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CloudATAC: a cloud-based framework for ATAC-Seq data analysis. | LitMetric

AI Article Synopsis

  • ATAC-seq is a technique used to study chromatin accessibility, which helps researchers understand gene regulation at both the cell population and single-cell levels.
  • Comprehensive analysis of ATAC-seq data requires navigating a sequence of complex steps and selecting appropriate parameters, which can be challenging for researchers without bioinformatics expertise.
  • Cloud ATAC is an open-source, cloud-based framework designed to simplify ATAC-seq analysis, leveraging Google Cloud for computational resources and Jupyter Notebook for interactive learning, with publicly available source codes and learning materials.

Article Abstract

Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) generates genome-wide chromatin accessibility profiles, providing valuable insights into epigenetic gene regulation at both pooled-cell and single-cell population levels. Comprehensive analysis of ATAC-seq data involves the use of various interdependent programs. Learning the correct sequence of steps needed to process the data can represent a major hurdle. Selecting appropriate parameters at each stage, including pre-analysis, core analysis, and advanced downstream analysis, is important to ensure accurate analysis and interpretation of ATAC-seq data. Additionally, obtaining and working within a limited computational environment presents a significant challenge to non-bioinformatic researchers. Therefore, we present Cloud ATAC, an open-source, cloud-based interactive framework with a scalable, flexible, and streamlined analysis framework based on the best practices approach for pooled-cell and single-cell ATAC-seq data. These frameworks use on-demand computational power and memory, scalability, and a secure and compliant environment provided by the Google Cloud. Additionally, we leverage Jupyter Notebook's interactive computing platform that combines live code, tutorials, narrative text, flashcards, quizzes, and custom visualizations to enhance learning and analysis. Further, leveraging GPU instances has significantly improved the run-time of the single-cell framework. The source codes and data are publicly available through NIH Cloud lab https://github.com/NIGMS/ATAC-Seq-and-Single-Cell-ATAC-Seq-Analysis. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11264300PMC
http://dx.doi.org/10.1093/bib/bbae090DOI Listing

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