Merge pull request #54 from fhdsl/DNA_seq_goals

add blurb on goals

07-microarray-data.Rmd

@@ -39,7 +39,7 @@ On a basic principle, oligonucleotide probes are designed for different targets
 ### Cons:
 
 - Microarray chips can only measure the targets they are designed for, and cannot be used for exploratory purposes [@Zhang2015].
-- Microarrays' probe designs can only be as up to date as the genome they were designed against at the time [@Mantione2014; @refinebioexamples].
+- Microarrays' probe designs can only be as up to date as the genome they were designed against at the time [@Mantione2014; @refinebioexamples2019].
 - Microarray does not escape oligonucleotide biases like GC content and sequence composition biases[@refinebioexamples2019].
 
 
@@ -66,8 +66,8 @@ Gene expression arrays are designed to measure gene expression. They are designe
 
 #### Examples:
 - [refine.bio](https://www.refine.bio/) is the largest collection of publicly available, already normalized gene expression data (including gene expression microarrays).
-- [Getting started in gene expression microarray analysis](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000543) [@Slonim2009].
-- [Microarray and its applications](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3467903/) [@Govindarajan2012].
+- [Getting started in gene expression microarray analysis](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000543) [@Slonim_Yanai_2009].
+- [Microarray and its applications](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3467903/) [-@Govindarajan2012].
 - [Analysis of microarray experiments of gene expression profiling](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2435252/) [@Tarca2006].
 
 ### DNA methylation arrays

09-DNA.Rmd

@@ -19,15 +19,36 @@ ottrpal::include_slide("https://docs.google.com/presentation/d/1YwxXy2rnUgbx_7B7
 
 ## What are the goals of analyzing DNA sequences?
 
-```{r, fig.alt = "", out.width = "100%", echo = FALSE}
-ottrpal::include_slide("https://docs.google.com/presentation/d/1YwxXy2rnUgbx_7B7ENH9wpDX-j6JpJz6lGVzOkjo0qY/edit#slide=id.g12890ae15d7_0_71")
-```
+There are several larger goals behind DNA sequencing experiments ranging from assembling whole genomes, to identifying variation or performing a functional genomic analysis or comparative genomic study. Each of these has implications when studying disease.
+
+* Assembling whole genomes:
+
+  Because an organism's genome determines how an organism develops and functions [@NHGRIGlossary2024], an important task in the genomics field is assembling the genome of an organism from sequencing reads. This assembly process attempts to reconstruct how the sequencing reads overlap or fit together [@Schatz2010; @Li_Durbin_2024]. Recent examples of genome assembly in the genomics field include a complete 3.055 billion-base pair sequence of the human reference genome which was published by the Telomere-to-Telomere (T2T) Consortium [-@Nurk2022], the T2T-CHM13 version (followed not long after by the complete sequence of the human Y chromosome [-@Rhie2023]). A goal of the field is to better capture human genetic diversity by creating a reference pangenome, assembled from multiple donors within the population [-@Taylor_2024]. Genome assemblies are an important part of genomics beyond human genomics research; there are reference gnomes available for most model organisms as well as many plants, animals, and pathogens, with more and more being published at a high frequency [@Miller2023; @Alonge2022; @Gershman2023; @Sistrom2016]. These reference genomes each act as an extensive compilation of the observed DNA sequence of genes, regulatory elements, etc. and the related coordinate systems for these elements, such that, for the corresponding organism, sequencing reads from other experiments can be mapped or aligned to the reference in order to localize where that read was in the genome. In the case of cancer informatics, a recent approach utilized personalized genome assembly to more accurately detect tumor somatic mutations. This is likely to be an area of future research for application in precision medicine [@Xiao2022; @Ermini_Driguez_2024].   
+
+* Identifying variation:
+
+  Variant caller software is used within the field of genomics to identify places where reads from a DNA sequencing experiment differ from a comparative reference genome sequence [@NHGRIfactsheet2022]. Variants may be as small as single nucleotide differences (single-nucleotide polymorphisms or SNPs) or much larger (50 base pairs or more) structural variation (SVs) such as duplications, deletions, insertions, inversions, translocations [@Wong2011]. (Shorter insertions or deletions are termed indels.) The SVs involving gains or losses in genomic DNA can lead to copy number variations (CNVs). Mutation and structural variants are very common in cancer as well as larger-scale catastrophic genomic rearrangements [@Zhang2022]. Overall, variants may be rare in a population or fairly common [@Audano2019]. Further, variants may be somatic or germline variants: germline variants are hereditary and will be passed down from parent to offspring; in the offspring, the variant will be present in every cell, while somatic variants are generally not hereditary and present only in some cells rather than every cell [@NHSFrost2022]. Because variation, specifically genetic diversity is a necessary part of a healthy species [@GeneticDiversity] and because variation, specifically mutations/variants may cause disease, identifying variation is a common goal in a DNA sequencing workflow. An example of research focusing on studying genetic diversity in humans is the 1000 Genomes Project which recently expanded its resource of sequenced genomes and in doing so discovered even more variation present in the population [@Byrska-Bishop2022].      
+
+* Functional genomic analysis:
+
+  Genomes contain more than just genes (the coding sequences that will be transcribed and translated into a protein); they also contain functional elements such as promoters, enhancers, or silencers that modulate the expression of genes [@Kellis2014]. Further, differential gene expression is the phenomenon by which cells with the same DNA sequence show different patterns of gene expression. Functional genomic analyses aim to better understand differential gene expression and the impact of genetic variation found in functional elements. For example, many human genetic variants associated with common traits and diseases are localized in or near known functional elements [@Hindorff2009]. These variants may impact gene expression due to either changes in transcription factor binding at that site, or resulting epigenetic changes, which are defined as chemical modifications of chromatin or nucleotides beyond the DNA sequence. Such epigenetic modifications, which include histone marks and DNA methylation, can alter DNA compaction and influence a functional element’s accessibility for transcriptional machinery (e.g., if the element isn't accessible, transcription may not occur; while previously the element was accessible and the gene could be transcribed). In later sections, methods that study epigenetic modifications like chromatin accessibility, DNA methylation, or binding of specific proteins will be discussed. All of these methods support functional genomic analyses and are important for better understanding differential gene expression and the impact of genetic variants located in functional elements may have on disease occurrence. A somewhat recent and high profile example of a functional genomic analysis centers again on work from the T2T Consortium. Not only did they publish a new, complete reference genome, but they also studied the epigenetic landscape in the newly resolved regions of the genome and pointed to potential newly discovered functional elements in a region previously thought to be transcriptionally inactive [@Gershman2022].
+
+* Comparative genomics:
+
+  A common saying in the genomics field is that structure determines function and conserved structure may be constrained such that there is an important function which needs to be conserved [@Alföldi_Lindblad-Toh_2013]. Further, similarities in structure may be due to shared ancestry through the processes of evolution; therefore, some comparative genomics studies aim to infer homology or an evolutionary relationship from structural similarity [@Pearson2013]. More pertinent to the topics discussed previously, comparative genomics studies are also useful for identifying functional elements [@Taylor2006] and variants associated with disease (e.g., by comparing the genomes of those with the disease and those without it and identifying differences) [@Alföldi_Lindblad-Toh_2013; @Eichler_2019].  
 
 ## Comparison of DNA methods
 
 ```{r, fig.alt = "Comparing DNA Sequencing Techniques. The most common DNA sequencing techniques are described. Whole genome sequencing coverages all genes and non-coding DNA. 3.2 billion bases are covered when applied to human samples. This the most expensive of the techniques. Depth of coverage required for 99.9% sensitivity is 30X. Whole exome sequencing coverage is the exome or expressed genes. Approximately 45 million bases are sequenced. This is a cost-effective technique. The depth of coverage required for 99.9% sensitivity is 100X. Targeted gene panel sequencing coverages 50-500 genes. 20,000 to 62 million bases are sequenced. This is the most cost-effective technique. Depth of coverage is >500X.", out.width = "100%", echo = FALSE}
 ottrpal::include_slide("https://docs.google.com/presentation/d/1YwxXy2rnUgbx_7B7ENH9wpDX-j6JpJz6lGVzOkjo0qY/edit#slide=id.g138a6ce16b7_35_18")
 ```
+There are four DNA sequencing methods discussed in this chapter. The above graph compares WGS, WXS, and Targeted gene sequencing. The last section compares all 4.
+
+1. Whole genome sequencing (WGS)
+2. Whole exome sequencing (WXS)
+3. Targeted gene sequencing
+4. DNA/SNP microarrays
+
 Compared to WXS and Targeted Gene Sequencing, WGS is the most expensive but requires the lowest depth of coverage to achieve 95% sensitivity. In other words, WGS requires sequencing each region of the genome (3.2 billion bases) 30 times in order to confidently be able to pick up all possible meaningful variants. [@Sims2014] goes into more depth on how these depths are calculated.
 
 Alternatively, WXS is a more cost effective way to study the genome, focusing places in the genome that have open reading frames -- aka generally genes that are able to be expressed. This focuses on enriching for exons and not introns so splicing variants may be missed. In this case, each gene must be sequenced 80-100x for sufficient sensitivity to pick up meaningful variants.

10b-single-cell-RNA-seq.Rmd

@@ -155,13 +155,13 @@ These tutorials cover explicit steps, code, tool recommendations and other consi
 - [Processing raw 10X Genomics single-cell RNA-seq data (with cellranger)](https://swaruplab.bio.uci.edu/tutorial/cellranger/cellranger-rna.html) - a tutorial based on using CellRanger.
 
 ## Useful readings
-- [An Introduction to the Analysis of Single-Cell RNA-Sequencing Data](https://doi.org/10.1016/j.omtm.2018.07.003) [@AlJanahi2018].
-- [Orchestrating single-cell analysis with Bioconductor](https://www.nature.com/articles/s41592-019-0654-x) [@Amezquita2019].
+- [An Introduction to the Analysis of Single-Cell RNA-Sequencing Data](https://doi.org/10.1016/j.omtm.2018.07.003) [@Aljanahi2018].
+- [Orchestrating single-cell analysis with Bioconductor](https://www.nature.com/articles/s41592-019-0654-x) [@Amezquita2020].
 - [UMIs the problem, the solution and the proof](https://cgatoxford.wordpress.com/2015/08/14/unique-molecular-identifiers-the-problem-the-solution-and-the-proof/) [@Smith2015].
 - [Experimental design for single-cell RNA sequencing](https://doi.org/10.1093/bfgp/elx035) [@BaranGale2018].
-- [Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies](https://doi.org/10.1038/s41596-018-0073-y) [@Lafzi2019].
-- [Comparative Analysis of Single-Cell RNA Sequencing Methods](http://dx.doi.org/10.1016/j.molcel.2017.01.023) [@Ziegenhain2018].
-- [Comparative Analysis of Droplet-Based Ultra-High-Throughput Single-Cell RNA-Seq Systems](https://doi.org/10.1016/j.molcel.2018.10.020) [@Zhang2018].
+- [Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies](https://doi.org/10.1038/s41596-018-0073-y) [@Lafzi2018].
+- [Comparative Analysis of Single-Cell RNA Sequencing Methods](http://dx.doi.org/10.1016/j.molcel.2017.01.023) [@Ziegenhain2017].
+- [Comparative Analysis of Droplet-Based Ultra-High-Throughput Single-Cell RNA-Seq Systems](https://doi.org/10.1016/j.molcel.2018.10.020) [@Zhang2019].
 - [Single cells make big data: New challenges and opportunities in transcriptomics](http://dx.doi.org/10.1016/j.coisb.2017.07.004) [@Angerer2017].
 - [Comparative Analysis of common alignment tools for single cell RNA sequencing](https://www.biorxiv.org/content/10.1101/2021.02.15.430948v2) [@Bruning2021].
 - [Current best practices in single-cell RNA-seq analysis: a tutorial](https://doi.org/10.15252/msb.20188746) [@Luecken2019].

11c-ChIP-Seq.Rmd

@@ -118,7 +118,7 @@ Annotation
 
 ### Tools for preprocessing
 
-- [Trimmomatic](http://www.usadellab.org/cms/?page=trimmomatic) is a widely used tool for trimming and filtering Illumina sequencing data. It is often used to remove low-quality reads, adapter sequences, and other artifacts that can affect downstream analysis.
+- [Trimmomatic](http://www.usadellab.org/cms/index.php?page=trimmomatic) is a widely used tool for trimming and filtering Illumina sequencing data. It is often used to remove low-quality reads, adapter sequences, and other artifacts that can affect downstream analysis.
 - [Cutadapt](https://cutadapt.readthedocs.io/en/stable/) is another popular tool for trimming adapter sequences from high-throughput sequencing data. It is particularly useful for removing adapters that contain degenerate nucleotides or that have been ligated with variable lengths.
 - [Bowtie2](https://bowtie-bio.sourceforge.net/bowtie2/manual.shtml) is a fast and memory-efficient tool for aligning sequencing reads to a reference genome. It is often used to map ChIP-Seq reads to the genome prior to peak calling.
 - [SAMtools](http://www.htslib.org/) is a suite of tools for manipulating SAM/BAM files, which are commonly used to store alignment data from high-throughput sequencing experiments. It can be used for filtering and sorting reads, as well as for generating summary statistics.

11d-CUT-and-RUN.Rmd

@@ -48,7 +48,7 @@ ottrpal::include_slide("https://docs.google.com/presentation/d/1YwxXy2rnUgbx_7B7
 
 ### CUT&RUN
 
-**Cleavage Under Targets and Release Using Nuclease**, **CUT&RUN** for short, is an antibody-targeted chromatin profiling method to measure the histone modification enrichment or transcription factor binding. This is a more advanced technology for epigenomic landscape profiling compared to the tradditional ChIP-seq technology and known for its easy implementation and low cost.  The procedure is carried out in situ where micrococcal nuclease tethered to protein A binds to an antibody of choice and cuts immediately adjacent DNA, releasing DNA-bound to the antibody target. Therefore, CUT&RUN produces precise transcription factor or histone modification profiles while avoiding crosslinking and solubilization issues. Extremely low backgrounds make profiling possible with typically one-tenth of the sequencing depth required for ChIP-seq and permit profiling using low cell numbers (i.e., a few hundred cells) without losing quality.
+**Cleavage Under Targets and Release Using Nuclease**, **CUT&RUN** for short, is an antibody-targeted chromatin profiling method to measure the histone modification enrichment or transcription factor binding. This is a more advanced technology for epigenomic landscape profiling compared to the traditional ChIP-seq technology and known for its easy implementation and low cost.  The procedure is carried out in situ where micrococcal nuclease tethered to protein A binds to an antibody of choice and cuts immediately adjacent DNA, releasing DNA-bound to the antibody target. Therefore, CUT&RUN produces precise transcription factor or histone modification profiles while avoiding crosslinking and solubilization issues. Extremely low backgrounds make profiling possible with typically one-tenth of the sequencing depth required for ChIP-seq and permit profiling using low cell numbers (i.e., a few hundred cells) without losing quality.
 
 <!-- [Henikoff lab](https://research.fredhutch.org/henikoff/en.html) constructed a 6xHis and HA-tagged protein A-protein G-MNase fusion (pAG-MNase) that allows direct binding of mouse antibodies that bind poorly to protein A, eliminating the need for a secondary antibody. The His tag allows the purification of pAG-MNase with a commercial kit, while the HA tag can be used for pulling out pAG-MNase chromatin complexes for CUT&RUN.ChIP. Henikoff lab developed low salt and high calcium conditions that prevent diffusion of released complexes into the supernatant, allowing for longer digestion times and increased yields without increased cleavage at non-specific accessible sites. E. coli DNA carried over from pA-MNase or pAG-MNase preparation is sufficient for internal calibration of samples without adding heterologous spike-in DNA. -->