $PATH variable to change the order of paths.

I can't solve my environment ​

Sometimes Conda won't be able to resolve package dependencies in your environment's specification. If someone else has successfully created an environment from this specification it's likely an issue with how Conda is searching channels.

Channels are repositories that Conda searches for package versions. Conda searches these in a particular order depending on how you've configured it. In most cases, the solution is to make sure you've set up 'strict channel priorities'.

bash
conda config --set channel_priority strict

This means that Conda will always search through channels in the order they're specified. If that doesn't fix the issue, change the order of the channels until the environment can solve. If that doesn't work, check that the dependencies actually exist in the channels you're searching.

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Organization matters in programming. Your goal should be that someone who isn't you can open your project directory and understand where things are, what things are, and why things are. Ideally, you want to front-load the organizational work for a new project. If you've ever done any significant reorganization of a mature project, you'll know how much of a headache that can be.

Below, I'll go through some high-level organizational principles that you should follow. There isn't one right way to do things, but there sure are a lot of wrong ways.

Creating a project ​

All of the code for a single project belongs in one directory. You should not split code for a single project among multiple distinct directories. Everything should be housed under one roof. But what is a project?

Imagine that you're researching the function of your favorite protein. You're probably taking advantage of a variety of assays and tools, each of which is analyzed in a particular fashion. It could be tempting to split each of these analyses into separate directories housed in different locations in your file system, but this is not good practice. Instead, organize them under a single directory.

Housing all of the analysis files for a project in a single directory has a variety of benefits:

  • It simplifies the process of backing up your code and analysis.
  • It makes it easy to share the relevant code when it comes time to publish.
  • It lets you track/version all of the analyses in your project with tools like git.

Generally, a good rule of thumb is a project corresponds to all of the analysis for a single paper. However, there can be exceptions for large papers that involve many distinct and involved computational pipelines. At the end of the day, you have to use your best judgment.

I'll use this project from the Bloom Lab as an example in the following sections.

Naming a project ​

Names should be simple and descriptive. The name should communicate the purpose of the project, but it shouldn't be so long that it's annoying to type out. Generally, you should ask yourself if the name you've chosen is:

  • Descriptive
  • Readable
  • Succinct
  • Consistent

Take this project as an example. Bernadeta Dadonaite from the Bloom Lab performed deep mutational scanning on the SARS-CoV-2 Delta variant Spike protein to identify mutations that escape neutralization by the therapeutic antibody REGN10933. All of the code for this analysis is located in a directory called 'SARS-CoV-2_Delta_spike_DMS_REGN10933'. The name clearly describes the project without being overly wordy. It also distinguishes this project from similar ones while adhering to the same naming scheme.

Organizing a project ​

After you've decided what your project is and how to name it, you should think about how to organize the files within. There are many correct ways to do this, but there are some general principles that you should try to follow. Below, we'll cover some of these general principles.

Project organization should be consistent. If you're working with someone opinionated about project organization, you should do your best to use their approach (or they'll be upset). Another thing to consider is whether there is an established set of best practices for the type of project you're working on. For example, a Snakemake pipeline typically adheres to a specific organizational structure. By sticking to this, you'll make it easier to develop your project and easier for others to follow along.

If you're working alone and there isn't a clear template for organizing your project, here are simple set of rules to follow:

  1. ALWAYS write a README: A 'README' is a plain text file––typically written in markdown––that describes the contents of your project. It should explain the purpose of the project, give credit to the creators, outline the basic organization of the directory, and give details on how to run the analysis. Here's the README for SARS-CoV-2_Delta_spike_DMS_REGN10933.

  2. Separate data from code: Separate the code that's responsible for the analysis from the data that you'll be analyzing. I always put data inside a directory called data/. The organization of data/'s contents is up to you.

  3. Separate results (i.e. derived data) from data: Your analysis will generate results (like plots and tables) and intermediate files (like alignments). Any files that are derived by your code should be kept separately from your input data/. I'd highly recommend storing your derived files in a directory called results/. The organization of results/ is up to you.

  4. Choose clear, explicit, and consistent names: For files and subdirectories, follow the naming advice described above.

  5. Avoid over-nesting: You can organize files and directories to a point where you have to dig through dozens of layers before you even find what you're looking for. When in doubt, flatten it out.

Summary ​

There are many reasonable ways to organize an analysis or project. However, the principles described above hopefully provide a general road map.

',20);function p(t,g,u,m,f,y){return s(),a("div",null,[e("h1",h,[r(i(t.$frontmatter.title)+" ",1),c]),d])}const w=o(l,[["render",p]]);export{_ as __pageData,w as default}; diff --git a/assets/sections_organizing-your-projects_index.md.Rc_x_RJO.lean.js b/assets/sections_organizing-your-projects_index.md.Rc_x_RJO.lean.js new file mode 100644 index 0000000..b9ff248 --- /dev/null +++ b/assets/sections_organizing-your-projects_index.md.Rc_x_RJO.lean.js @@ -0,0 +1 @@ +import{_ as o,c as a,m as e,a as r,t as i,V as n,o as s}from"./chunks/framework.Cw5Ferw_.js";const _=JSON.parse('{"title":"Section 2: Organizing your projects","description":"","frontmatter":{"layout":"doc","title":"Section 2: Organizing your projects","sidebar":true,"aside":true},"headers":[],"relativePath":"sections/organizing-your-projects/index.md","filePath":"sections/organizing-your-projects/index.md"}'),l={name:"sections/organizing-your-projects/index.md"},h={id:"frontmatter-title",tabindex:"-1"},c=e("a",{class:"header-anchor",href:"#frontmatter-title","aria-label":'Permalink to "{{ $frontmatter.title }}"'},"​",-1),d=n("",20);function p(t,g,u,m,f,y){return s(),a("div",null,[e("h1",h,[r(i(t.$frontmatter.title)+" ",1),c]),d])}const w=o(l,[["render",p]]);export{_ as __pageData,w as default}; diff --git a/assets/sections_setting-up-your-ide_index.md.gmvqJbs5.js b/assets/sections_setting-up-your-ide_index.md.gmvqJbs5.js new file mode 100644 index 0000000..2090a02 --- /dev/null +++ b/assets/sections_setting-up-your-ide_index.md.gmvqJbs5.js @@ -0,0 +1,8 @@ +import{_ as o,c as i,m as e,a,t as n,V as s,o as r}from"./chunks/framework.Cw5Ferw_.js";const b=JSON.parse('{"title":"Section 1: Setting up your IDE","description":"","frontmatter":{"layout":"doc","title":"Section 1: Setting up your IDE","sidebar":true,"aside":true},"headers":[],"relativePath":"sections/setting-up-your-ide/index.md","filePath":"sections/setting-up-your-ide/index.md"}'),c={name:"sections/setting-up-your-ide/index.md"},d={id:"frontmatter-title",tabindex:"-1"},l=e("a",{class:"header-anchor",href:"#frontmatter-title","aria-label":'Permalink to "{{ $frontmatter.title }}"'},"​",-1),h=s(`

Those familiar with coding will know the importance of using the appropriate text editor. Hopefully, you know better than to write code in Microsoft Word. You've probably encountered tools like Rstudio for writing R, or Jupyter for writing Python. These tools are a perfectly reasonable way to write code, but they're inherently limited to a handful of languages. This quickly becomes an issue in scientific programming where a single project can consist of bash, Python, R, and markdown files.

An IDE, or Integrated Development Environment, provides a solution to this problem. With an IDE, you can write markdown documentation, edit a bash script, organize your directories, quickly check a .log file, and run a Python script all in the same window. I can't stress enough how much time an IDE can save you. However, so much functionality in one application can is intimidating. Below, I'll introduce you to Microsoft's Visual Studio Code (VS Code), one of the most approachable IDEs for general-purpose programming.

Note

I'd highly recommend giving VS Code a try even if you're familiar with another text editor for coding. However, I wouldn't recommend VS Code if you're primarily using R. I'd suggest that you use Rstudio for all R coding.

Getting started with VS Code ​

There is a reason that VS Code is the most popular code editor among software developers. It's powerful, flexible, user-friendly, and free. VS Code comes with lots of handy features, including intelligent autocomplete and built-in git commands that make it a breeze to keep track of changes to your code. However, the most powerful feature of VS Code is that it's customizable.

Installation

VS Code is a desktop application like any other. To install VS Code, simply download it from the VS Code website for your specific operating system (Windows, Mac, etc...).

User Interface ​

VS Code is laid out in a manner that makes it easy to edit code, navigate your project directory, and perform actions like git commands all in the same compact view. Here's an example of what a typical VS Code window looks like.

Image

An example VS Code project from the VS Code documentation

  • Activity Bar - On the far left side, this changes the content of the Side Bar and lets you perform activities like exploring your project directory or performing source control with git.

  • Side Bar - The sidebar is an interface for performing various actions depending on the Activity. For example, installing extensions or navigating your current directory.

  • Editor - The main area to edit your files. You can open as many editors as you like side-by-side, vertically, and horizontally.

  • Panel - Space below the code editor that houses additional functions. The most relevant feature for readers of this guide is the integrated terminal. You can collapse the panel out of view for more space.

  • Status Bar - Provides project-specific information.

In addition to these main visual elements, there is also an interface at the top of the page for inputting commands called the Command Pallet.

Command Pallet

The command pallet taken from the VS Code documentation

To open the command pallet, use the keyboard shortcut ⇧⌘P. This shortcut is the most important thing to remember when using VS Code. With the command pallet, you can perform a variety of actions from formatting a file, opening a terminal window, changing your settings, and more. Effectively, anything you can do in VS Code, you can do it with the command pallet.

Although this covers the basic interface of VS Code, there are a lot more features packed into the standard view that I didn't cover. If you're interested in learning more about the user interface for VS Code and how you can customize it, check out this page in the documentation.

Installing Extensions ​

VS Code comes standard with most of the features you'll need. However, there will probably be certain features specific to the project you're working on. For example, if you're frequently using Python, you might want to format your code with black when you save a file. If you're writing documents in markdown, you'll probably want some basic form of spellchecking. Although VS Code doesn't come with these features built-in, you can add them as 'extensions'. In most cases, VS Code will recommend appropriate extensions.

Here are several VS Code extensions that are particularly useful to us:

  • Python - IntelliSense for Python with features like code-checking and debugging.
  • Rainbow CSV - Syntax highlighting for CSV files.
  • Jupyter - Edit and run Jupyter notebooks right in VS Code.
  • Ruff - For fast Python linting (code-checking).
  • Black Formatter - Automatic formatting of Python code.
  • Snakemake - IntelliSense and code highlighting for Snakemake.
  • vscode-pdf - To open PDF files right in VS Code.
  • Protein Viewer - A simple 3D protein viewer that opens in VS Code.

To add extensions, you click on the Extensions button on the Activity Panel and search for extensions to install.

Install Extensions

An example showing how to install extensions from VS Code documentation

Configuring VSCode ​

In addition to customizing VS Code with extensions, it's also worth taking time to configure your settings. In the settings, you can configure everything from the appearance of your code to the code formatting tool that should be run on specific files when you save them. There are two ways to customize your settings: locally and globally.

The global settings that are applied to all workspaces are stored in a settings.json file. You can directly access this file by opening the command pallet (⇧⌘P) and typing in Preference: Open User Settings (the command pallet will autocomplete). However, the easiest way to edit the settings is to select Code > Settings > Settings from the application menu. This will provide a GUI where you change your settings alongside descriptions of what each setting does.

In addition to global settings, you can also configure settings on a project-by-project basis using workplace settings. These settings will only apply to the specific project (directory) that you've configured them in. These settings are stored in a project within a .vscode directory. You set these the same way you set global settings, but you select Workspace rather than User in the settings menu.

Settings Example

Here is an example of the settings menu from the VS Code documentation.

Command Line Interface ​

In addition to interacting with VS Code through the application itself, you can also open up projects and files in VS Code directly from the command line. Here are some of the most helpful commands when using VS Code.

bash
# open VS Code with current directory in the Explorer
+code .
+
+# create a new window
+code -n
+
+# see help options
+code --help

Summary ​

VS Code is a must for projects where you're editing many files of different formats. However, VS Code is not a singular solution to all code editing needs. Although you can run code interactively in a Jupyter Notebook from within VS Code, it can often be faster to open up Jupyter from the command line for quick interactive scripting. Additionally, programming languages like R have specific IDEs like Rstudio that make them the default choice for projects built around R code.

Helpful Resources ​

Check out this page on the VS Code documentation for a thorough walkthrough of the basics.

`,36);function p(t,u,m,g,f,y){return r(),i("div",null,[e("h1",d,[a(n(t.$frontmatter.title)+" ",1),l]),h])}const w=o(c,[["render",p]]);export{b as __pageData,w as default}; diff --git a/assets/sections_setting-up-your-ide_index.md.gmvqJbs5.lean.js b/assets/sections_setting-up-your-ide_index.md.gmvqJbs5.lean.js new file mode 100644 index 0000000..2701e15 --- /dev/null +++ b/assets/sections_setting-up-your-ide_index.md.gmvqJbs5.lean.js @@ -0,0 +1 @@ +import{_ as o,c as i,m as e,a,t as n,V as s,o as r}from"./chunks/framework.Cw5Ferw_.js";const b=JSON.parse('{"title":"Section 1: Setting up your IDE","description":"","frontmatter":{"layout":"doc","title":"Section 1: Setting up your IDE","sidebar":true,"aside":true},"headers":[],"relativePath":"sections/setting-up-your-ide/index.md","filePath":"sections/setting-up-your-ide/index.md"}'),c={name:"sections/setting-up-your-ide/index.md"},d={id:"frontmatter-title",tabindex:"-1"},l=e("a",{class:"header-anchor",href:"#frontmatter-title","aria-label":'Permalink to "{{ $frontmatter.title }}"'},"​",-1),h=s("",36);function p(t,u,m,g,f,y){return r(),i("div",null,[e("h1",d,[a(n(t.$frontmatter.title)+" ",1),l]),h])}const w=o(c,[["render",p]]);export{b as __pageData,w as default}; diff --git a/assets/sections_tracking-your-code_index.md.CAO3av_f.js b/assets/sections_tracking-your-code_index.md.CAO3av_f.js new file mode 100644 index 0000000..31c51a1 --- /dev/null +++ b/assets/sections_tracking-your-code_index.md.CAO3av_f.js @@ -0,0 +1,4 @@ +import{_ as t,c as i,m as e,a,t as o,V as n,o as r}from"./chunks/framework.Cw5Ferw_.js";const v=JSON.parse('{"title":"Section 5: Tracking code with Git","description":"","frontmatter":{"layout":"doc","title":"Section 5: Tracking code with Git","sidebar":true,"aside":true},"headers":[],"relativePath":"sections/tracking-your-code/index.md","filePath":"sections/tracking-your-code/index.md"}'),h={name:"sections/tracking-your-code/index.md"},l={id:"frontmatter-title",tabindex:"-1"},p=e("a",{class:"header-anchor",href:"#frontmatter-title","aria-label":'Permalink to "{{ $frontmatter.title }}"'},"​",-1),c=n(`

I use version control for nearly all of the programming I do. It's a crucial skill for learning to how program that can fall by the wayside in favor of learning a specific programming language. Leaning to program and learning version control should happen side-by-side. But what is "version control?" Version control is more than just saving a file. Here's a description from the book Pro Git:

Version control is a system that records changes to a file or set of files over time so that you can recall specific versions later.

By preserving a history of incremental changes over time, you can go back and see what you've done, find out when and why something stopped working, make multiple changes to your project in parallel, and more. In this section, I'll introduce Git, a common tool for version control developed by Linus Torvalds.

TIP

This free book is the definitive resource for learning Git. I highly recommend skimming chapters 1-3.

How does git work? ​

At the core, Git records changes to your files over time. Unlike other systems that store differences between file versions, Git takes snapshots of your entire project each time you make a commit. These snapshots allow you to revisit any point in your project's history.

Here's a high-level overview of how git tracks your files:

  1. Repository Initialization: When you initialize a Git repository in your project directory using git init, Git starts monitoring the files within that directory.

  2. Working Directory and Staging Area: As you modify files, the changes are reflected in your 'working' directory. To prepare changes for a commit, you add them to the staging area using git add. This step lets you decide which changes to include in your next snapshot.

  3. Committing Changes: After staging, you create a commit with git commit. This action records a snapshot of the staged changes and adds it to your project's history. Each commit is uniquely identified, allowing you to track and revert to specific versions.

  4. Local and Remote Repositories: Git stores your project's history locally, but, optionally, you can also link your repository to a remote server like GitHub or Bitbucket which enables collaboration, backup, and access from different machines. I'll talk more about this in the next section.

Working tree

Working tree, staging area, and Git directory taken from Pro Git

Basic git ​

To get started with Git, you'll need to familiarize yourself with a couple key commands:

Initialize a Repository:

You create a new Git repository by telling Git to start tracking the code in a directory.

bash
mkdir my-project
+cd my-project
+git init

Or, you can clone an existing Git repository from a remote server to your local machine.

bash
git clone <repository URL>
+cd my_project

TIP

A quick note about cloning a remote repository. There are several methods for doing this, some of which require a little set up, and those will be covered in the next section.

Check Repository Status:

After you've made some changes in your directory, you can check what these are using the following command.

bash
git status

This command displays the status of your working directory and staging area. It shows what file have been edited, what new files aren't being tracked, and more.

Add Changes to Staging Area:

bash
git add [file(s)]

Stages specific files for the next commit. Use git add . to stage all changes.

Commit Changes:

bash
git commit -m "Your commit message"

Records a snapshot of the staged changes. The commit message should briefly describe what you've done. It's important to write a good commit message.

View Commit History:

You can see a log of all commits that have been made to a project.

bash
git log

Compare Changes:

This can be useful, but you're unlikely to do it much.

bash
git diff

It compares changes between commits, branches, or your working directory and the last commit.

Push Changes to Remote Repository:

bash
git push

Uploads your local commits to a remote repository.

Pull Updates from Remote Repository:

bash
git pull

Fetches and merges updates from a remote repository into your local repository.

These commands form the backbone of Git operations. Regular use of git status and git log will help you stay informed about your project's state and history.

Branching and Merging ​

Branches are a powerful feature in Git that allow you to diverge from the main codebase and work independently on a set of changes. The primary branch is usually called main or sometimes master, and it's where the stable version of your project resides.

Why Use Branches?

Branching your code with Git allows for parallel development of multiple features or fixes simultaneously without interference. While you're editing the code on a branch, the main branch remains stable, and new code is only merged after it's ready. Here are some things you might use branches for:

  1. Feature Development: When adding new features, you can create a branch to isolate your work. This way, the main codebase remains unaffected until the feature is complete and tested.

  2. Bug Fixes: For fixing bugs, especially in a production environment, branches let you address issues without disrupting ongoing development work.

  3. Experimentation: If you're trying out new ideas or approaches, branches provide a safe space to experiment without the risk of breaking existing code.

See all active branches:

If you want to see what branches you have active locally, run:

bash
git branch

This will print out the branches you have in your local repo and tell you which branch you're currently on.

Switch to a Branch:

If you want to switch (or checkout) a different branch, run:

bash
git checkout [branch-name]

This switches your working directory to the specified branch.

Create and Switch to a New Branch:

To make a new branch, run:

bash
git checkout -b [branch-name]

This is a shortcut that creates a new branch and switches to it immediately.

Merge a Branch:

After you've completed work on a branch and ensured it's stable, you'll want to merge it back into the main branch. This process integrates your changes and updates the main codebase. First, checkout the branch you want to merge into:

bash
git checkout [target-branch]

Once you're in the target branch (for example, main), merge the branch with your changes by running:

bash
git merge [source-branch]

This merges the changes in the [source-branch] into [target-branch].

Note

While this approach works well for merging branches locally, in collaborative projects, you'll typically merge branches using pull requests. I'll cover this in more detail in the next section.

`,66);function d(s,g,u,k,m,y){return r(),i("div",null,[e("h1",l,[a(o(s.$frontmatter.title)+" ",1),p]),c])}const f=t(h,[["render",d]]);export{v as __pageData,f as default}; diff --git a/assets/sections_tracking-your-code_index.md.CAO3av_f.lean.js b/assets/sections_tracking-your-code_index.md.CAO3av_f.lean.js new file mode 100644 index 0000000..2b67072 --- /dev/null +++ b/assets/sections_tracking-your-code_index.md.CAO3av_f.lean.js @@ -0,0 +1 @@ +import{_ as t,c as i,m as e,a,t as o,V as n,o as r}from"./chunks/framework.Cw5Ferw_.js";const v=JSON.parse('{"title":"Section 5: Tracking code with Git","description":"","frontmatter":{"layout":"doc","title":"Section 5: Tracking code with Git","sidebar":true,"aside":true},"headers":[],"relativePath":"sections/tracking-your-code/index.md","filePath":"sections/tracking-your-code/index.md"}'),h={name:"sections/tracking-your-code/index.md"},l={id:"frontmatter-title",tabindex:"-1"},p=e("a",{class:"header-anchor",href:"#frontmatter-title","aria-label":'Permalink to "{{ $frontmatter.title }}"'},"​",-1),c=n("",66);function d(s,g,u,k,m,y){return r(),i("div",null,[e("h1",l,[a(o(s.$frontmatter.title)+" ",1),p]),c])}const f=t(h,[["render",d]]);export{v as __pageData,f as default}; diff --git a/assets/sections_using-remote-resources_index.md.C5lMMmr2.js b/assets/sections_using-remote-resources_index.md.C5lMMmr2.js new file mode 100644 index 0000000..c9155a9 --- /dev/null +++ b/assets/sections_using-remote-resources_index.md.C5lMMmr2.js @@ -0,0 +1,20 @@ +import{_ as a,c as s,m as e,a as t,t as i,V as n,o as c}from"./chunks/framework.Cw5Ferw_.js";const f=JSON.parse('{"title":"Section 3: Using remote computing resources","description":"","frontmatter":{"layout":"doc","title":"Section 3: Using remote computing resources","sidebar":true,"aside":true},"headers":[],"relativePath":"sections/using-remote-resources/index.md","filePath":"sections/using-remote-resources/index.md"}'),l={name:"sections/using-remote-resources/index.md"},r={id:"frontmatter-title",tabindex:"-1"},h=e("a",{class:"header-anchor",href:"#frontmatter-title","aria-label":'Permalink to "{{ $frontmatter.title }}"'},"​",-1),d=n(`

In this section, I'll describe how and why we use remote computational resources. I'll walk you though how to access remote file systems and how to run code on a cluster.

Computing at Fred Hutch Cancer Center ​

TIP

This wiki is an invaluable resource for people doing scientific computing at Fred Hutch Cancer Center. It goes in depth on every topic that's covered in this section.

In the Bloom lab, we use the Fred Hutch Cancer Center's file system to store our notebooks, data, and code. You can access the file system through a variety of methods. The most common way that people access our lab's files is by mounting the file system to their computer. This allows you to open and edit these files on your local machine with the advantage that you can navigate through files using a GUI (Graphical User Interface) like Finder on Mac or File Explorer on Windows.

The shared folders in the file system are all located at /fh/. There are several shared folders including /fh/fast, /fh/scratch and /fh/secure, but our lab's files are located under /fh/fast in /fh/fast/bloom_j/. Computational projects are stored under /fh/fast/bloom_j/ in a directory called computational_notebooks/ and organized by the lab member's Fred Hutch username and the year that a project was started.

text
/fh/fast/bloom_j/computational_notebooks/
+|
+|
+––––– <username>/
+    |
+    |
+    ––––– <year>/
+        |
+        |
+        ––––– <project>/

For example, if I have a project called HMPV-Antibody-Passaging that I created in 2024, it's location on the file system should be /fh/fast/bloom_j/computational_notebooks/whannon/2024/HMPV-Antibody-Passaging. Aside from the organizational benefits of adhering to this convention, all of the code in /fh/fast/bloom_j/computational_notebooks/ is automatically backed up on a daily basis.

Access through the command line ​

In addition to mounting the file system to your local computer as a network drive, you can connect to the file system by accessing a remote computer cluster called Rhino. Rhino is a cluster of 3 large memory, shared computers that are intended for interactively prototyping and writing code. You can use Rhino to execute code to analyze your data, however, Rhino is not intended for intensive computational tasks (like aligning sequencing reads). Fred Hutch has a separate computer cluster called Gizmo for intensive tasks.

Logging into Rhino ​

You log into Rhino through the command line or shell. If you're not familiar with the command line, check out this excellent tutorial.

Log into Rhino by opening up the command line and typing the ssh command.

bash
ssh username@rhino

You'll be prompted to enter your Fred Hutch password. Note, you can only access Rhino like this while using the Fred Hutch wifi. You can log into Rhino without being on the wifi, but that requires an alternative method described in a later section. Now, you'll see that your terminal's prompt has changed to something like username@rhino02:~$ , and commands that you run from the command line will be executed by Rhino.

When you initially connect to Rhino, you're location on the file system is a special 'home' directory that's specific to each user and located at /home/username. We'll use this home directory later, but for now, most of our code and data should be located in the Bloom lab's directory (/fh/fast/bloom_j/). We could move there by navigating to our computational notebook like so.

bash
cd /fh/fast/bloom_j/computational_notebooks/username/

But, it becomes annoying if you have to remember the full path every time you log onto Rhino. Instead, we can make our lives a little easier with 'symbolic links'. These are a way of making shortcuts between places on the file system. Instead of typing the full path each time, we can type a shortened version and it will jump us to the correct location.

Run the following command to make a symbolic link to your computational notebook.

bash
ln -s fh/fast/bloom_j/computational_notebooks/username/ username/

Now, you can access your folder in computational_notebooks from either it's original path (cd fh/fast/bloom_j/computational_notebooks/username) or a new path from your home directory (cd ~/username). Both paths will take you to the same place.

Setting up SSH keys ​

The approach outlined above is a perfectly good way to log into Rhino, but entering your password each time gets annoying. Instead, I'd recommend setting up SSH to be password-free using SSH keys. This will allow you to log in securely without needing to enter your password every time.

SSH keys are a pair of cryptographic keys (one public, one private) used to authenticate your identity when connecting to a remote server like Rhino. The private key stays on your local machine and is kept secret, while the public key is placed with the server. When you log in, the server checks that the private key on your machine matches the public key it has, allowing you to authenticate without needing to enter a password.

WARNING

The process for generating SSH keys differs between operating systems. The following steps apply to a local computer with Mac OS and a remote computer with Ubuntu Linux (Rhino). There are tons of tutorials online for setting up SSH keys if your computer has a different operating system.

Step 1: Generate an SSH Key Pair ​

First, you'll need to generate a pair of SSH keys on your local machine. To do this, open a terminal (make sure you're not logged into Rhino) and run the following command:

bash
ssh-keygen -t rsa -b 4096 -C "rhino.fhcrc.org"
  • -t rsa specifies that you're generating an RSA key.
  • -b 4096 generates a 4096-bit key for added security.
  • -C "rhino.fhcrc.org" adds an optional label (in this case, the name of the remote host) to help identify your key.

When you're be prompted for a file location, press Enter to select the default (~/.ssh/id_rsa). When you're prompted to enter an optional passphrase, also press Enter to skip this step.

Step 2: Copy the public key to Rhino ​

Now you'll have two keys located at ~/.ssh/id_rsa, one public and one private. Copy the public key to Rhino using the following command:

bash
ssh-copy-id username@rhino

Your public SSH key will now be on Rhino in ~/.ssh/authorized_keys where ~ is your home directory on the server (/home/username).

Step 3: Log in to make sure it worked ​

You should be able to log into Rhino without a password now. Test it out:

bash
ssh username@rhino

If everything worked, you won't be prompted for a password.

Step 4: Edit your SSH config ​

This step is optional, but it will make life easier down the road. Make a new file called 'config' (no extension) in ~/ssh/ on your local machine and add the following line:

text
Host rhino
+  HostName rhino
+  User username
+  ForwardX11 yes
+  ForwardAgent yes
+  ForwardX11Trusted yes
+  IdentityFile ~/.ssh/id_rsa

Setting up a proxy jump ​

So far, the approaches detailed above will only work when you're on the Fred Hutch wifi. But what about if you're working from home and you want to access the sever? The simplest option is to log into the Fred Hutch VPN from a Fred Hutch managed computer and log into Rhino like you normally would. However, if you don't have a Fred Hutch managed computer, this won't work. In that case, the other option is to use a 'proxy jump'.

A proxy jump involves logging into Rhino through an intermediate host called Snail. First you log into Snail, then from Snail you log into Rhino. Thankfully, we can automate this process using SSH keys and the SSH config. Here's a detailed tutorial on setting up a proxy jump to Rhino with Snail.

Computationally intensive computing with Gizmo ​

The Rhino cluster is intended for accessing and manipulating files, running and developing programs, managing software environments, and interactive coding. It's not intended running for computationally intensive jobs (such as aligning sequencing reads) that take a long time or use many CPUs. For those kinds of jobs, we use the Gizmo cluster. Like Rhino, Gizmo is a computer cluster managed by Fred Hutch; unlike Rhino, it's very, very large.

There are two methods of running programs on Gizmo: (1) submitting them as jobs with Slurm or (2) running them interactively with grabnode. I'll discuss both below.

Note

If you've never used Gizmo before, you'll need to ask Fred Hutch Scicomp for permission. Shoot them a quick email and you'll usually hear back within an hour.

Using Gizmo interactively ​

This is the easiest way to get access to Gizmo. Log into Rhino and type:

bash
grabnode

You'll be prompted with several questions including how many CPU you want, how much memory you need, how much time you need, and whether you need a GPU. It can take a few minutes for the system to allocate you resources. When it's done, you'll see that you're prompt has changed from something like username@rhino02:~$ to username@gizmok3:~$ . Now, anything you type into the command line will be run on the Gizmo cluster.

This is a great approach for troubleshooting computationally intensive tasks interactively. However, it's not a good approach for running jobs you expect to take a long time. The resources you request from Gizmo will be released when you log out. That means that any job that's running will be terminated if you log out or lose connection to the server. There's a way to get around this constraint with tmux, but it's better to submit long-running jobs with Slurm.

Using Gizmo with Slurm ​

This is the more common use case for Gizmo. If you have a long-running computationally-intensive job, for example, a Snakemake pipeline that performs many sequencing alignments, you can submit a 'job' to Gizmo and the code will run until it's finished. You're using the resources of Gizmo just like you do above, but you don't need to leave Rhino and your jobs will keep running if you log out.

Gizmo uses a program called Slurm to handle job submission. Slurm (Simple Linux Utility for Resource Management) is an open-source job scheduling system used for managing and allocating computing resources in a cluster.

  1. Submitting a Job

To submit a job in Slurm, you use the sbatch command with a job script. The script contains the commands you want to run and Slurm directives to request resources. A simple bash script (run_job.bash) looks like this:

bash
#!/bin/bash
+#
+#SBATCH -c 16
+
+snakemake -j 16 --software-deployment-method conda -s dms-vep-pipeline-3/Snakefile

The comments starting with #SBATCH configure Slurm by providing arguments like how many CPUs you need (#SBATCH -c 16). You submit the job script to Slurm using the sbatch command as follows:

bash
sbatch run_job.bash

You can check on the status of your jobs running on Gizmo with the squeue command. This command shows you the jobs you have running and how long they've been running for.

bash
squeue -u username

If you want to cancel a job that's running, use the scancel command.

bash
scancel -u username

There's a lot more to Slurm, but that's most of what you'll need to know.

Using tmux ​

As I mentioned above, you may run into a scenario where you want to run something interactively and you don't want the process to terminate when you log off the server. That's where tmux comes in handy. tmux (Terminal Multiplexer) is software that's pre-loaded on Rhino and Gizmo. It allows you to create multiple terminal sessions within a single window, and more importantly, lets you detach from a session while keeping it running in the background. This way, you can log off and later reconnect to the session to check on your processes or pick up where you left off.

1. Start a new session ​

To start a new tmux session, use the command:

bash
tmux

This opens a new terminal window with a little green bar at the bottom. You'd use this terminal as you would normally.

2. Detach from a session ​

To detach from a session and leave it running, press Ctrl + B, followed by D. This will bring you back to your original terminal prompt, but your tmux session will continue to run in the background.

3. List active sessions ​

If you want to see a list of all the running tmux sessions, use:

bash
tmux ls

4. Reattach to a session ​

To reattach to a session you previously detached from, use:

bash
tmux attach -t <session_id>

Replace <session_id> with the number or name (if you named it) of the session you want to reconnect to, which can be found using the tmux ls command.

5. Kill a session ​

When you're done, you can kill a tmux session by typing exit within the session or by pressing Ctrl + D.

`,82);function p(o,u,m,g,y,k){return c(),s("div",null,[e("h1",r,[t(i(o.$frontmatter.title)+" ",1),h]),d])}const w=a(l,[["render",p]]);export{f as __pageData,w as default}; diff --git a/assets/sections_using-remote-resources_index.md.C5lMMmr2.lean.js b/assets/sections_using-remote-resources_index.md.C5lMMmr2.lean.js new file mode 100644 index 0000000..8c01772 --- /dev/null +++ b/assets/sections_using-remote-resources_index.md.C5lMMmr2.lean.js @@ -0,0 +1 @@ +import{_ as a,c as s,m as e,a as t,t as i,V as n,o as c}from"./chunks/framework.Cw5Ferw_.js";const f=JSON.parse('{"title":"Section 3: Using remote computing resources","description":"","frontmatter":{"layout":"doc","title":"Section 3: Using remote computing resources","sidebar":true,"aside":true},"headers":[],"relativePath":"sections/using-remote-resources/index.md","filePath":"sections/using-remote-resources/index.md"}'),l={name:"sections/using-remote-resources/index.md"},r={id:"frontmatter-title",tabindex:"-1"},h=e("a",{class:"header-anchor",href:"#frontmatter-title","aria-label":'Permalink to "{{ $frontmatter.title }}"'},"​",-1),d=n("",82);function p(o,u,m,g,y,k){return c(),s("div",null,[e("h1",r,[t(i(o.$frontmatter.title)+" ",1),h]),d])}const w=a(l,[["render",p]]);export{f as __pageData,w as default}; diff --git a/assets/sections_working-collaboratively_index.md.C5p8aCvH.js b/assets/sections_working-collaboratively_index.md.C5p8aCvH.js new file mode 100644 index 0000000..891d7b2 --- /dev/null +++ b/assets/sections_working-collaboratively_index.md.C5p8aCvH.js @@ -0,0 +1 @@ +import{_ as t,c as r,m as e,a,t as i,V as s,o as n}from"./chunks/framework.Cw5Ferw_.js";const w=JSON.parse('{"title":"Section 6: Working collaboratively","description":"","frontmatter":{"layout":"doc","title":"Section 6: Working collaboratively","sidebar":true,"aside":true},"headers":[],"relativePath":"sections/working-collaboratively/index.md","filePath":"sections/working-collaboratively/index.md"}'),l={name:"sections/working-collaboratively/index.md"},c={id:"frontmatter-title",tabindex:"-1"},u=e("a",{class:"header-anchor",href:"#frontmatter-title","aria-label":'Permalink to "{{ $frontmatter.title }}"'},"​",-1),h=s('

In the last section, I talked about tracking your code with Git. In principle, this can all happen locally in your repository, but in many cases, you want to make a remote copy of a repository and its history. That's where services like GitHub come in. GitHub is a website that acts as a remote repository for your code, allowing you to store it online, collaborate with others, and take advantage of various tools for version control and project management.

Using GitHub as a 'Remote' ​

What's a Remote? ​

A remote in Git is simply a version of your project that's hosted on the internet or another network. It's the counterpart to your local repository on your computer. By pushing your local changes to the remote repository, you ensure that your code is backed up and accessible from other locations or by other collaborators.

Why Use GitHub? ​

GitHub is our preferred website for hosting Git repositories remotely. It provides a platform for:

  • Collaborative Coding: Multiple people can work on the same project simultaneously without overwriting each other's changes.
  • Version Control: Keeps track of every change made to the codebase, allowing you to revert to previous versions if needed.
  • Documentation Hosting: With GitHub Pages, you can host project documentation or even entire websites directly from your repository.

In our lab, we use GitHub to coordinate coding efforts, especially for projects that will be part of publications. By connecting your local Git repository to GitHub, you make it easier to share your work and collaborate effectively.

Bloom Lab GitHub Organizations ​

In the Bloom lab, we use specific GitHub organizations to manage our projects:

But how do you decide when to host your local project remotely? Generally, I host a project remotely on GitHub for two reasons:

  1. If I'm working on code that's likely to be connected to a paper: Hosting your code on GitHub early makes it easy when you need links to data and code for a manuscript.
  2. If I'm working on code with someone else: If you code is on GitHub it makes it easy to share with collaborators or work on the code alongside with else.

Connecting a local repository to a remote ​

As I mentioned in the previous section, Git is the version control system you use locally on your machine, while GitHub is the remote hosting service where you can store your repositories online. To connect a local repository to a GitHub remote you:

  1. Initialize a Local Repository: You can start from scratch by creating a local Git repository with git init. However, you can also connect an existing repository to a GitHub remote repository at any time.

  2. Create a Remote Repository: On GitHub, you create a new repository to host your project in the appropriate organization or account.

  3. Link Local and Remote Repositories: When you create a new GitHub repository, you'll get instructions for connecting your local repository to the GitHub repository.

  4. Push and Pull Changes: Now, you can use git push to upload your local commits to GitHub and git pull to update your local repository with changes from GitHub.

Cloning a remote repository locally ​

You can also clone an existing repository on GitHub onto your local machine. The local clone will be automatically connected to the remote repository it was cloned from, allowing you to pull updates or push changes.

To clone a repository, you can use the following command in your terminal, replacing [repository-url] with the URL of the repository you want to clone:

bash
git clone [repository-url]

There are two main protocols for cloning repositories: HTTP and SSH.

  • HTTP: No extra configuration, but you’ll need to enter your username and a personal access token every time you push changes.
  • SSH: A more secure and convenient method, which allows you to authenticate using SSH keys (no passwords!).

TIP

Only fools use HTTP.

Setting up SSH for GitHub ​

Using SSH is preferred because it’s more secure and doesn’t require you to input credentials each time you push or pull changes. Once you’ve set up your SSH keys, GitHub will trust your machine, allowing you to interact with your repositories seamlessly.

Setting up SSH for GitHub is similar to setting up SSH for Rhino. The main difference is that you'll add your public key to your account on GitHub. You should set this up for any computer you use GitHub with.

Here are detailed and up-to-date instructions for connecting to GitHub with SSH.

Collaborative Workflows ​

How do you work on code with others productively? It requires some structure to prevent conflicts and ensure code quality. Note, the following workflow mainly applies to projects where you're actively coding with other people. Small projects or projects in their early stages don't require this level of organization.

Best Practices ​

  1. Protect the Main Branch: The main branch (sometimes called master) should represent the stable, release-ready version of your code––what you'd link to in a paper. It's important to protect it by restricting direct commits. You can do this in the repository settings on GitHub.

  2. Use Feature Branches: When you're working on a new feature or addressing an issue, create a new branch from main. This keeps your changes isolated until they're ready to be merged. Use descriptive names for your branches, like sequencing-analysis-improvement or typo-correction-in-readme.

  3. Merge Changes with Pull Requests: When a feature branch is ready to become a part of main initiate a pull request on GitHub to merge the changes on the feature branch into main. Typically, these changes will be reviewed by at least one other person.

Pull Requests ​

A pull request (PR) is a way to propose changes you've made on a branch to be merged into another branch (usually main). It allows others to review your code, discuss potential issues, and approve the changes before they become part of the main codebase.

How to Make a Pull Request ​

  1. Push Your Branch to GitHub: Use git push to upload your branch.

  2. Navigate to the Repository on GitHub: You'll see a prompt to create a pull request for your recently pushed branch.

  3. Create the Pull Request: Provide a descriptive title and include any relevant information or context in the description. You can also link your pull request to specific issues so that they automatically close when the branch is merged.

  4. Review and Merge: Team members can review the pull request, leave comments, and approve it. Once approved, it can be merged into the main branch.

Merge Conflicts ​

When multiple collaborators edit the same parts of a file, Git may not be able to automatically merge the changes. This results in a 'merge conflict', which you'll need to resolve manually. A typical workflow for resolving these conflicts is as follows:

  1. Identify the Conflict: Git will mark the conflicting areas in the file.
  2. Choose the Correct Code: Decide which changes to keep—yours, theirs, or a combination.
  3. Edit the File: Remove the conflict markers and make the necessary edits.
  4. Commit the Resolution: After resolving the conflicts, commit the changes to complete the merge.

Generally, you're unlikely to run into many merge conflicts while coding in the Bloom Lab.

Issues and Discussions ​

Another valuable feature of GitHub is the ability to open 'Issues' on repository. Issues should be used for the following things:

  1. Suggesting Features: Outline a new feature you want to add to a project.
  2. Reporting Bugs: Highlight an issue with a project's code.
  3. Asking Questions: Seek clarification on how to use run code. This is something you might have to do if you're using someone else's code.

The best practices for opening Issues is to be as descriptive as possible, use labels to categorize issues, and reference related Issues or pull requests. Another important thing to note is that you should not use Slack for code-related discussions. Unlike Slack messages, Issues on GitHub are persistent, organized, and easy to keep track of.

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var(--vp-c-purple-1);--vp-c-important-2: var(--vp-c-purple-2);--vp-c-important-3: var(--vp-c-purple-3);--vp-c-important-soft: var(--vp-c-purple-soft);--vp-c-warning-1: var(--vp-c-yellow-1);--vp-c-warning-2: var(--vp-c-yellow-2);--vp-c-warning-3: var(--vp-c-yellow-3);--vp-c-warning-soft: var(--vp-c-yellow-soft);--vp-c-danger-1: var(--vp-c-red-1);--vp-c-danger-2: var(--vp-c-red-2);--vp-c-danger-3: var(--vp-c-red-3);--vp-c-danger-soft: var(--vp-c-red-soft);--vp-c-caution-1: var(--vp-c-red-1);--vp-c-caution-2: var(--vp-c-red-2);--vp-c-caution-3: var(--vp-c-red-3);--vp-c-caution-soft: var(--vp-c-red-soft)}:root{--vp-font-family-base: "Chinese Quotes", "Inter var", "Inter", ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Helvetica, Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";--vp-font-family-mono: ui-monospace, SFMono-Regular, "SF Mono", Menlo, Monaco, Consolas, "Liberation 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var(--vp-c-default-soft);--vp-code-line-number-color: var(--vp-c-text-3);--vp-code-line-diff-add-color: var(--vp-c-success-soft);--vp-code-line-diff-add-symbol-color: var(--vp-c-success-1);--vp-code-line-diff-remove-color: var(--vp-c-danger-soft);--vp-code-line-diff-remove-symbol-color: var(--vp-c-danger-1);--vp-code-line-warning-color: var(--vp-c-warning-soft);--vp-code-line-error-color: var(--vp-c-danger-soft);--vp-code-copy-code-border-color: var(--vp-c-divider);--vp-code-copy-code-bg: var(--vp-c-bg-soft);--vp-code-copy-code-hover-border-color: var(--vp-c-divider);--vp-code-copy-code-hover-bg: var(--vp-c-bg);--vp-code-copy-code-active-text: var(--vp-c-text-2);--vp-code-copy-copied-text-content: "Copied";--vp-code-tab-divider: var(--vp-code-block-divider-color);--vp-code-tab-text-color: var(--vp-c-text-2);--vp-code-tab-bg: var(--vp-code-block-bg);--vp-code-tab-hover-text-color: var(--vp-c-text-1);--vp-code-tab-active-text-color: var(--vp-c-text-1);--vp-code-tab-active-bar-color: 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+{"readme.md":"hhc_Bc4e","sections_coding-best-practices_index.md":"Dta9vFSp","sections_chatgpt-and-llms_index.md":"r93Rcp6X","sections_introduction_index.md":"CQpxuAdz","index.md":"CklGwWbF","sections_working-collaboratively_index.md":"C5p8aCvH","sections_managing-software-environments_index.md":"BYauHb59","sections_setting-up-your-ide_index.md":"gmvqJbs5","sections_organizing-your-projects_index.md":"Rc_x_RJO","sections_creating-workflows-and-pipelines_index.md":"CKk2bu3I","sections_tracking-your-code_index.md":"CAO3av_f","sections_using-remote-resources_index.md":"C5lMMmr2"} diff --git a/index.html b/index.html new file mode 100644 index 0000000..abe389c --- /dev/null +++ b/index.html @@ -0,0 +1,25 @@ + + + + + + Bench to Byte + + + + + + + + + + + + + + +
Bench2Byte

Appearance

Bench to Byte

A biologist's guide to crafting quality code

Start Learning!
Bench to ByteBench to Byte

Contributors

Released under the MIT License.

Copyright © 2024-present Will Hannon

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Bench2Byte

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Section 0: Coding Smarter with Online Tools ​

I've spent a lot of time helping people troubleshoot programming issues, do data analysis, and get set up computationally. If you interviewed people about this experience, a common theme would emerge; I spend a lot of time asking the internet for help. Whether it's a forum like Stack Overflow or an AI chatbot like ChatGPT, you can figure out anything with the internet.

When it comes to programming, my philosophy is that it's much more important to grasp a concept than commit specifics to memory. If you understand the concept behind what you're trying to accomplish, the internet will always be able to fill in the specifics. That's why this is the first section.

While you're following this guide, you're bound to run into the occasional issue or topic that's lacking detail. The internet will help, but how do you decide which resources are appropriate for your questions? Below, I'll address the main online resources that I use.

Note

Almost no resource is free of factual inaccuracies: LLMs 'hallucinate', people writing posts on Stack Overflow might have no idea what they're talking about, and even programmers writing documentation make mistakes.

I have a specific goal and lack any background knowledge ​

If you know vaguely what you want to accomplish but nothing about how to accomplish it, I'd recommend googling a recent tutorial. For example, if you're trying to figure out how to host a website with GitHub Pages, read their current documentation or watch a video tutorial rather than going to a forum or using an LLM. This is for two reasons: (1) Forums and LLMs will not always be up-to-date, and (2) they make mistakes that you won't be able to catch without a little background know-how.

I have a specific goal and have a little background knowledge ​

In this case, LLMs are almost always the best approach. LLM stands for Large Language Model. These are machine learning models that are trained on vast amounts of text, usually scraped from the internet, to understand, generate, and predict new text. They learn patterns, enabling them to perform a variety of tasks including proof-reading, generating, and answering questions about code.

Because LLMs are trained on data scraped from the internet, they tend to be better at answering questions with lots of examples to train on. For this reason, LLMs are great at answering common coding questions and writing code in popular languages like Python and Javascript. However, if your question is about something obscure, like a tool that's only used by the Bloom Lab, you won't get a useful answer.

Also, LLMs can 'hallucinate' and respond confidently with factually inaccurate answers. For this reason, I only use LLMs when I feel that I can identify these mistakes or I have a way of validating the response.

Helpful Resources ​

There are several different LLMs that people use for coding. Personally, I like Open AI's ChatGPT. It's a good generalist model that can answer coding questions as well as performing other tasks. I'd recommend the paid model, at the time of writing this it makes a significant difference.

Another popular LLM that theoretically performs coding tasks better than ChatGPT is Anthropic's Claude. At the time of writing it's well liked by several members of the lab.

Additionally, there are coding specific models like GitHub's Copilot. Copilot is nice because it's integrated directly into the development environment that you write code in giving you suggestions as you go. I use it, but mostly as intelligent autocomplete rather than a resource for asking questions. It's nice, but not as valuable as ChatGPT or Claude.

Released under the MIT License.

Copyright © 2024-present Will Hannon

+ + + + \ No newline at end of file diff --git a/sections/coding-best-practices/index.html b/sections/coding-best-practices/index.html new file mode 100644 index 0000000..51553af --- /dev/null +++ b/sections/coding-best-practices/index.html @@ -0,0 +1,78 @@ + + + + + + Section 8: Coding best practices | Bench to Byte + + + + + + + + + + + + + + +
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Section 8: Coding best practices ​

Hopefully, you're already aware of the importance of properly organizing your projects, tracking your code, and organizing complex analyses into automated workflows. In this final section, I'll highlight some important language-agnostic tips for improving your coding.

Following conventions ​

Each programming language has established conventions that cover aspects like code formatting, indentation, naming styles, and file organization. Adhering to these is crucial for writing clean, readable, and maintainable code.

There are often style guides that outline common conventions for programming languages. For examples, Python has the PEP 8 style guide, which recommends using 4 spaces for indentation, limiting lines to 79 characters, and using lowercase with underscores for function and variable names.

Remembering all of these recommendation while you're coding would be a huge pain. Thankfully, code formatters like black can automatically format code to adhere to a specific style.

Naming variables ​

Choosing the right names for your variables is like labeling boxes in the -80C: it helps you and others understand what's inside without digging through them. Here are some universal best practices for naming variables:

  • Don't Use Reserved Words: Most programming languages have 'reserved words' with special meanings (e.g. for, true, and if). Familiarize yourself these words so you don't accidentally name something with one. Typically, a good syntax highlighter or linter will catch these mistakes.
  • Use Descriptive Names: Use clear and descriptive names that convey the purpose of the variable. For example, if you have a data frame of gene expression data use gene_expression_df instead of just using df.
  • Avoid Abbreviations: Unless they are standard abbreviations that people reading your code are likely to understand.
  • Be Consistent: Stick to a naming convention and use it consistently. Each programming language has preferred naming conventions for variables, classes, and functions. For example, variables in Python are usually written in snake_case whereas variables in Javascript are written in camelCase.

Commenting code ​

Comments are annotations in your code that explain what's happening, but they should be used judiciously. It's tempting for a beginning programmer to comment every line of code. This practice can actually make the code harder to read. Generally, you want to say why code is the way it is, not what the code is doing. If you're following the advice mentioned in this section, your code should be pretty clear even in the absence of comments.

Here's an example of well commented code from a Python packaged developed by the Bloom lab called Polyclonal:

python
def corr_heatmap(
+    corr_df,
+    corr_col,
+    sample_cols,
+    *,
+    group_col=None,
+    corr_range=(0, 1),
+    columns=3,
+    diverging_colors=None,
+    scheme=None,
+):
+    """Plot a correlation matrix as heat map from a tidy data frame of correlations.
+
+    Parameters
+    ----------
+    corr_df : pandas.DataFrame
+        Data to plot.
+    corr_col : str
+        Column in `corr_df` with correlation coefficient.
+    sample_cols : str or list
+        Column(s) in corresponding to sample identifiers, suffixed by "_1" and "_2" for
+        the distinct samples. Should be entries for all pairs of samples.
+    group_col : str or None
+        Column in `corr_df` to facet plots on, or `None` if no facets.
+    corr_range : tuple or None
+        Range of heat map as `(min, max)`, or `None` to use data range. Typically
+        you will want to set to `(0, 1)` for :math:`r^2` and `(-1, 1)` for :math:`r`.
+    columns : int
+        Facet by `group_col` into this many columns.
+    diverging_colors : None or bool
+        If `True`, mid point of color scale is set to zero. If `None`, select `True`
+        if `corr_range` extends below 0.
+    scheme : None or str
+        Color scheme to use, see https://vega.github.io/vega/docs/schemes/.
+        If `None`, choose intelligently based on `corr_range` and `diverging_colors`.
+
+    Returns
+    -------
+    altair.Chart
+        Heatmap(s) of correlation coefficients.
+
+    """
+    corr_df = corr_df.copy()  # so we don't change input data frame
+
+    if corr_col not in corr_df:
+        raise ValueError(f"{corr_col=} not in {corr_df.columns=}")
+
+    if corr_range is None:
+        corr_range = (corr_df[corr_col].min(), corr_df[corr_col].max())
+    else:
+        if corr_range[0] > corr_df[corr_col].min():
+            raise ValueError(f"{corr_range[0]} > {corr_df[corr_col].min()=}")
+        if corr_range[1] < corr_df[corr_col].max():
+            raise ValueError(f"{corr_range[1]} < {corr_df[corr_col].max()=}")

Notice two things: (1) the function has a 'Docstring' that describes the function's purpose, inputs, and outputs. (2) The code is commented judiciously, and uses comments to explain aspects of the code that aren't self-explanatory.

Modularizing code ​

Breaking your code into smaller, reusable pieces makes it easier to manage and understand. Those pieces can be functions, classes, or modules depending on the conventions of the programming language. Generally, you don't want to have repetitive code. If you're copying and pasting code from one part of your script into another, that's a sign that you should be modularizing you code with functions or classes.

Logging, Errors, and Warnings ​

It's important to use logs, warnings, and errors to monitor what's going on with your code. You should have expectations about how your code is supposed to work. These features help ensure that those expectations are met. They prevent silent bugs and help make your code user-friendly.

Notebooks vs Scripts ​

Programming languages that are commonly used for data analysis, like Python and R, have interactive environments called 'Notebooks' that combine code execution, text, and visualizations in a single document. Notebooks are awesome tools for data analysis, but sometimes scripts are better suited to the task. How do you decide whether to use a Notebook or a script?

When to use a Notebook (i.e. Jupyter Notebooks):

Notebooks are awesome because they combine code, visualizations, and documentation in a single location. You can also run code interactively. I typically use Notebooks for:

  • Prototyping code: It's hard to beat the convenience of running code interactively.
  • Documenting an analysis: If I'm working on an analysis that I want to document for other people it's nice to have code, writing, and plots in a single location.

When to use a script:

Scripts a great way of modularizing code that you're going to run often. I typically use scripts over Notebooks in the following situations:

  • Frequently used code: Scripts are smaller, faster, and simpler to run than Notebooks. I use scripts to modularize code that functions as a tool I plan to run frequently.

Released under the MIT License.

Copyright © 2024-present Will Hannon

+ + + + \ No newline at end of file diff --git a/sections/creating-workflows-and-pipelines/index.html b/sections/creating-workflows-and-pipelines/index.html new file mode 100644 index 0000000..5cb1c5d --- /dev/null +++ b/sections/creating-workflows-and-pipelines/index.html @@ -0,0 +1,25 @@ + + + + + + Section 7: Creating reproducible workflows and pipelines | Bench to Byte + + + + + + + + + + + + + + +
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Section 7: Creating reproducible workflows and pipelines ​

In biology, we often string together bits of code, scripts, and tools to complete a single analysis. Each step runs sequentially to produce some desired output. Instead of remembering the order and manually running each step, you should take advantage of programming tools to codify these steps in a pipeline. In this section, I'll describe what a pipeline is, when you should write one, and what tools are available to make it easier.

What's a pipeline? ​

Pipelines group together different computational steps one after the other. People often accomplish this with bash scripts or by manually running commands in sequence. But there's a better way. You can use a workflow language. These languages are designed to handle complex workflows, manage dependencies, and automate the execution of tasks. They're smart because they can:

  • Automatically detect dependencies: Determine which steps depend on others.
  • Optimize execution: Run independent tasks in parallel.
  • Resume from failures: Restart workflows from the point of failure without redoing completed tasks.
  • Ensure reproducibility: Keep a record of all steps for consistent results. Imagine being able to run a large complex analysis with a single command.

When should you write an analysis as a pipeline? ​

If your analysis is more complex than a single script or command, a pipeline can significantly improve efficiency and reliability. This is especially true when certain steps depend on the outputs of previous ones. You should use pipelines to manage these relationships efficiently.

What tools are available for writing pipelines? ​

There are several tools designed to help you write pipelines including Snakemake, Make, Nextflow, and CWL. Each tool has its strengths, but Snakemake and Nextflow are the most commonly used tools used in Bioinformatics.

Snakemake ​

We primarily use Snakemake in the Bloom lab. Snakemake is a workflow management system that uses a a Python-based language to define rules, inputs, outputs, and commands. In Snakemake, you define a series of rules in a file called a Snakefile. Each rule specifies:

  • Targets (Outputs): The files or results you want to produce.
  • Dependencies (Inputs): The files required to produce the targets.
  • Actions (Shell Commands or Scripts): The commands to execute.

Snakemake automatically builds a workflow based on these rules, figuring out the order of execution by analyzing the dependencies. The best way to learn Snakemake is by following the tutorial in it's documentation.

Released under the MIT License.

Copyright © 2024-present Will Hannon

+ + + + \ No newline at end of file diff --git a/sections/introduction/index.html b/sections/introduction/index.html new file mode 100644 index 0000000..a536543 --- /dev/null +++ b/sections/introduction/index.html @@ -0,0 +1,25 @@ + + + + + + Introduction | Bench to Byte + + + + + + + + + + + + + + +
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Introduction ​

I made this guide for biologists with moderate programming experience who want to improve the way they work. This guide is not intended to teach you a particular coding language (here's a quick aside about that). Instead, you'll learn how to organize your projects, use remote resources, create reproducible software environments, and build computational pipelines. My goal is that you not only learn the how but also the why behind each practice.

A Quick Note

This guide was made with members of the Bloom lab in mind. In the future, I might expand this resource to apply to a more general audience.

Outline ​

Although each section is useful in isolation, you'll get the most out of this guide if you follow it in order.

  1. Coding Smarter with Online Tools

    Taking advantage of online resources

  2. Setting up your IDE

    Using VSCode to streamline your coding workflow

  3. Organizing your projects

    Creating clearly named, well organized projects

  4. Using remote resources

    Increasing your computational capabilities with remote resources

  5. Managing software environments

    Using conda to isolate software environments

  6. Tracking your code

    Versioning your code with git

  7. Working collaboratively

    Collaborating with GitHub

  8. Reproducible workflows and pipelines

    Building reproducible pipelines with Snakemake

  9. Coding best practices

    Writing clear code

Learning to Program ​

Some basic programming knowledge is necessary to get the most out of this guide. Below, I'll give a quick overview of the programming languages that are widely used by biologists so you can pick the language that best suites your needs. ß Generally, programming languages are either compiled––you write a script and an intermediate software called a compiler transforms it into an executable program––or interpreted––you write a script that can be executed at any point without a compilation step. Compiled languages are fast and easy to run on different operating systems; however, they tend to be more difficult to learn, run, and debug. Interpreted languages are usually not as fast as compiled languages, but they're much easier to learn. Both types of language have a place in biological programming. Compiled languages are great for writing programs that run quickly on huge amounts of data (like an aligner). Interpreted languages are great for data analysis and plotting. Most biologists will want to learn an interpreted language.

Common compiled programming languages used by biologists are C/C++, Java, and Rust. They're used to write short-read aligners like BWA (C/C++), genome browsers like IGV (Java), and tools for single-cell genomics like cellranger (Rust). You're likely to interact with tools written in a compiled language, but you probably won't need to write your own.

Common interpreted languages are Python, R, Javascript, and Perl. Python and R are the most widely used by biologists. Perl was common among biologists, but has fallen out favor. Javascript is the principle programming language of the web and is occasionally used by biologists to build websites and interactive dashboards. If you're a biologist who want's to learn programming, stick to with Python or R.

When choosing whether to learn Python or R, consider the following factors:

1. What are my research needs?

Python and R share most of the same core features, but they have different strengths. Python is a better general programming language with many useful libraries for biological problems. R is geared towards statistics and data analysis, so programming for these scenarios is more natural. Additionally, R has more common packages for analyzing gene expression and single-cell data. Learn the language with the best set of tools for the type of research you're doing.

2. What are people around me using?

If possible, learn the language that people around you (in your lab or institute) are using. It makes it easier to collaborate and they'll be a valuable resource while you're learning.

TIP

Learn Python if you're a member of the Bloom lab.

Suggestions ​

If you have any suggestions for topics that aren't covered in this guide, please suggest them in the corresponding Discussions thread.

Released under the MIT License.

Copyright © 2024-present Will Hannon

+ + + + \ No newline at end of file diff --git a/sections/managing-software-environments/index.html b/sections/managing-software-environments/index.html new file mode 100644 index 0000000..f0d058a --- /dev/null +++ b/sections/managing-software-environments/index.html @@ -0,0 +1,36 @@ + + + + + + Section 4: Managing software environments | Bench to Byte + + + + + + + + + + + + + + +
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Section 4: Managing software environments ​

Biological programming typically involves a combination of writing your own code and using tools developed by others. This can lead to projects with many dependencies, each of which has specific version requirements. It's crucial for reproducibility that these software environments are tracked and managed. Otherwise, you'd be the only one who can run your analysis pipeline. Managing this by hand would be difficult. Thankfully, there are tools like Conda for managing software environments.

What is Conda? ​

Conda is an open-source tool for managing both software packages. It allows you to install software and their dependencies in isolated virtual environments. This means you can easily switch between projects, each with its own set of package versions, without worrying about conflicts between dependencies.

With Conda, you can ​

  • Install the software you need (e.g., bioinformatics tools, Python packages) with all required dependencies
  • Easily switch between environments
  • Share environment "recipes" for reproducibility

We make extensive use of Conda in the Bloom Lab. However, there are alternative solutions for managing software environments that you might run into, the most common of which is Docker. I won't describe Docker in this guide, but it's worth reading about.

Conda vs. Mamba ​

Mamba is a re-implementation of Conda in C++ that offers the same functionality but is often faster, especially when resolving complex dependencies or handling large environments. I tend to use Mamba for building environments and Conda for quick operations like activating and deactivating an environment. However, this is just a habit, and Mamba's version of Conda's commands are basically identical.

Which should you use? Conda works fine for small, simple environments. Mamba can save you time for larger or more complex environments. If you're setting up Conda from scratch using the instructions below, use Mamba. If you've already been using Conda with an installation of miniconda, and you would need to install Mamba, stick with Conda.

Setting up Conda on Rhino ​

Conda isn't pre-installed on Rhino, so you'll have to set it up yourself. The easiest way to do this is by installing a lightweight version of Conda called miniconda or its open-source alternative, miniforge, which comes pre-configured with Mamba. If you're starting from scratch, I'd highly recommend installing miniforge instead of miniconda. If you've already installed miniconda, the process of uninstalling and re-installing a Conda distribution isn't worth the marginal advantages offered by miniforge.

Installing miniforge ​

TIP

A more comprehensive version of the following instructions is available here.

We're going to install Mamba and Conda on Rhino by downloading miniforge into our home directory (the default directory when you log into Rhino, i.e. /home/username).

Run the following command in home (/home/username) to fetch and run the latest miniforge build script from the web.

bash
curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
+bash Miniforge3-$(uname)-$(uname -m).sh

You should follow the prompts during the installation and accept all the default options. Say yes to accepting the license, use the default recommended installation location (~/miniforge3), and say yes to the final question about whether to initialize the environment.

The build script will edit your .bashrc (a script that runs every time you log into Rhino) file with code that activates Mamba. This happens automatically, but to trigger the activation of Mamba this first time without logging out, run the following command.

bash
source .bashrc

Now, instead of the usual prompt (username@rhino02:~$ ), you'll see a new prompt that looks like: (base) username@rhino02:~$ . The name inside of the parenthesis indicates the Conda environment that's activated. The default environment when you log into Rhino is always base.

Conda Basics ​

Conda has lots of commands and arguments, but you only need to know a few basics to get a lot of utility out of it. Note that conda and mamba are synonymous in the following sections.

Making a named environment ​

To create a new environment with a specific name, you can use the create command. By default, this will create an isolated environment where you can install any packages you need. This is a great option for making an environment for a specific task.

bash
conda create --name myenv

You can also specify the version of Python or any other package you want in the environment. For example, to create an environment called myenv with Python 3.9 installed:

bash
conda create --name myenv python=3.9

After creating the environment, you can activate it using:

bash
conda activate myenv

This switches your terminal into the isolated environment, allowing you to install packages specific to that project. You'll see the prompt change from something like (base) username@rhino02:~$ to (myenv) username@rhino02:~$ to reflect that myenv is active. To deactivate and return to your base environment:

bash
conda deactivate

Making an environment from a file ​

If someone has already created an environment and shared it with you, or if you want to share yours with others, you can easily set up an environment from an environment file (usually environment.yml). This is very common practice in the Bloom lab since we're often running analysis pipelines with similar dependencies.

To create an environment from a file, use:

bash
conda env create --file environment.yml

This will install all packages listed in the file and configure the environment exactly as defined.

The environment.yml file will look something like this:

yaml
name: myenv
+
+channels:
+  - conda-forge
+  - bioconda
+  - defaults
+
+dependencies:
+  - altair=5.3
+  - biopython=1.83
+  - mafft=7.525

It specifies the name of the environment, like places (channels) to search for each software, and the software (dependencies) and their versions. You activate the environment the same way described above.

Common issues ​

While Conda is a helpful tool, you're bound to run into issues occasionally. Below are some common issues you might encounter and how to troubleshoot them.

My environment is taking a very long time to solve ​

This is a common issue when Conda struggles to resolve dependencies. Here are a few ways to address this:

  • Use Mamba: Mamba is faster at resolving dependencies, especially in complex environments. Simply replace conda with mamba in your commands:
bash
mamba create --name myenv python=3.9

If you're using conda from an installation of miniconda, you can use mamba by installing it like any other dependency into your base environment.

  • Specify fewer package versions: Conda and Mamba struggle when too many version constraints are placed on packages. If possible, relax some of these constraints and let Conda handle version selection. In most cases, a dependency doesn't need to pinned at a specific minor version.

  • Split up a big environment into smaller environments: The main strength of conda is that you can create isolated environments. Use that to your advantage and split up big environments into small ones and activate them as needed.

I'm getting permissions issues ​

This is a specific issue that comes up in the Bloom lab. If you've configured Conda so that there are multiple versions available at the same time––a local version in /home/username and a Bloom lab version in /fh/fast/bloom_j/software––you will eventually run into this problem.

  • Edit your .bashrc so that you're not accessing both versions of Conda: In other words, only use your local version of Conda.

  • Check your $PATH variable: There is a variable on Rhino called $PATH that contains a list of paths that Rhino searches for software. If you type a command like python, Rhino searches sequentially through each path in $PATH until it finds the first mention of python. Depending on the order of paths, it might end up accessing python from a place you don't expect. The same is true for a command like conda or mamba. You can check the order of paths by entering the command echo $PATH. You can edit the $PATH variable to change the order of paths.

I can't solve my environment ​

Sometimes Conda won't be able to resolve package dependencies in your environment's specification. If someone else has successfully created an environment from this specification it's likely an issue with how Conda is searching channels.

Channels are repositories that Conda searches for package versions. Conda searches these in a particular order depending on how you've configured it. In most cases, the solution is to make sure you've set up 'strict channel priorities'.

bash
conda config --set channel_priority strict

This means that Conda will always search through channels in the order they're specified. If that doesn't fix the issue, change the order of the channels until the environment can solve. If that doesn't work, check that the dependencies actually exist in the channels you're searching.

Released under the MIT License.

Copyright © 2024-present Will Hannon

+ + + + \ No newline at end of file diff --git a/sections/organizing-your-projects/index.html b/sections/organizing-your-projects/index.html new file mode 100644 index 0000000..b9112a1 --- /dev/null +++ b/sections/organizing-your-projects/index.html @@ -0,0 +1,25 @@ + + + + + + Section 2: Organizing your projects | Bench to Byte + + + + + + + + + + + + + + +
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Section 2: Organizing your projects ​

Organization matters in programming. Your goal should be that someone who isn't you can open your project directory and understand where things are, what things are, and why things are. Ideally, you want to front-load the organizational work for a new project. If you've ever done any significant reorganization of a mature project, you'll know how much of a headache that can be.

Below, I'll go through some high-level organizational principles that you should follow. There isn't one right way to do things, but there sure are a lot of wrong ways.

Creating a project ​

All of the code for a single project belongs in one directory. You should not split code for a single project among multiple distinct directories. Everything should be housed under one roof. But what is a project?

Imagine that you're researching the function of your favorite protein. You're probably taking advantage of a variety of assays and tools, each of which is analyzed in a particular fashion. It could be tempting to split each of these analyses into separate directories housed in different locations in your file system, but this is not good practice. Instead, organize them under a single directory.

Housing all of the analysis files for a project in a single directory has a variety of benefits:

  • It simplifies the process of backing up your code and analysis.
  • It makes it easy to share the relevant code when it comes time to publish.
  • It lets you track/version all of the analyses in your project with tools like git.

Generally, a good rule of thumb is a project corresponds to all of the analysis for a single paper. However, there can be exceptions for large papers that involve many distinct and involved computational pipelines. At the end of the day, you have to use your best judgment.

I'll use this project from the Bloom Lab as an example in the following sections.

Naming a project ​

Names should be simple and descriptive. The name should communicate the purpose of the project, but it shouldn't be so long that it's annoying to type out. Generally, you should ask yourself if the name you've chosen is:

  • Descriptive
  • Readable
  • Succinct
  • Consistent

Take this project as an example. Bernadeta Dadonaite from the Bloom Lab performed deep mutational scanning on the SARS-CoV-2 Delta variant Spike protein to identify mutations that escape neutralization by the therapeutic antibody REGN10933. All of the code for this analysis is located in a directory called 'SARS-CoV-2_Delta_spike_DMS_REGN10933'. The name clearly describes the project without being overly wordy. It also distinguishes this project from similar ones while adhering to the same naming scheme.

Organizing a project ​

After you've decided what your project is and how to name it, you should think about how to organize the files within. There are many correct ways to do this, but there are some general principles that you should try to follow. Below, we'll cover some of these general principles.

Project organization should be consistent. If you're working with someone opinionated about project organization, you should do your best to use their approach (or they'll be upset). Another thing to consider is whether there is an established set of best practices for the type of project you're working on. For example, a Snakemake pipeline typically adheres to a specific organizational structure. By sticking to this, you'll make it easier to develop your project and easier for others to follow along.

If you're working alone and there isn't a clear template for organizing your project, here are simple set of rules to follow:

  1. ALWAYS write a README: A 'README' is a plain text file––typically written in markdown––that describes the contents of your project. It should explain the purpose of the project, give credit to the creators, outline the basic organization of the directory, and give details on how to run the analysis. Here's the README for SARS-CoV-2_Delta_spike_DMS_REGN10933.

  2. Separate data from code: Separate the code that's responsible for the analysis from the data that you'll be analyzing. I always put data inside a directory called data/. The organization of data/'s contents is up to you.

  3. Separate results (i.e. derived data) from data: Your analysis will generate results (like plots and tables) and intermediate files (like alignments). Any files that are derived by your code should be kept separately from your input data/. I'd highly recommend storing your derived files in a directory called results/. The organization of results/ is up to you.

  4. Choose clear, explicit, and consistent names: For files and subdirectories, follow the naming advice described above.

  5. Avoid over-nesting: You can organize files and directories to a point where you have to dig through dozens of layers before you even find what you're looking for. When in doubt, flatten it out.

Summary ​

There are many reasonable ways to organize an analysis or project. However, the principles described above hopefully provide a general road map.

Released under the MIT License.

Copyright © 2024-present Will Hannon

+ + + + \ No newline at end of file diff --git a/sections/setting-up-your-ide/index.html b/sections/setting-up-your-ide/index.html new file mode 100644 index 0000000..dae1711 --- /dev/null +++ b/sections/setting-up-your-ide/index.html @@ -0,0 +1,32 @@ + + + + + + Section 1: Setting up your IDE | Bench to Byte + + + + + + + + + + + + + + +
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Section 1: Setting up your IDE ​

Those familiar with coding will know the importance of using the appropriate text editor. Hopefully, you know better than to write code in Microsoft Word. You've probably encountered tools like Rstudio for writing R, or Jupyter for writing Python. These tools are a perfectly reasonable way to write code, but they're inherently limited to a handful of languages. This quickly becomes an issue in scientific programming where a single project can consist of bash, Python, R, and markdown files.

An IDE, or Integrated Development Environment, provides a solution to this problem. With an IDE, you can write markdown documentation, edit a bash script, organize your directories, quickly check a .log file, and run a Python script all in the same window. I can't stress enough how much time an IDE can save you. However, so much functionality in one application can is intimidating. Below, I'll introduce you to Microsoft's Visual Studio Code (VS Code), one of the most approachable IDEs for general-purpose programming.

Note

I'd highly recommend giving VS Code a try even if you're familiar with another text editor for coding. However, I wouldn't recommend VS Code if you're primarily using R. I'd suggest that you use Rstudio for all R coding.

Getting started with VS Code ​

There is a reason that VS Code is the most popular code editor among software developers. It's powerful, flexible, user-friendly, and free. VS Code comes with lots of handy features, including intelligent autocomplete and built-in git commands that make it a breeze to keep track of changes to your code. However, the most powerful feature of VS Code is that it's customizable.

Installation

VS Code is a desktop application like any other. To install VS Code, simply download it from the VS Code website for your specific operating system (Windows, Mac, etc...).

User Interface ​

VS Code is laid out in a manner that makes it easy to edit code, navigate your project directory, and perform actions like git commands all in the same compact view. Here's an example of what a typical VS Code window looks like.

Image

An example VS Code project from the VS Code documentation

  • Activity Bar - On the far left side, this changes the content of the Side Bar and lets you perform activities like exploring your project directory or performing source control with git.

  • Side Bar - The sidebar is an interface for performing various actions depending on the Activity. For example, installing extensions or navigating your current directory.

  • Editor - The main area to edit your files. You can open as many editors as you like side-by-side, vertically, and horizontally.

  • Panel - Space below the code editor that houses additional functions. The most relevant feature for readers of this guide is the integrated terminal. You can collapse the panel out of view for more space.

  • Status Bar - Provides project-specific information.

In addition to these main visual elements, there is also an interface at the top of the page for inputting commands called the Command Pallet.

Command Pallet

The command pallet taken from the VS Code documentation

To open the command pallet, use the keyboard shortcut ⇧⌘P. This shortcut is the most important thing to remember when using VS Code. With the command pallet, you can perform a variety of actions from formatting a file, opening a terminal window, changing your settings, and more. Effectively, anything you can do in VS Code, you can do it with the command pallet.

Although this covers the basic interface of VS Code, there are a lot more features packed into the standard view that I didn't cover. If you're interested in learning more about the user interface for VS Code and how you can customize it, check out this page in the documentation.

Installing Extensions ​

VS Code comes standard with most of the features you'll need. However, there will probably be certain features specific to the project you're working on. For example, if you're frequently using Python, you might want to format your code with black when you save a file. If you're writing documents in markdown, you'll probably want some basic form of spellchecking. Although VS Code doesn't come with these features built-in, you can add them as 'extensions'. In most cases, VS Code will recommend appropriate extensions.

Here are several VS Code extensions that are particularly useful to us:

  • Python - IntelliSense for Python with features like code-checking and debugging.
  • Rainbow CSV - Syntax highlighting for CSV files.
  • Jupyter - Edit and run Jupyter notebooks right in VS Code.
  • Ruff - For fast Python linting (code-checking).
  • Black Formatter - Automatic formatting of Python code.
  • Snakemake - IntelliSense and code highlighting for Snakemake.
  • vscode-pdf - To open PDF files right in VS Code.
  • Protein Viewer - A simple 3D protein viewer that opens in VS Code.

To add extensions, you click on the Extensions button on the Activity Panel and search for extensions to install.

Install Extensions

An example showing how to install extensions from VS Code documentation

Configuring VSCode ​

In addition to customizing VS Code with extensions, it's also worth taking time to configure your settings. In the settings, you can configure everything from the appearance of your code to the code formatting tool that should be run on specific files when you save them. There are two ways to customize your settings: locally and globally.

The global settings that are applied to all workspaces are stored in a settings.json file. You can directly access this file by opening the command pallet (⇧⌘P) and typing in Preference: Open User Settings (the command pallet will autocomplete). However, the easiest way to edit the settings is to select Code > Settings > Settings from the application menu. This will provide a GUI where you change your settings alongside descriptions of what each setting does.

In addition to global settings, you can also configure settings on a project-by-project basis using workplace settings. These settings will only apply to the specific project (directory) that you've configured them in. These settings are stored in a project within a .vscode directory. You set these the same way you set global settings, but you select Workspace rather than User in the settings menu.

Settings Example

Here is an example of the settings menu from the VS Code documentation.

Command Line Interface ​

In addition to interacting with VS Code through the application itself, you can also open up projects and files in VS Code directly from the command line. Here are some of the most helpful commands when using VS Code.

bash
# open VS Code with current directory in the Explorer
+code .
+
+# create a new window
+code -n
+
+# see help options
+code --help

Summary ​

VS Code is a must for projects where you're editing many files of different formats. However, VS Code is not a singular solution to all code editing needs. Although you can run code interactively in a Jupyter Notebook from within VS Code, it can often be faster to open up Jupyter from the command line for quick interactive scripting. Additionally, programming languages like R have specific IDEs like Rstudio that make them the default choice for projects built around R code.

Helpful Resources ​

Check out this page on the VS Code documentation for a thorough walkthrough of the basics.

Released under the MIT License.

Copyright © 2024-present Will Hannon

+ + + + \ No newline at end of file diff --git a/sections/tracking-your-code/index.html b/sections/tracking-your-code/index.html new file mode 100644 index 0000000..6aee416 --- /dev/null +++ b/sections/tracking-your-code/index.html @@ -0,0 +1,28 @@ + + + + + + Section 5: Tracking code with Git | Bench to Byte + + + + + + + + + + + + + + +
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Section 5: Tracking code with Git ​

I use version control for nearly all of the programming I do. It's a crucial skill for learning to how program that can fall by the wayside in favor of learning a specific programming language. Leaning to program and learning version control should happen side-by-side. But what is "version control?" Version control is more than just saving a file. Here's a description from the book Pro Git:

Version control is a system that records changes to a file or set of files over time so that you can recall specific versions later.

By preserving a history of incremental changes over time, you can go back and see what you've done, find out when and why something stopped working, make multiple changes to your project in parallel, and more. In this section, I'll introduce Git, a common tool for version control developed by Linus Torvalds.

TIP

This free book is the definitive resource for learning Git. I highly recommend skimming chapters 1-3.

How does git work? ​

At the core, Git records changes to your files over time. Unlike other systems that store differences between file versions, Git takes snapshots of your entire project each time you make a commit. These snapshots allow you to revisit any point in your project's history.

Here's a high-level overview of how git tracks your files:

  1. Repository Initialization: When you initialize a Git repository in your project directory using git init, Git starts monitoring the files within that directory.

  2. Working Directory and Staging Area: As you modify files, the changes are reflected in your 'working' directory. To prepare changes for a commit, you add them to the staging area using git add. This step lets you decide which changes to include in your next snapshot.

  3. Committing Changes: After staging, you create a commit with git commit. This action records a snapshot of the staged changes and adds it to your project's history. Each commit is uniquely identified, allowing you to track and revert to specific versions.

  4. Local and Remote Repositories: Git stores your project's history locally, but, optionally, you can also link your repository to a remote server like GitHub or Bitbucket which enables collaboration, backup, and access from different machines. I'll talk more about this in the next section.

Working tree

Working tree, staging area, and Git directory taken from Pro Git

Basic git ​

To get started with Git, you'll need to familiarize yourself with a couple key commands:

Initialize a Repository:

You create a new Git repository by telling Git to start tracking the code in a directory.

bash
mkdir my-project
+cd my-project
+git init

Or, you can clone an existing Git repository from a remote server to your local machine.

bash
git clone <repository URL>
+cd my_project

TIP

A quick note about cloning a remote repository. There are several methods for doing this, some of which require a little set up, and those will be covered in the next section.

Check Repository Status:

After you've made some changes in your directory, you can check what these are using the following command.

bash
git status

This command displays the status of your working directory and staging area. It shows what file have been edited, what new files aren't being tracked, and more.

Add Changes to Staging Area:

bash
git add [file(s)]

Stages specific files for the next commit. Use git add . to stage all changes.

Commit Changes:

bash
git commit -m "Your commit message"

Records a snapshot of the staged changes. The commit message should briefly describe what you've done. It's important to write a good commit message.

View Commit History:

You can see a log of all commits that have been made to a project.

bash
git log

Compare Changes:

This can be useful, but you're unlikely to do it much.

bash
git diff

It compares changes between commits, branches, or your working directory and the last commit.

Push Changes to Remote Repository:

bash
git push

Uploads your local commits to a remote repository.

Pull Updates from Remote Repository:

bash
git pull

Fetches and merges updates from a remote repository into your local repository.

These commands form the backbone of Git operations. Regular use of git status and git log will help you stay informed about your project's state and history.

Branching and Merging ​

Branches are a powerful feature in Git that allow you to diverge from the main codebase and work independently on a set of changes. The primary branch is usually called main or sometimes master, and it's where the stable version of your project resides.

Why Use Branches?

Branching your code with Git allows for parallel development of multiple features or fixes simultaneously without interference. While you're editing the code on a branch, the main branch remains stable, and new code is only merged after it's ready. Here are some things you might use branches for:

  1. Feature Development: When adding new features, you can create a branch to isolate your work. This way, the main codebase remains unaffected until the feature is complete and tested.

  2. Bug Fixes: For fixing bugs, especially in a production environment, branches let you address issues without disrupting ongoing development work.

  3. Experimentation: If you're trying out new ideas or approaches, branches provide a safe space to experiment without the risk of breaking existing code.

See all active branches:

If you want to see what branches you have active locally, run:

bash
git branch

This will print out the branches you have in your local repo and tell you which branch you're currently on.

Switch to a Branch:

If you want to switch (or checkout) a different branch, run:

bash
git checkout [branch-name]

This switches your working directory to the specified branch.

Create and Switch to a New Branch:

To make a new branch, run:

bash
git checkout -b [branch-name]

This is a shortcut that creates a new branch and switches to it immediately.

Merge a Branch:

After you've completed work on a branch and ensured it's stable, you'll want to merge it back into the main branch. This process integrates your changes and updates the main codebase. First, checkout the branch you want to merge into:

bash
git checkout [target-branch]

Once you're in the target branch (for example, main), merge the branch with your changes by running:

bash
git merge [source-branch]

This merges the changes in the [source-branch] into [target-branch].

Note

While this approach works well for merging branches locally, in collaborative projects, you'll typically merge branches using pull requests. I'll cover this in more detail in the next section.

Released under the MIT License.

Copyright © 2024-present Will Hannon

+ + + + \ No newline at end of file diff --git a/sections/using-remote-resources/index.html b/sections/using-remote-resources/index.html new file mode 100644 index 0000000..3bb7104 --- /dev/null +++ b/sections/using-remote-resources/index.html @@ -0,0 +1,44 @@ + + + + + + Section 3: Using remote computing resources | Bench to Byte + + + + + + + + + + + + + + +
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Section 3: Using remote computing resources ​

In this section, I'll describe how and why we use remote computational resources. I'll walk you though how to access remote file systems and how to run code on a cluster.

Computing at Fred Hutch Cancer Center ​

TIP

This wiki is an invaluable resource for people doing scientific computing at Fred Hutch Cancer Center. It goes in depth on every topic that's covered in this section.

In the Bloom lab, we use the Fred Hutch Cancer Center's file system to store our notebooks, data, and code. You can access the file system through a variety of methods. The most common way that people access our lab's files is by mounting the file system to their computer. This allows you to open and edit these files on your local machine with the advantage that you can navigate through files using a GUI (Graphical User Interface) like Finder on Mac or File Explorer on Windows.

The shared folders in the file system are all located at /fh/. There are several shared folders including /fh/fast, /fh/scratch and /fh/secure, but our lab's files are located under /fh/fast in /fh/fast/bloom_j/. Computational projects are stored under /fh/fast/bloom_j/ in a directory called computational_notebooks/ and organized by the lab member's Fred Hutch username and the year that a project was started.

text
/fh/fast/bloom_j/computational_notebooks/
+|
+|
+––––– <username>/
+    |
+    |
+    ––––– <year>/
+        |
+        |
+        ––––– <project>/

For example, if I have a project called HMPV-Antibody-Passaging that I created in 2024, it's location on the file system should be /fh/fast/bloom_j/computational_notebooks/whannon/2024/HMPV-Antibody-Passaging. Aside from the organizational benefits of adhering to this convention, all of the code in /fh/fast/bloom_j/computational_notebooks/ is automatically backed up on a daily basis.

Access through the command line ​

In addition to mounting the file system to your local computer as a network drive, you can connect to the file system by accessing a remote computer cluster called Rhino. Rhino is a cluster of 3 large memory, shared computers that are intended for interactively prototyping and writing code. You can use Rhino to execute code to analyze your data, however, Rhino is not intended for intensive computational tasks (like aligning sequencing reads). Fred Hutch has a separate computer cluster called Gizmo for intensive tasks.

Logging into Rhino ​

You log into Rhino through the command line or shell. If you're not familiar with the command line, check out this excellent tutorial.

Log into Rhino by opening up the command line and typing the ssh command.

bash
ssh username@rhino

You'll be prompted to enter your Fred Hutch password. Note, you can only access Rhino like this while using the Fred Hutch wifi. You can log into Rhino without being on the wifi, but that requires an alternative method described in a later section. Now, you'll see that your terminal's prompt has changed to something like username@rhino02:~$ , and commands that you run from the command line will be executed by Rhino.

When you initially connect to Rhino, you're location on the file system is a special 'home' directory that's specific to each user and located at /home/username. We'll use this home directory later, but for now, most of our code and data should be located in the Bloom lab's directory (/fh/fast/bloom_j/). We could move there by navigating to our computational notebook like so.

bash
cd /fh/fast/bloom_j/computational_notebooks/username/

But, it becomes annoying if you have to remember the full path every time you log onto Rhino. Instead, we can make our lives a little easier with 'symbolic links'. These are a way of making shortcuts between places on the file system. Instead of typing the full path each time, we can type a shortened version and it will jump us to the correct location.

Run the following command to make a symbolic link to your computational notebook.

bash
ln -s fh/fast/bloom_j/computational_notebooks/username/ username/

Now, you can access your folder in computational_notebooks from either it's original path (cd fh/fast/bloom_j/computational_notebooks/username) or a new path from your home directory (cd ~/username). Both paths will take you to the same place.

Setting up SSH keys ​

The approach outlined above is a perfectly good way to log into Rhino, but entering your password each time gets annoying. Instead, I'd recommend setting up SSH to be password-free using SSH keys. This will allow you to log in securely without needing to enter your password every time.

SSH keys are a pair of cryptographic keys (one public, one private) used to authenticate your identity when connecting to a remote server like Rhino. The private key stays on your local machine and is kept secret, while the public key is placed with the server. When you log in, the server checks that the private key on your machine matches the public key it has, allowing you to authenticate without needing to enter a password.

WARNING

The process for generating SSH keys differs between operating systems. The following steps apply to a local computer with Mac OS and a remote computer with Ubuntu Linux (Rhino). There are tons of tutorials online for setting up SSH keys if your computer has a different operating system.

Step 1: Generate an SSH Key Pair ​

First, you'll need to generate a pair of SSH keys on your local machine. To do this, open a terminal (make sure you're not logged into Rhino) and run the following command:

bash
ssh-keygen -t rsa -b 4096 -C "rhino.fhcrc.org"
  • -t rsa specifies that you're generating an RSA key.
  • -b 4096 generates a 4096-bit key for added security.
  • -C "rhino.fhcrc.org" adds an optional label (in this case, the name of the remote host) to help identify your key.

When you're be prompted for a file location, press Enter to select the default (~/.ssh/id_rsa). When you're prompted to enter an optional passphrase, also press Enter to skip this step.

Step 2: Copy the public key to Rhino ​

Now you'll have two keys located at ~/.ssh/id_rsa, one public and one private. Copy the public key to Rhino using the following command:

bash
ssh-copy-id username@rhino

Your public SSH key will now be on Rhino in ~/.ssh/authorized_keys where ~ is your home directory on the server (/home/username).

Step 3: Log in to make sure it worked ​

You should be able to log into Rhino without a password now. Test it out:

bash
ssh username@rhino

If everything worked, you won't be prompted for a password.

Step 4: Edit your SSH config ​

This step is optional, but it will make life easier down the road. Make a new file called 'config' (no extension) in ~/ssh/ on your local machine and add the following line:

text
Host rhino
+  HostName rhino
+  User username
+  ForwardX11 yes
+  ForwardAgent yes
+  ForwardX11Trusted yes
+  IdentityFile ~/.ssh/id_rsa

Setting up a proxy jump ​

So far, the approaches detailed above will only work when you're on the Fred Hutch wifi. But what about if you're working from home and you want to access the sever? The simplest option is to log into the Fred Hutch VPN from a Fred Hutch managed computer and log into Rhino like you normally would. However, if you don't have a Fred Hutch managed computer, this won't work. In that case, the other option is to use a 'proxy jump'.

A proxy jump involves logging into Rhino through an intermediate host called Snail. First you log into Snail, then from Snail you log into Rhino. Thankfully, we can automate this process using SSH keys and the SSH config. Here's a detailed tutorial on setting up a proxy jump to Rhino with Snail.

Computationally intensive computing with Gizmo ​

The Rhino cluster is intended for accessing and manipulating files, running and developing programs, managing software environments, and interactive coding. It's not intended running for computationally intensive jobs (such as aligning sequencing reads) that take a long time or use many CPUs. For those kinds of jobs, we use the Gizmo cluster. Like Rhino, Gizmo is a computer cluster managed by Fred Hutch; unlike Rhino, it's very, very large.

There are two methods of running programs on Gizmo: (1) submitting them as jobs with Slurm or (2) running them interactively with grabnode. I'll discuss both below.

Note

If you've never used Gizmo before, you'll need to ask Fred Hutch Scicomp for permission. Shoot them a quick email and you'll usually hear back within an hour.

Using Gizmo interactively ​

This is the easiest way to get access to Gizmo. Log into Rhino and type:

bash
grabnode

You'll be prompted with several questions including how many CPU you want, how much memory you need, how much time you need, and whether you need a GPU. It can take a few minutes for the system to allocate you resources. When it's done, you'll see that you're prompt has changed from something like username@rhino02:~$ to username@gizmok3:~$ . Now, anything you type into the command line will be run on the Gizmo cluster.

This is a great approach for troubleshooting computationally intensive tasks interactively. However, it's not a good approach for running jobs you expect to take a long time. The resources you request from Gizmo will be released when you log out. That means that any job that's running will be terminated if you log out or lose connection to the server. There's a way to get around this constraint with tmux, but it's better to submit long-running jobs with Slurm.

Using Gizmo with Slurm ​

This is the more common use case for Gizmo. If you have a long-running computationally-intensive job, for example, a Snakemake pipeline that performs many sequencing alignments, you can submit a 'job' to Gizmo and the code will run until it's finished. You're using the resources of Gizmo just like you do above, but you don't need to leave Rhino and your jobs will keep running if you log out.

Gizmo uses a program called Slurm to handle job submission. Slurm (Simple Linux Utility for Resource Management) is an open-source job scheduling system used for managing and allocating computing resources in a cluster.

  1. Submitting a Job

To submit a job in Slurm, you use the sbatch command with a job script. The script contains the commands you want to run and Slurm directives to request resources. A simple bash script (run_job.bash) looks like this:

bash
#!/bin/bash
+#
+#SBATCH -c 16
+
+snakemake -j 16 --software-deployment-method conda -s dms-vep-pipeline-3/Snakefile

The comments starting with #SBATCH configure Slurm by providing arguments like how many CPUs you need (#SBATCH -c 16). You submit the job script to Slurm using the sbatch command as follows:

bash
sbatch run_job.bash

You can check on the status of your jobs running on Gizmo with the squeue command. This command shows you the jobs you have running and how long they've been running for.

bash
squeue -u username

If you want to cancel a job that's running, use the scancel command.

bash
scancel -u username

There's a lot more to Slurm, but that's most of what you'll need to know.

Using tmux ​

As I mentioned above, you may run into a scenario where you want to run something interactively and you don't want the process to terminate when you log off the server. That's where tmux comes in handy. tmux (Terminal Multiplexer) is software that's pre-loaded on Rhino and Gizmo. It allows you to create multiple terminal sessions within a single window, and more importantly, lets you detach from a session while keeping it running in the background. This way, you can log off and later reconnect to the session to check on your processes or pick up where you left off.

1. Start a new session ​

To start a new tmux session, use the command:

bash
tmux

This opens a new terminal window with a little green bar at the bottom. You'd use this terminal as you would normally.

2. Detach from a session ​

To detach from a session and leave it running, press Ctrl + B, followed by D. This will bring you back to your original terminal prompt, but your tmux session will continue to run in the background.

3. List active sessions ​

If you want to see a list of all the running tmux sessions, use:

bash
tmux ls

4. Reattach to a session ​

To reattach to a session you previously detached from, use:

bash
tmux attach -t <session_id>

Replace <session_id> with the number or name (if you named it) of the session you want to reconnect to, which can be found using the tmux ls command.

5. Kill a session ​

When you're done, you can kill a tmux session by typing exit within the session or by pressing Ctrl + D.

Released under the MIT License.

Copyright © 2024-present Will Hannon

+ + + + \ No newline at end of file diff --git a/sections/working-collaboratively/index.html b/sections/working-collaboratively/index.html new file mode 100644 index 0000000..a723a41 --- /dev/null +++ b/sections/working-collaboratively/index.html @@ -0,0 +1,25 @@ + + + + + + Section 6: Working collaboratively | Bench to Byte + + + + + + + + + + + + + + +
Bench2Byte

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Section 6: Working collaboratively ​

In the last section, I talked about tracking your code with Git. In principle, this can all happen locally in your repository, but in many cases, you want to make a remote copy of a repository and its history. That's where services like GitHub come in. GitHub is a website that acts as a remote repository for your code, allowing you to store it online, collaborate with others, and take advantage of various tools for version control and project management.

Using GitHub as a 'Remote' ​

What's a Remote? ​

A remote in Git is simply a version of your project that's hosted on the internet or another network. It's the counterpart to your local repository on your computer. By pushing your local changes to the remote repository, you ensure that your code is backed up and accessible from other locations or by other collaborators.

Why Use GitHub? ​

GitHub is our preferred website for hosting Git repositories remotely. It provides a platform for:

  • Collaborative Coding: Multiple people can work on the same project simultaneously without overwriting each other's changes.
  • Version Control: Keeps track of every change made to the codebase, allowing you to revert to previous versions if needed.
  • Documentation Hosting: With GitHub Pages, you can host project documentation or even entire websites directly from your repository.

In our lab, we use GitHub to coordinate coding efforts, especially for projects that will be part of publications. By connecting your local Git repository to GitHub, you make it easier to share your work and collaborate effectively.

Bloom Lab GitHub Organizations ​

In the Bloom lab, we use specific GitHub organizations to manage our projects:

But how do you decide when to host your local project remotely? Generally, I host a project remotely on GitHub for two reasons:

  1. If I'm working on code that's likely to be connected to a paper: Hosting your code on GitHub early makes it easy when you need links to data and code for a manuscript.
  2. If I'm working on code with someone else: If you code is on GitHub it makes it easy to share with collaborators or work on the code alongside with else.

Connecting a local repository to a remote ​

As I mentioned in the previous section, Git is the version control system you use locally on your machine, while GitHub is the remote hosting service where you can store your repositories online. To connect a local repository to a GitHub remote you:

  1. Initialize a Local Repository: You can start from scratch by creating a local Git repository with git init. However, you can also connect an existing repository to a GitHub remote repository at any time.

  2. Create a Remote Repository: On GitHub, you create a new repository to host your project in the appropriate organization or account.

  3. Link Local and Remote Repositories: When you create a new GitHub repository, you'll get instructions for connecting your local repository to the GitHub repository.

  4. Push and Pull Changes: Now, you can use git push to upload your local commits to GitHub and git pull to update your local repository with changes from GitHub.

Cloning a remote repository locally ​

You can also clone an existing repository on GitHub onto your local machine. The local clone will be automatically connected to the remote repository it was cloned from, allowing you to pull updates or push changes.

To clone a repository, you can use the following command in your terminal, replacing [repository-url] with the URL of the repository you want to clone:

bash
git clone [repository-url]

There are two main protocols for cloning repositories: HTTP and SSH.

  • HTTP: No extra configuration, but you’ll need to enter your username and a personal access token every time you push changes.
  • SSH: A more secure and convenient method, which allows you to authenticate using SSH keys (no passwords!).

TIP

Only fools use HTTP.

Setting up SSH for GitHub ​

Using SSH is preferred because it’s more secure and doesn’t require you to input credentials each time you push or pull changes. Once you’ve set up your SSH keys, GitHub will trust your machine, allowing you to interact with your repositories seamlessly.

Setting up SSH for GitHub is similar to setting up SSH for Rhino. The main difference is that you'll add your public key to your account on GitHub. You should set this up for any computer you use GitHub with.

Here are detailed and up-to-date instructions for connecting to GitHub with SSH.

Collaborative Workflows ​

How do you work on code with others productively? It requires some structure to prevent conflicts and ensure code quality. Note, the following workflow mainly applies to projects where you're actively coding with other people. Small projects or projects in their early stages don't require this level of organization.

Best Practices ​

  1. Protect the Main Branch: The main branch (sometimes called master) should represent the stable, release-ready version of your code––what you'd link to in a paper. It's important to protect it by restricting direct commits. You can do this in the repository settings on GitHub.

  2. Use Feature Branches: When you're working on a new feature or addressing an issue, create a new branch from main. This keeps your changes isolated until they're ready to be merged. Use descriptive names for your branches, like sequencing-analysis-improvement or typo-correction-in-readme.

  3. Merge Changes with Pull Requests: When a feature branch is ready to become a part of main initiate a pull request on GitHub to merge the changes on the feature branch into main. Typically, these changes will be reviewed by at least one other person.

Pull Requests ​

A pull request (PR) is a way to propose changes you've made on a branch to be merged into another branch (usually main). It allows others to review your code, discuss potential issues, and approve the changes before they become part of the main codebase.

How to Make a Pull Request ​

  1. Push Your Branch to GitHub: Use git push to upload your branch.

  2. Navigate to the Repository on GitHub: You'll see a prompt to create a pull request for your recently pushed branch.

  3. Create the Pull Request: Provide a descriptive title and include any relevant information or context in the description. You can also link your pull request to specific issues so that they automatically close when the branch is merged.

  4. Review and Merge: Team members can review the pull request, leave comments, and approve it. Once approved, it can be merged into the main branch.

Merge Conflicts ​

When multiple collaborators edit the same parts of a file, Git may not be able to automatically merge the changes. This results in a 'merge conflict', which you'll need to resolve manually. A typical workflow for resolving these conflicts is as follows:

  1. Identify the Conflict: Git will mark the conflicting areas in the file.
  2. Choose the Correct Code: Decide which changes to keep—yours, theirs, or a combination.
  3. Edit the File: Remove the conflict markers and make the necessary edits.
  4. Commit the Resolution: After resolving the conflicts, commit the changes to complete the merge.

Generally, you're unlikely to run into many merge conflicts while coding in the Bloom Lab.

Issues and Discussions ​

Another valuable feature of GitHub is the ability to open 'Issues' on repository. Issues should be used for the following things:

  1. Suggesting Features: Outline a new feature you want to add to a project.
  2. Reporting Bugs: Highlight an issue with a project's code.
  3. Asking Questions: Seek clarification on how to use run code. This is something you might have to do if you're using someone else's code.

The best practices for opening Issues is to be as descriptive as possible, use labels to categorize issues, and reference related Issues or pull requests. Another important thing to note is that you should not use Slack for code-related discussions. Unlike Slack messages, Issues on GitHub are persistent, organized, and easy to keep track of.

Released under the MIT License.

Copyright © 2024-present Will Hannon

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