Software to perform genome-wide association study of SNP effects on trait varianceAlevin-fry is a fast,
accurate, and memory-frugal tool for preprocessing single-cell and single-nucleus
@@ -15901,11 +15902,11 @@ DeepLearnPhysics/playground-singularity:
data_format: 2
description: A repository with simple singularity recipes for tutorial purpose
filenames:
- - Singularity.ub16.04-step3
- - Singularity.ub16.04-step0
- Singularity.ub16.04-step2
- - Singularity.ub16.04-step1
- Singularity.ub16.04-step4
+ - Singularity.ub16.04-step3
+ - Singularity.ub16.04-step1
+ - Singularity.ub16.04-step0
full_name: DeepLearnPhysics/playground-singularity
latest_release: null
readme: ' http://mikado.readthedocs.org/ Mikado can be installed from\
- \ PyPI with pip: Alternatively,\
- \ you can clone the repository from source and install with: You can verify\
- \ the correctness of the installation with the unit tests (outside of the\
- \ source folder, as otherwise Python will get confused and try to use the\
- \ An alternative way of installing using The steps above will ensure\
- \ that any additional python dependencies will be installed correctly. A full\
- \ list of library dependencies can be found in the file Mikado by itself does require only the presence of a database solution, such\
- \ as SQLite (although we do support MySQL and PostGRESQL as well).\nHowever, the\
- \ Daijin pipeline requires additional programs to run. For driving Mikado\
- \ through Daijin, the following programs are required: Using mamba download\
+ \ mamba using pip Create a mamba environment using the environment.yml\
+ \ file Check and run mikado Mikado can also be be installed from PyPI with pip (deprecated): Alternatively, you can clone the repository\
+ \ from source and install with: You can verify the correctness of the installation\
+ \ with the unit tests (outside of the source folder, as otherwise Python\
+ \ will get confused and try to use the An alternative way of installing\
+ \ using The steps above will ensure that any additional python dependencies will be\
+ \ installed correctly. A full list of library dependencies can be found in the\
+ \ file Mikado by itself does require only the presence of a database\
+ \ solution, such as SQLite (although we do support MySQL and PostGRESQL as well).\n\
+ However, the Daijin pipeline requires additional programs to run. For\
+ \ driving Mikado through Daijin, the following programs are required:Usefulaf
is an all-in-one Docker/Singularity image for single-cell processing with Alevin-fry(
- Usefulaf
- history
+ Usefulaf
+ history
Installation
\npip3 install mikado
\npip\
- \ wheel -w dist .\npip install dist/*whl \nMikado source folder instead of the system installation):
\npython -c \"import Mikado; Mikado.test(); Mikado.test(label='slow')\"\
- \nsetuptools:
\npip install -r requirements.txt\npip install Cython\npython setup.py\
- \ bdist_wheel\npip install dist/*whl\nrequirements.txtAdditional dependencies
\n\
- \n
\npip\
+ \ install mamba=0.27.0
\nmamba env create\
+ \ -f environment.yml\nconda activate mikado2
\n\
+ mikado --help
pip3 install mikado
\npip wheel -w dist .\npip install\
+ \ dist/*whl\nMikado source folder instead\
+ \ of the system installation):
\npython -c \"import Mikado; Mikado.test();\
+ \ Mikado.test(label='slow')\"\nsetuptools:
\n\
+ pip install -r requirements.txt\n\
+ pip install Cython\npython setup.py bdist_wheel\npip install dist/*whl\nrequirements.txtAdditional\
+ \ dependencies
\n\n\
+
Software to perform genome-wide association study of SNP effects on trait variance
Full documentation available from https://mrcieu.github.io/varGWAS
-R-package also available from https://github.com/MRCIEU/varGWASR
-Lyon M, Millard L, Davey Smith G, Gaunt T, Tilling K. Hypothesis-free detection of gene-interaction effects on biomarker concentration in UK Biobank using variance @@ -38984,7 +38992,7 @@ MRCIEU/varGWAS: rel="nofollow">https://doi.org/10.1101/2022.01.05.21268406
' - stargazers_count: 2 + stargazers_count: 3 subscribers_count: 7 topics: - gwas @@ -38992,7 +39000,7 @@ MRCIEU/varGWAS: - heteroscedasticity - heteroskedasticity - variability - updated_at: 1675690783.0 + updated_at: 1695043730.0 MRChemSoft/mrchem: data_format: 2 description: MultiResolution Chemistry @@ -39383,10 +39391,10 @@ MRtrix3/mrtrix3: octicon octicon-link\">Contributing\nThank you for your interest\ \ in contributing to MRtrix3! Please read on here to find out how to report issues, request features and make direct contributions.
\n" - stargazers_count: 259 + stargazers_count: 260 subscribers_count: 31 topics: [] - updated_at: 1702256913.0 + updated_at: 1703007415.0 MacIver-Lab/Ergodic-Information-Harvesting: data_format: 2 description: 'Code, data, and tutorials for "Sense organ control in moths to moles @@ -42245,31 +42253,33 @@ Mykrobe-tools/mykrobe: - Singularity.def full_name: Mykrobe-tools/mykrobe latest_release: v0.13.0 - readme: "Antibiotic resistance prediction in minutes.
\nCurrently supports Mycobacterium\ - \ tuberculosis, Staphylococcus aureus, Shigella sonnei,\ - \ Salmonella typhi.
\n\nAntibiotic resistance prediction\ + \ in minutes.
\nCurrently supports Mycobacterium tuberculosis,\ + \ Staphylococcus aureus, Shigella sonnei, Salmonella typhi.
\n\ + \n\ +Please see the mykrobe wiki for documentation.\n\ For S. typhi also see https://github.com/katholt/genotyphi
\nInstall:
\n\ -conda install -c bioconda mykrobe\npip3 install . && mykrobe panels update_metadata &&\
- \ mykrobe panels update_species all\nRun on Mtb, making\ - \ a JSON file of results:
\nmykrobe predict --sample my_sample_name\
- \ \\\n --species tb \\\n --output out.json \\\n --format json \\\n --seq reads.fq.gz\n\
- Test reads can be obtained by running:
\nwget\
- \ -O reads.fq.gz https://ndownloader.figshare.com/files/21059229\nInstall:
\nconda install -c\
+ \ bioconda mykrobe\npip3 install . &&\
+ \ mykrobe panels update_metadata && mykrobe panels update_species all\n\
+ Run on Mtb, making a JSON file of results:
\nmykrobe\
+ \ predict --sample my_sample_name \\\n --species tb \\\n --output out.json \\\
+ \n --format json \\\n --seq reads.fq.gz\nTest reads can be\ + \ obtained by running:
\nwget -O reads.fq.gz https://ndownloader.figshare.com/files/21059229\n\
+ This repo provides a container for OpenStudio\ - \ as well as several dependencies, including Ruby 2.x, Bundler,\nbuild-essentials\ - \ and various development libraries for gem support.
\nAt the moment, there\ - \ is only one target container for this project, however, we are working on containers\ - \ for a\nslim version and a version compatible with Singularity.
\nBelow is a table of\ - \ the various docker tags and their meanings as seen on OpenStudio\n
![\"openstudio-docker\"](\"https://github.com/NREL/docker-openstudio/actions/workflows/docker-openstudio.yml/badge.svg\"\){kind=icon}
This repo\ + \ provides a container for OpenStudio as well as several dependencies, including\ + \ Ruby 2.x, Bundler,\nbuild-essentials and various development libraries for gem\ + \ support.
\nAt the moment, there is only one target container for this\ + \ project, however, we are working on containers for a\nslim version and a version\ + \ compatible with Singularity.
\n\ +Below is a table of the various docker tags and\ + \ their meanings as seen on this page.
\n| Tag | \n\Description | \n
|---|---|
| x.y.z | \nBuild\ \ of official OpenStudio release (recommended use) | \n
| latest | \n\Latest official release of OpenStudio (e.g. 2.5.1) | \n
| develop | \n\Release of develop branch\n | \n
These images are automatically built in TravisCI. To trigger TravisCI for a\ - \ new build do the following:
\nOn develop branch update the\ - \ .travis.yml with the new version of OpenStudio and\ - \ SHA
\n - OPENSTUDIO_VERSION: 2.6.0\n - OPENSTUDIO_SHA: e3cb91f98a Push changes to feature branch, make and merge a pull-request\ - \ to develop
\nWait for CI to finish and verify new develop\ - \ image is available on develop branch\n\n\n\n\n
These images are automatically built in TravisCI.\ + \ To trigger TravisCI for a new build do the following:
\nOn\ + \ develop branch update the .travis.yml with the new\ + \ version of OpenStudio and SHA
\n - OPENSTUDIO_VERSION: 2.6.0\n - OPENSTUDIO_SHA: e3cb91f98aPush changes to the feature\ + \ branch, make and merge a pull request to develop
\nWait for\ + \ CI to finish and verify new develop image is available on docker hub.
\nTest locally (if needed)
\n\docker pull nrel/openstudio:develop\n\
docker run -it --rm nrel/openstudio:develop bash\nirb\nrequire 'openstudio'\n\
- puts OpenStudio.getOpenStudioCLIBegin by installing\ - \ the docker tool-kit version 17.03.1 or later, as\ndescribed in the linked documentation.\ - \ Once the tool-kit is installed and activated, run the command below to build\ - \ the base image with OpenStudio 2.6.1.
\ndocker build --target\
- \ base -t openstudio-local --build-arg OPENSTUDIO_VERSION=2.6.1 --build-arg OPENSTUDIO_SHA=ab0dddde0b\
- \ .\nThe version of OpenStudio and the SHAs are listed \n
Begin by installing the docker toolkit version 17.03.1 or later, as\ndescribed\ + \ in the linked documentation. Once the toolkit is installed and activated, run\ + \ the command below to build the base image with OpenStudio 2.6.1.
\ndocker\
+ \ build --target base -t openstudio-local --build-arg OPENSTUDIO_VERSION=2.6.1\
+ \ --build-arg OPENSTUDIO_SHA=ab0dddde0b .\nThe version of OpenStudio\ + \ and the SHAs are listed here.
\nIf the --target is not passed, then the docker\
- \ build will contain only the CLI.
There are two options to acquire the docker container required\
- \ for execution. Both assume that the\n\n There are two options to acquire the docker container\
+ \ required for execution. Both assume that the\ndocker tool-kit version 17.03.1 or later is installed.\
\ The first option,\nbuilding the container from a GitHub checkout, is outlined\
\ above. Additionally, it is typically easiest to\n If gem\
\ dependencies are required as part of the CLI outside of those\npacked with OpenStudio please contact\nKyle Benne, Ry Horsey, and Dan Macumber\
- \ at their NREL email addresses. Please submit issues on the project's Executing\
+ \ OpenStudio Container
\nIssues
\n
Please submit\ + \ issues on the project's Github page.
\n" stargazers_count: 20 subscribers_count: 14 topics: [] @@ -45432,7 +45443,7 @@ NVIDIA/hpc-container-maker: - hpc - docker - singularity - updated_at: 1701713419.0 + updated_at: 1703043033.0 NYU-Molecular-Pathology/LG-PACT: data_format: 2 description: NYU Langone Genome PACT (Genome Profiling of Actionable Cancer Targets) @@ -49041,42 +49052,43 @@ Oulu-IMEDS/DeepWrist: - Singularity full_name: Oulu-IMEDS/DeepWrist latest_release: null - readme: "Title: Deep Learning for Wrist Fracture Detection: Are We There Yet?
\n\
- arXiv link
A transfer learning pipeline to detect wrist fracture from DICOM files. It\ - \ has two blocks: Landmark Localization Block\nand Fracture Detection Block.\n\ - Paper Link\n
Title: Deep Learning for Wrist\
+ \ Fracture Detection: Are We There Yet?
\narXiv link
A transfer learning pipeline to detect wrist fracture from\
+ \ DICOM files. It has two blocks: Landmark Localization Block\nand Fracture Detection\
+ \ Block.\n![\"DeepWrist\"](\"./figures/DeepWrist_pipeline.png\"\)
Both of the blocks are yml configuration based. We used\
\ OmegaConf for this purpose. Each executable python file can\neither run standalone\
\ or requires a yml file as experiment argument to be passed down\
- \ at command line.
Landmark Localization Block is adapted from KNEEL. However, we developed some data\naugmentation methods\
- \ suited to our task. The localizer folder contains the source code\
- \ for Landmark Localizer and\nstructured as
localizer \n\
- |---config \n| |---experiment\n|---kneel_before_wrist \n| |---data \n| |---model\
- \ \n|---scripts \nThe config folder contains the\
- \ initial default configuration and a configuration processor. There is a folder\
- \ named\nexperiment inside config folder which holds\
- \ confgiguration for different experiments.
kneel_before_wrist\
+ \ at command line.
Landmark Localization Block is adapted from KNEEL. However, we developed\
+ \ some data\naugmentation methods suited to our task. The localizer\
+ \ folder contains the source code for Landmark Localizer and\nstructured as
localizer \n|---config \n| |---experiment\n|---kneel_before_wrist\
+ \ \n| |---data \n| |---model \n|---scripts \nThe config\
+ \ folder contains the initial default configuration and a configuration processor.\
+ \ There is a folder named\nexperiment inside config\
+ \ folder which holds confgiguration for different experiments.
kneel_before_wrist\
\ hosts the body of the localizer part of our pipeline. It has two sub-directory:\
\ data and model.\nThe data folder contains\
\ utilities necessary to process and augment data for training and evaluation.\
\ model\nsub-directory contains the pytorch lightning version of\
\ HourGlass network which we will use for training the localizer.
The\
\ scripts directory hosts all the experiment scripts for which the\
- \ yaml configurations are created.
First step for training\ - \ your own localizer is to collect data. For our research we used private hospital\ + \ yaml configurations are created.
\nFirst step for training your\ + \ own localizer is to collect data. For our research we used private hospital\ \ data,\nthrefore it cannot be shared. To make things simple, we used csv file\ \ to store meta data about the dataset. This way,\nyou don't have to load the\ \ full dataset to the memory rather fecth the file location from csv file and\ @@ -49097,22 +49109,23 @@ Oulu-IMEDS/DeepWrist: \ created. should be inside data folder \n\n
train_ptl.py --experiment=YourClonedYAMLFile.\
\ This file is located inside\nscripts folder. It will start the\
- \ training.The\
- \ classifier folder hosts the Fracture Detection Block. It has a\
- \ similar structure like localizer.
classifier \n|---config \n\
- |---fracture_detector\n| |---callback \n| |---data \n| |---model \n|---script\
- \ \n\nLike before config folder hosts the script\
- \ configurations. fracture_detector folder hosts necessary folders\
- \ and\nfiles for model, data and training related stuffs. Inside this folder,\
- \ there are three folders: 1) callback (hosts\ncallback function\
- \ definitions), 2) data (hosts data related utilities) and 3) model\
- \ (hosts model definition and\ntraining methods)
Step 1. First step to train your custom fracture detector\
- \ is to collect data using the localizer model trained previously.\n\
+ \ training.
The classifier folder hosts the Fracture\
+ \ Detection Block. It has a similar structure like localizer.
classifier\
+ \ \n|---config \n|---fracture_detector\n| |---callback \n| |---data \n| \
+ \ |---model \n|---script \n\nLike before config\
+ \ folder hosts the script configurations. fracture_detector folder\
+ \ hosts necessary folders and\nfiles for model, data and training related stuffs.\
+ \ Inside this folder, there are three folders: 1) callback (hosts\n\
+ callback function definitions), 2) data (hosts data related utilities)\
+ \ and 3) model (hosts model definition and\ntraining methods)
Step 1. First step to train your custom fracture\
+ \ detector is to collect data using the localizer model trained previously.\n\
Save the generated ROI with the corresponding ID as filename. Create\
\ a csv metadata file with ID, Side, Fname(optional)\n\
and Fracture columns. Say, the meta file name is your_meta.csvCreate\
@@ -49124,41 +49137,42 @@ Oulu-IMEDS/DeepWrist:
\ and update the following field
data_home: root\nmeta: your_meta.csv\n\
Step 3. Once you are done with the config file go inside the\
\ scripts folder and run python train_ptl.py experiment=your_config_file.\n\
- this will start the training.
Step 1. Create a csv meta file of for the data you want to predict. Use ID,\
- \ Side, Fname, and Fracture columns.
Step2. Clone fracture_deteciton_testset_1.yaml to your_testset.yaml
Step 3. Find and update the following field in your_testset.yaml
dataset:\n train_data_home: root\n test_data_home: /location/of/test/data\n\
- \ meta: /absolute/location/to/your_testset.csv\nsave_path: /absolute/location/to/save/prediction.csv\n\
- snapshot_folder: /folder/location/where/fracture/detector/models/are/saved\nsave_image:\
- \ true or false\nsave_image_dir: /folder/location/if/you/want/to/save/output/images\n\
+ this will start the training.\nInference\
+ \ on your Data
\nStep 1. Create a csv meta file of for the data you want\
+ \ to predict. Use ID, Side, Fname, and\
+ \ Fracture columns.
\nStep2. Clone fracture_deteciton_testset_1.yaml\
+ \ to your_testset.yaml
\nStep 3. Find and update the following\
+ \ field in your_testset.yaml
\ndataset:\n train_data_home:\
+ \ root\n test_data_home: /location/of/test/data\n meta: /absolute/location/to/your_testset.csv\n\
+ save_path: /absolute/location/to/save/prediction.csv\nsnapshot_folder: /folder/location/where/fracture/detector/models/are/saved\n\
+ save_image: true or false\nsave_image_dir: /folder/location/if/you/want/to/save/output/images\n\
\nlocalizer:\n snapshot_folder: /folder/location/where/roi/localizer/models/are/saved\n\
\ dataset:\n train_data_home: /folder/location/where/localization/data/are/stored\n\
\nkeep only Fracture in gt. If you want\
\ to save gradcam set save_gradcam: true and define gradcam_dir
\n\
Step 4. Now in the scripts folder run, python test.py experiment=your_testset\n\
This will do inference on your data, the predicitons will be saved in the csv\
- \ file you defined.
\n\nTrained Models
\nUse the following commands\
- \ to get the trained models.
\nwget http://mipt-ml.oulu.fi/models/DeepWrist/Fracture_Detection_Block.tar.gz\n\
+ \ file you defined.\n\nTrained\
+ \ Models
\nUse the following commands to get the trained models.
\n\
+ wget http://mipt-ml.oulu.fi/models/DeepWrist/Fracture_Detection_Block.tar.gz\n\
wget http://mipt-ml.oulu.fi/models/DeepWrist/ROI_Localization_Block.tar.gz\n
\n\
- How to Cite
\n\
- For citation, please use the following bibtex
\n@misc{raisuddin2020deep,\n\
- \ title={Deep Learning for Wrist Fracture Detection: Are We There Yet?},\
- \ \n author={Abu Mohammed Raisuddin and Elias Vaattovaara and Mika Nevalainen\
- \ and Marko Nikki and Elina J\xE4rvenp\xE4\xE4 and Kaisa Makkonen and Pekka Pinola\
- \ and Tuula Palsio and Arttu Niemensivu and Osmo Tervonen and Aleksei Tiulpin},\n\
- \ year={2020},\n eprint={2012.02577},\n archivePrefix={arXiv},\n\
- \ primaryClass={cs.CV}\n}\n
\n"
- stargazers_count: 7
+ How to Cite
\nFor citation, please use the following bibtex
\n\
+ @misc{raisuddin2020deep,\n title={Deep Learning for Wrist Fracture\
+ \ Detection: Are We There Yet?}, \n author={Abu Mohammed Raisuddin and Elias\
+ \ Vaattovaara and Mika Nevalainen and Marko Nikki and Elina J\xE4rvenp\xE4\xE4\
+ \ and Kaisa Makkonen and Pekka Pinola and Tuula Palsio and Arttu Niemensivu and\
+ \ Osmo Tervonen and Aleksei Tiulpin},\n year={2020},\n eprint={2012.02577},\n\
+ \ archivePrefix={arXiv},\n primaryClass={cs.CV}\n}\n
\n"
+ stargazers_count: 8
subscribers_count: 5
topics: []
- updated_at: 1685602432.0
+ updated_at: 1701003742.0
OxfordSKA/OSKAR:
data_format: 2
description: SKA Radio Telescope Simulator
@@ -51692,10 +51706,10 @@ QTIM-Lab/DeepNeuro:
octicon octicon-link\">Disclaimer\nThis software package and\
\ the deep learning models within are intended for research purposes only and\
\ have not yet been validated for clinical use.
\n"
- stargazers_count: 117
+ stargazers_count: 118
subscribers_count: 14
topics: []
- updated_at: 1701253473.0
+ updated_at: 1703042625.0
QsingularityAi/polar-pfc-master_active-crystel:
data_format: 2
description: null
@@ -51869,14 +51883,14 @@ RBigData/singularity:
data_format: 2
description: Singularity configurations for R and pbdR packages.
filenames:
- - pbdR/pbdR/mpich/Singularity.1.0-1
- pbdR/pbdR/openmpi/Singularity.1.0-1
- - pbdR/pbdR-minimal/mpich/Singularity.1.0-1
+ - pbdR/pbdR/mpich/Singularity.1.0-1
- pbdR/pbdR-minimal/openmpi/Singularity.1.0-1
+ - pbdR/pbdR-minimal/mpich/Singularity.1.0-1
- R/r-minimal/Singularity.3.5.1
- - R/rstudio-server/Singularity.1.1.456
- R/jupyter/Singularity
- R/rstudio/Singularity.1.1.456
+ - R/rstudio-server/Singularity.1.1.456
- R/r/Singularity.3.5.1
full_name: RBigData/singularity
latest_release: null
@@ -59807,8 +59821,8 @@ UPPMAX/offline-uppmax-env:
data_format: 2
description: A container that looks like uppmax but can be run completely offline.
filenames:
- - Singularity.ngsintro
- Singularity.default
+ - Singularity.ngsintro
full_name: UPPMAX/offline-uppmax-env
latest_release: null
readme: '
'
- stargazers_count: 8341
- subscribers_count: 349
+ stargazers_count: 8346
+ subscribers_count: 350
topics:
- c-plus-plus
- machine-learning
@@ -62171,7 +62185,7 @@ VowpalWabbit/vowpal_wabbit:
- active-learning
- learning-to-search
- cpp
- updated_at: 1702776911.0
+ updated_at: 1703339445.0
WIPACrepo/monitoring-scripts:
data_format: 2
description: Tools for monitoring HTCondor and other things
@@ -63656,9 +63670,10 @@ ZisongXu/trackObjectWithPF:
- Singularity
full_name: ZisongXu/trackObjectWithPF
latest_release: null
- readme: 'Physics Based Particle Filtering
+ readme: 'Physics Based Particle
+ Filtering
This is the official implementation of our paper "Real-Time Physics-Based Object
Pose Tracking during Non-Prehensile Manipulation".
@@ -63678,8 +63693,9 @@ ZisongXu/trackObjectWithPF:
the computational cost, resulting in more accurate tracking, and estimating object
pose even when the object is not clearly visible to the camera.
- Supplementary
- Video:
+ Supplementary Video:
Click to watch the video.
@@ -63687,16 +63703,18 @@ ZisongXu/trackObjectWithPF:
alt="Watch the video" data-canonical-src="https://i.ytimg.com/vi/EMBFYzkno64/maxresdefault.jpg"
style="max-width: 100%;">
- Brief
- Description:
+ Brief
+ Description:
We propose a method to track the pose of an object over time, by using the
image from the camera, and the particles in the physical engine. Although sometimes
the camera cannot see the object clearly, our method can still track the pose
of the object.
- Quick
- Setup:
+ Quick
+ Setup:
@@ -63722,8 +63740,9 @@ ZisongXu/trackObjectWithPF:
- Running
- Code
+ Running
+ Code
@@ -63854,8 +63873,8 @@ ZisongXu/trackObjectWithPF:
- Data
+ Data
All experimental data and figures of the results are placed in the ~/data/.
All scenes of rosbags can be downloaded through the link blow: license file.
'
- stargazers_count: 2476
- subscribers_count: 91
+ stargazers_count: 2477
+ subscribers_count: 90
topics:
- containers
- container
@@ -70002,7 +70021,7 @@ apptainer/singularity:
- portability
- rootless-containers
- cloud-native
- updated_at: 1702753123.0
+ updated_at: 1703163474.0
ar90n/lab:
data_format: 2
description: my laboratory
@@ -71824,7 +71843,7 @@ automl/HPOBench:
pl-pds\">},\n url={https://openreview.net/forum?id=1k4rJYEwda-} \n}
\n"
- stargazers_count: 119
+ stargazers_count: 120
subscribers_count: 12
topics:
- hyperparameter-optimization
@@ -71832,7 +71851,7 @@ automl/HPOBench:
- bayesian-optimization
- benchmark
- containerized-benchmarks
- updated_at: 1702168149.0
+ updated_at: 1703044085.0
ay-lab/FitHiChIP:
data_format: 2
description: Statistically Significant loops from HiChIP data
@@ -75072,7 +75091,7 @@ bcgsc/orca:
'
stargazers_count: 47
- subscribers_count: 10
+ subscribers_count: 11
topics: []
updated_at: 1688713744.0
bchaselab/DeepLabCut-HPC:
@@ -75991,9 +76010,9 @@ bhattlab/lathe:
description: A tool for generating bacterial genomes from metagenomes with nanopore
long read sequencing
filenames:
+ - singularity/Singularity.quickmerge
- singularity/Singularity.longread
- singularity/Singularity.htsbox
- - singularity/Singularity.quickmerge
full_name: bhattlab/lathe
latest_release: null
readme: "
Train model (Training takes around 1-2 hours using GTX 3060)
\nsingularity exec\
\ --nv main.sif python sastvd/scripts/train_best.pyvendor/README.md\
\ lists the repository for each grammar.\nAll other files are covered by\
\ the MIT license, see LICENSE.
![\"nfcore/rnaseq\"](\"misc/images/fast-downward.svg\"\)
![\"Build](\"https://travis-ci.org/nf-core/rnaseq.svg?branch=master\"\)
nfcore/rnaseq\ - \ is a bioinformatics analysis pipeline used for RNA sequencing data.
\nThe\ - \ workflow processes raw data from FastQ inputs (FastQC,
\nnfcore/rnaseq is a bioinformatics analysis pipeline used for\ + \ RNA sequencing data.
\nThe workflow processes raw data from FastQ inputs\ + \ (FastQC, Trim Galore!), aligns the reads (STAR or HiSAT2), generates gene counts (Nextflow, a bioinformatics workflow\ \ tool to run tasks across multiple compute infrastructures in a very portable\ \ manner. It comes with docker / singularity containers making installation trivial\ - \ and results highly reproducible.
\nThe nfcore/rnaseq pipeline\
- \ comes with documentation about the pipeline, found in the docs/\
- \ directory:
These\ - \ scripts were originally written for use at the Troubleshooting
These scripts were originally written for use at the National Genomics Infrastructure, part of SciLifeLab in Stockholm, Sweden, by Phil Ewels (@ewels) and Rickard Hammar\xE9n (@orzechoj,\n@apeltzer,\n@colindaven.
\n" stargazers_count: 1 - subscribers_count: 31 + subscribers_count: 29 topics: [] updated_at: 1552290431.0 lifebit-ai/roary: @@ -130061,10 +130083,10 @@ litebird/litebird_sim: \ class=\"anchor\" aria-hidden=\"true\" tabindex=\"-1\" href=\"#how-to-cite-this-code\"\ >How to cite\ \ this code\nTODO!
\n\n\n\n" - stargazers_count: 15 + stargazers_count: 16 subscribers_count: 18 topics: [] - updated_at: 1701765435.0 + updated_at: 1702532805.0 lixuekai2001/brain-inversion: data_format: 2 description: null @@ -130509,11 +130531,11 @@ lscsoft/bilby_pipe: data_format: 2 description: null filenames: - - containers/Singularity.dev - containers/Singularity.0.0.4 - - containers/Singularity.0.0.2 - - containers/Singularity.0.0.3 - containers/Singularity.0.0.1 + - containers/Singularity.dev + - containers/Singularity.0.0.3 + - containers/Singularity.0.0.2 full_name: lscsoft/bilby_pipe latest_release: null stargazers_count: 6 @@ -131643,7 +131665,8 @@ manuel-munoz-aguirre/singularity-pytorch-gpu: full_name: manuel-munoz-aguirre/singularity-pytorch-gpu latest_release: null readme: '
Installing
- singularity
+ tabindex="-1" href="#installing-singularity">Installing singularity
To install singularity, see the official docs.
To build an image called torchenv.sif based on the definition
@@ -131690,8 +131713,8 @@ manuel-munoz-aguirre/singularity-pytorch-gpu:
To spawn an interactive shell within the container, use the command below.
The --nv flag setups the container to use NVIDIA GPUs (read more
@@ -131712,7 +131735,7 @@ manuel-munoz-aguirre/singularity-pytorch-gpu:
upon startup it will display a warning about missing NVIDIA binaries on the host.
![\"napari](\"https://mybinder.org/badge_logo.svg\"\){kind=icon}
We're working on tutorials, but you can also\ - \ quickly get started by looking below.
\nIt is recommended to\ - \ install napari into a virtual environment, like this:
\n![\"Singularity](\"https://img.shields.io/badge/singularity-available-7E4C74.svg\"\){kind=icon}
conda create -y -n napari-env -c conda-forge python=3.9\n\
- conda activate napari-env\npython -m pip install \"napari[all]\"If you prefer conda over pip, you can replace the last line with: conda\
- \ install -c conda-forge napari pyqt
See here for the full installation guide.
\n(The examples below require\
- \ the scikit-image package to run. We just use data samples from\
- \ this package for demonstration purposes. If you change the examples to use your\
- \ own dataset, you may not need to install this package.)
From inside\ - \ an IPython shell, you can open up an interactive viewer by calling
\nfrom\
+ \ quickly get started by looking below.\ninstallation
\n\
+ It is recommended to install napari into a virtual environment, like this:
\n\
+ conda create -y -n napari-env\
+ \ -c conda-forge python=3.9\nconda activate napari-env\npython -m pip install\
+ \ \"napari[all]\"\nIf you prefer conda over pip, you can\
+ \ replace the last line with: conda install -c conda-forge napari pyqt
\n\
+ See here for the full installation guide.
\nsimple example
\n\
+ (The examples below require the scikit-image package to run. We\
+ \ just use data samples from this package for demonstration purposes. If you change\
+ \ the examples to use your own dataset, you may not need to install this package.)
\n\
+ From inside an IPython shell, you can open up an interactive viewer by calling
\n\
+ from\
\ skimage import data\nimport napari\n\nviewer 1, ndisplay=3)\nnapari.run() # start the \"event loop\" and show\
- \ the viewer
\nfeatures
\nCheck out the scripts in our examples folder to see some of the functionality we're\
- \ developing!
\nnapari supports six main different layer\
- \ types, Image, Labels, Points, Vectors,\
- \ Shapes, and Surface, each corresponding to a different\
- \ data type, visualization, and interactivity. You can add multiple layers of\
- \ different types into the viewer and then start working with them, adjusting\
- \ their properties.
\nAll our layer types support n-dimensional data and\
- \ the viewer provides the ability to quickly browse and visualize either 2D or\
- \ 3D slices of the data.
\nnapari also supports bidirectional\
- \ communication between the viewer and the Python kernel, which is especially\
- \ useful when launching from jupyter notebooks or when using our built-in console.\
- \ Using the console allows you to interactively load and save data from the viewer\
- \ and control all the features of the viewer programmatically.
\nYou can\
- \ extend napari using custom shortcuts, key bindings, and mouse\
- \ functions.
\ntutorials
\nFor more details on how to use napari checkout\
- \ our tutorials.\
- \ These are still a work in progress, but we'll be updating them regularly.
\n\
- mission, values, and roadmap
\nFor more information\
- \ about our plans for napari you can read our mission and values statement, which includes more details\
- \ on our vision for supporting a plugin ecosystem around napari.\nYou can see\
- \ details of the project roadmap here.
\ncontributing
\nContributions are encouraged! Please\
- \ read our contributing guide to get started. Given that we're in an early\
- \ stage, you may want to reach out on our
\nCheck out the\
+ \ scripts in our examples folder to see\
+ \ some of the functionality we're developing!
napari\
+ \ supports six main different layer types, Image, Labels,\
+ \ Points, Vectors, Shapes, and Surface,\
+ \ each corresponding to a different data type, visualization, and interactivity.\
+ \ You can add multiple layers of different types into the viewer and then start\
+ \ working with them, adjusting their properties.
All our layer types support\ + \ n-dimensional data and the viewer provides the ability to quickly browse and\ + \ visualize either 2D or 3D slices of the data.
\nnapari\ + \ also supports bidirectional communication between the viewer and the Python\ + \ kernel, which is especially useful when launching from jupyter notebooks or\ + \ when using our built-in console. Using the console allows you to interactively\ + \ load and save data from the viewer and control all the features of the viewer\ + \ programmatically.
\nYou can extend napari using custom\ + \ shortcuts, key bindings, and mouse functions.
\nFor more details on how to use napari checkout our tutorials. These are still\
+ \ a work in progress, but we'll be updating them regularly.
For more information about our plans for napari you can read our\
+ \ mission and values statement, which includes more details on our vision for\
+ \ supporting a plugin ecosystem around napari.\nYou can see details of the project roadmap\
+ \ here.
Contributions are encouraged!\ + \ Please read our contributing guide to get started. Given that we're in\ + \ an early stage, you may want to reach out on our Github Issues before jumping in.
\nIf you want to contribute or edit\ \ to our documentation, please go to napari/docs.
\nnapari has a Code of Conduct\
- \ that should be honored by everyone who participates in the napari\
- \ community.
You can learn more about how the napari project\
- \ is organized and managed from our governance model, which includes information about, and\
- \ ways to contact the @napari/steering-council and @napari/core-devs.
If you find napari useful please cite this repository using its DOI as follows:
![\"CZI](\"https://chanzuckerberg.com/wp-content/themes/czi/img/logo.svg\"\){kind=logo}
Old releases are available at ![\"0_0\"](\"data/example_images/0_0.png\")
![\"0_2\"](\"data/example_images/0_2.png\")
![\"1_0\"](\"data/example_images/1_0.png\"\)
![\"1_1\"](\"data/example_images/1_1.png\"\)
![\"1_2\"](\"data/example_images/1_2.png\"\)
![\"0_0\"](\"data/example_images/0_1_depth.png\")
![\"0_1\"](\"data/example_images/0_2_depth.png\")
![\"0_2\"](\"data/example_images/0_3_depth.png\")
![\"1_1\"](\"data/example_images/0_2_correspondence.png\"\)
![\"1_2\"](\"data/example_images/0_3_correspondence.png\"\)
![\"0_0\"](\"data/example_images/0_1_region_mask.png\"\)
![\"0_1\"](\"data/example_images/0_2_region_mask.png\")
![\"0_2\"](\"data/example_images/0_3_region_mask.png\")
![\"0_1\"](\"data/example_images/0_2_region_mask_bfm09.png\"\)
![\"0_2\"](\"data/example_images/0_3_region_mask_bfm09.png\"\)