AtacWorks

AtacWorks is a deep learning toolkit for coverage track denoising and peak calling from low-coverage or low-quality ATAC-Seq data.

The ability to measure biologically-meaningful changes in accessible chromatin using ATAC-seq depends on both the signal-to-noise ratio and the depth of sequencing coverage. Technical parameters such as the overall quality of cells or tissues, the nuclei extraction method or over-digestion of chromatin can result in attenuated measurements of accessibility. Importantly, these issues are exacerbated in single-cell experiments, where primary tissues may vary in quality and key cell types may be exceedingly rare.

A well trained AtacWorks model can accurately predict both chromatin accessibility at base-pair resolution (a coverage track), and the genomic locations of accessible regulatory regions (peak calls). We apply AtacWorks to subsampled low-coverage bulk ATAC-seq and show that AtacWorks improves the resolution of the chromatin accessibility signal and the identification of regulatory elements. Further, AtacWorks is able to denoise signal from cell types not present in the training set, demonstrating that our deep learning models learn generalizable features of chromatin accessibility. Below is an image of a noisy track (Blue), a clean track (Black) and a noisy track denoised by AtacWorks (Green). As can be seen, AtacWorks successfully denoises the 1 million read track to approximate the quality of a 50 million read track.

Model Architecture

The model architecture of Atacworks is based on Residual Neural Networks. To train an AtacWorks model, you need a pair of ATAC-Seq datasets from the same cell type, where one dataset has lower coverage or lower quality than the other. The model learns to predict both the clean coverage track and the positions of peaks in the clean dataset from the noisy coverage track.

$\"Model$

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Clone repository\n\n#### Latest released version\nThis will clone the repo to the `master` branch, which contains code for latest released version\nand hot-fixes.\n\n```\ngit clone --recursive -b master https://github.com/clara-genomics/AtacWorks.git\n```\n#### Latest development version\nThis will clone the repo to the default branch, which is set to be the latest development branch.\nThis branch is subject to change frequently as features and bug fixes are pushed.\n\n```\ngit clone --recursive https://github.com/clara-genomics/AtacWorks.git\n```\n\n### 2. Install dependencies\n\n* Download `bedGraphToBigWig` and `bigWigToBedGraph` binaries and add $PATH to your bashrc.\n ```\n rsync -aP rsync://hgdownload.soe.ucsc.edu/genome/admin/exe/linux.x86_64/bedGraphToBigWig \n rsync -aP rsync://hgdownload.soe.ucsc.edu/genome/admin/exe/linux.x86_64/bigWigToBedGraph \n export PATH=\"$PATH: >> ~/.bashrc\"\n ```\n\n* Install pip dependencies\n\n ```\n cd AtacWorks && pip install -r requirements.txt\n ```\n* Optional -- Install macs2.\n Only required if you want to use macs2 subcommands to call peaks based on peak probabilities generated by AtacWorks.\n\n ```\n pip install macs2==2.2.4\n ```\n\n* Install atacworks\n\n ```\n pip install .\n ```","hasSetup","quickStartGuide","# AtacWorks Quick Start Guide\n\nUsing AtacWorks toolkit, one can either train a custom AtacWorks model with their own data or use one of already pre-trained AtacWorks models and run denoising on the data directly. \n\n## Training an AtacWorks Model\n\nTo train an AtacWorks model, you need a pair of ATAC-Seq datasets from the same cell type, where one dataset has lower coverage or lower quality than the other. You can also use multiple such pairs of datasets. For each such pair of datasets, AtacWorks requires three input files:\n\n1. A coverage track representing the number of sequencing reads mapped to each position on the genome in the low-coverage or low-quality dataset. This may be smoothed or processed. Format: bigWig\n\n2. A coverage track representing the number of sequencing reads mapped to each position on the genome in the high-coverage or high-quality dataset. This may be smoothed or processed in the same way as the previous track. Format: bigWig\n\n3. The genomic positions of peaks called on the high-coverage or high-quality dataset. These can be obtained by using MACS2 or any other peak caller. Format: either BED or the narrowPeak format produced by MACS2.\n\nThe model learns a mapping from (1) to both (2) and (3); in other words, from the noisy coverage track, it learns to predict both the clean coverage track, and the positions of peaks in the clean dataset.\n\nThe step by step guide for how to load and pre-process the data, run training commands and the expected outputs is described in a tutorial notebook. More information about training an atacworks model can be found [here.](https://clara-parabricks.github.io/AtacWorks/tutorials/tutorial1.html)\n\n## Denoising and Peak Calling using Pre-trained AtacWorks Model\n\nTo denoise and call peaks from low-coverage, low cell count, or low quality ATAC-seq data, you need three input files:\n\n1. A trained AtacWorks model file with extension .pth.tar.\n\n2. A coverage track representing the number of sequencing reads mapped to each position on the genome in the low-coverage or low-quality dataset. This track should be processed in the same way as the files used for training the model. Format: bigWig\n\n3. Chromosome sizes file - a tab-separated text file containing the names and sizes of chromosomes in the genome.\n\nThe step-by-step guide for how to pass the appropriate inputs, what outputs to expect from AtacWorks and how to interpret the results are discussed in a tutorial notebook. More information about denoising using pre-trained atacworks model can be found [here.](https://clara-parabricks.github.io/AtacWorks/tutorials/tutorial2.html#)","hasQuickStartGuide","advanced","## AtacWorks Paper\nLal, A., Chiang, Z.D., Yakovenko, N., Duarte, F.M., Israeli, J. and Buenrostro, J.D., 2019. AtacWorks: A deep convolutional neural network toolkit for epigenomics. BioRxiv, p.829481.\n\nLink: https://www.biorxiv.org/content/10.1101/829481v2\n\n## AtacWorks pre-trained models\nThe list of pre-trained models mentioned in the paper above are all hosted on [NGC.](https://ngc.nvidia.com/catalog/models?orderBy=modifiedDESC&pageNumber=0&query=atac&quickFilter=&filters=)\nThe one line command for downloading a model and all it's corresponding files can be found in `CLI Command` box in the catalog description when you click on the model.\n\nAll the details about how atac-seq data is conditioned before sending it as input to AtacWorks, all the different models trained and the data used for them etc, is documented [here.](https://clara-parabricks.github.io/AtacWorks/tutorials/pretrained_models.html)\n\n## AtacWorks Code\nAtacWorks is an open source project and we highly value collaborations and contributions from the community. If you'd like to contribute to the toolkit, [here's the github repository.](https://github.com/clara-parabricks/AtacWorks)\n\n### Contributing to AtacWorks\nThis section is only for developers of atacworks. If you would like to contribute to atacworks codebase,\ntake a look at the development guidelines [here](https://clara-parabricks.github.io/development.html#contributing-to-clara-omics).\nYou can read about our Continuous Integration (CI) test flow [here](https://clara-parabricks.github.io/development.html#ci-testing).\n\n### Running CI Tests Locally\nWhen a PR is submitted to the github, CI tests are triggered by github CI. If you would like to run those tests locally,\nfollow the instructions below.\n!!CAUTION!!\nPlease note, your git repository will be mounted to the container, any untracked files will be removed from it.\nBefore executing the CI locally, stash or add them to the index.\n\nRequirements:\n1. [docker](https://docs.docker.com/install/linux/docker-ce/ubuntu/)\n2. [nvidia-docker](https://github.com/NVIDIA/nvidia-docker)\n3. [nvidia-container-runtime](https://github.com/NVIDIA/nvidia-container-runtime)\n\nRun the following command to execute the CI build steps inside a container locally:\n\n```\nbash ci/local/build.sh -r \n```\nci/local/build.sh script was adapted from [rapidsai/cudf](https://github.com/rapidsai/cudf/tree/branch-0.11/ci/local)\nThe default docker image is **clara-genomics-base:cuda10.1-ubuntu16.04-gcc5-py3.6**.\n\nOther images from [gpuci/clara-genomics-base](https://hub.docker.com/r/gpuci/clara-genomics-base/tags) repository can be used instead, by using -i argument as shown below:\n\n```\nbash ci/local/build.sh -r -i gpuci/clara-genomics-base:cuda10.0-ubuntu18.04-gcc7-py3.6\n```","hasAdvanced","performance","hasPerformance","policies",[],{"_77":206,"_79":207,"_178":-5,"_185":31,"_187":31,"_201":205,"_189":31},{"_77":22,"_79":22,"_178":-5,"_185":31,"_187":31,"_201":205,"_189":31},[],"-","Latest (0.3)","nvidia/atacworks","key","nvidia/resources/atacworks","resourceType","resources","resourceId","atacworks","shortDescription","Deep learning toolkit for coverage track denoising and peak calling from low-coverage or low-quality ATAC-Seq data","# AtacWorks\n\nAtacWorks is a deep learning toolkit for coverage track denoising and peak calling from low-coverage or low-quality ATAC-Seq data.\n\nThe ability to measure biologically-meaningful changes in accessible chromatin using ATAC-seq depends on both the signal-to-noise ratio and the depth of sequencing coverage. Technical parameters such as the overall quality of cells or tissues, the nuclei extraction method or over-digestion of chromatin can result in attenuated measurements of accessibility. Importantly, these issues are exacerbated in single-cell experiments, where primary tissues may vary in quality and key cell types may be exceedingly rare. \n\nA well trained AtacWorks model can accurately predict both chromatin accessibility at base-pair resolution (a coverage track), and the genomic locations of accessible regulatory regions (peak calls). We apply AtacWorks to subsampled low-coverage bulk ATAC-seq and show that AtacWorks improves the resolution of the chromatin accessibility signal and the identification of regulatory elements. Further, AtacWorks is able to denoise signal from cell types not present in the training set, demonstrating that our deep learning models learn generalizable features of chromatin accessibility. Below is an image of a noisy track (Blue), a clean track (Black) and a noisy track denoised by AtacWorks (Green). As can be seen, AtacWorks successfully denoises the 1 million read track to approximate the quality of a 50 million read track. \n\n

\n\n## Model Architecture\n\nThe model architecture of Atacworks is based on Residual Neural Networks. To train an AtacWorks model, you need a pair of ATAC-Seq datasets from the same cell type, where one dataset has lower coverage or lower quality than the other. The model learns to predict both the clean coverage track and the positions of peaks in the clean dataset from the noisy coverage track.\n\n![Model Architecture](https://github.com/ntadimeti/AtacWorks/blob/ntadimeti/host_ngc_images/data/ngc/model_architecture.png?raw=true)","displayName","AtacWorks",["D",1601413138162],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/-/resources/atacworks","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Genomics.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[250,251,252,253,254,255],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_218":272,"_257":272,"_258":273,"_260":261},{"_218":219,"_257":219,"_258":271,"_260":261},{"_218":269,"_257":269,"_258":270,"_260":261},{"_218":267,"_257":267,"_258":268,"_260":261},{"_218":263,"_257":264,"_258":265,"_260":261},{"_218":256,"_257":256,"_258":259,"_260":261},"Single Cell Denoising","value","filter",{"_262":261,"_257":256},"category","label","field","Healthcare","indus_healthcare",{"_262":266,"_257":264},"industry","Deep Learning",{"_262":261,"_257":267},"Bulk Denoising",{"_262":261,"_257":269},{"_262":261,"_257":219},"Atac-seq",{"_262":261,"_257":272},{"_118":1254,"_120":1255},"ngcApps",[1207],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1188":1189,"_1190":1191,"_1192":1193,"_1194":1195,"_1196":285,"_149":309,"_1197":1198,"_1199":56,"_1200":97,"_1201":97,"_1202":1203,"_1204":97,"_1205":1206},"amplitude",{"_1185":1186,"_823":-7,"_1187":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1179":1180,"_1181":1182,"_1183":1184},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1063":390,"_1064":1065,"_1066":1067,"_1068":1069},"announcements",[],"consentState",{"_347":1053,"_1054":31,"_1055":31,"_1056":97,"_1057":1058},"consentConfig",{"_329":330,"_331":332,"_333":334,"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348},"showContactAdmin",1784938726760,[],[],[323,324,325,326,327,328],{"_45":59,"_47":69,"_49":113,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":113,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":66,"_49":113,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":113,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":16,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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atacworks_track.png	57.39 KB	September 29, 2020 UTC	curl wget Download
model_architecture.png	46.72 KB	September 29, 2020 UTC	curl wget Download