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CloudATAC: a cloud-based framework for ATAC-Seq data analysis.
Veerappa, Avinash M; Rowley, M Jordan; Maggio, Angela; Beaudry, Laura; Hawkins, Dale; Kim, Allen; Sethi, Sahil; Sorgen, Paul L; Guda, Chittibabu.
Afiliação
  • Veerappa AM; University of Nebraska Medical Center, Omaha, NE 68105 USA.
  • Rowley MJ; University of Nebraska Medical Center, Omaha, NE 68105 USA.
  • Maggio A; Deloitte Consulting LLP, Health Data and AI Arlington, VA, USA.
  • Beaudry L; Google Google Public Sector, Professional Services Reston, VA, USA.
  • Hawkins D; Google Google Public Sector, Professional Services Reston, VA, USA.
  • Kim A; Google Google Public Sector, Professional Services Reston, VA, USA.
  • Sethi S; University of Nebraska Medical Center, Omaha, NE 68105 USA.
  • Sorgen PL; University of Nebraska Medical Center, Omaha, NE 68105 USA.
  • Guda C; University of Nebraska Medical Center, Omaha, NE 68105 USA.
Brief Bioinform ; 25(Supplement_1)2024 Jul 23.
Article em En | MEDLINE | ID: mdl-39041910
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Sequenciamento de Nucleotídeos em Larga Escala / Computação em Nuvem Limite: Humans Idioma: En Revista: Brief Bioinform Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Sequenciamento de Nucleotídeos em Larga Escala / Computação em Nuvem Limite: Humans Idioma: En Revista: Brief Bioinform Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2024 Tipo de documento: Article