Latest revision as of 21:42, 22 March 2021

Abstract

Background Chromatin accessibility profiling assays such as ATAC-seq and DNase1-seq offer the opportunity to rapidly characterize the regulatory state of the genome at a single nucleotide resolution. Optimization of molecular protocols has enabled the molecular biologist to produce next-generation sequencing libraries in several hours, leaving the analysis of sequencing data as the primary obstacle to wide-scale deployment of accessibility profiling assays. To address this obstacle we have developed an optimized and efficient pipeline for the analysis of ATAC-seq and DNase1-seq data. Results We executed a multi-dimensional grid-search on the NIH Biowulf supercomputing cluster to assess the impact of parameter selection on biological reproducibility and ChIP-seq recovery by analyzing 4560 pipeline configurations. Our analysis improved ChIP-seq recovery by 15% for ATAC-seq and 3% for DNase1-seq and determined that PCR duplicate removal improves biological reproducibility by 36% without significant costs in footprinting transcription factors. Our analyses of down sampled reads identified a point of diminishing returns for increased library sequencing depth, with 95% of the ChIP-seq data of a 200 million read footprinting library recovered by 160 million reads. Conclusions We present optimized ATAC-seq and DNase-seq pipelines in both Snakemake and bash formats as well as optimal sequencing depths for ATAC-seq and DNase-seq projects. The optimized ATAC-seq and DNase1-seq analysis pipelines, parameters, and ground-truth ChIP-seq datasets have been made available for deployment and future algorithmic profiling. Electronic supplementary material The online version of this article (10.1186/s12864-018-4943-z) contains supplementary material, which is available to authorized users.

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• http://dx.doi.org/10.1186/s12864-018-4943-z under the license http://creativecommons.org/licenses/by/4.0/

• http://europepmc.org/articles/PMC6069842 under the license https://creativecommons.org/licenses/by/4.0/legalcode

• https://bmcgenomics.biomedcentral.com/track/pdf/10.1186/s12864-018-4943-z

• http://link.springer.com/article/10.1186/s12864-018-4943-z,

https://doaj.org/toc/1471-2164 under the license cc-by

• http://link.springer.com/content/pdf/10.1186/s12864-018-4943-z.pdf,

http://link.springer.com/article/10.1186/s12864-018-4943-z/fulltext.html,

http://dx.doi.org/10.1186/s12864-018-4943-z

• https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4943-z,

https://pubmed.ncbi.nlm.nih.gov/30064353,

https://www.ncbi.nlm.nih.gov/pubmed/30064353,

https://academic.microsoft.com/#/detail/2885585984 under the license http://creativecommons.org/licenses/by/4.0/