Evidence map›Paper›PMID 39041910›Full record

ArticleBriefings in bioinformatics2024

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

Avinash M Veerappa, M Jordan Rowley, Angela Maggio, Laura Beaudry, Dale Hawkins, Allen Kim, Sahil Sethi, Paul L Sorgen, Chittibabu Guda

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Avinash M VeerappaUniversity of Nebraska Medical Center, Omaha, NE 68105 USA.
M Jordan RowleyUniversity of Nebraska Medical Center, Omaha, NE 68105 USA.
Angela MaggioDeloitte Consulting LLP, Health Data and AI Arlington, VA, USA.
Laura BeaudryGoogle Google Public Sector, Professional Services Reston, VA, USA.
Dale HawkinsGoogle Google Public Sector, Professional Services Reston, VA, USA.
Allen KimGoogle Google Public Sector, Professional Services Reston, VA, USA.
Sahil SethiUniversity of Nebraska Medical Center, Omaha, NE 68105 USA.
Paul L SorgenUniversity of Nebraska Medical Center, Omaha, NE 68105 USA.
Chittibabu GudaUniversity of Nebraska Medical Center, Omaha, NE 68105 USA.ORCID 0000-0002-5393-9316

Funding

UNMC Structural Biology CoreP20GM103427 · NIGMS · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI Heather Colleen Jensen-Smith · 2012 to 2026
$59.2M
NIGMS NIH HHS P20 GM103427NOSI supplement to the parent IDeA Networks of Biomedical Research Excellence (INBRE) Program NIH/NIGMS P20 GM103427
6 · The paper itself

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.

Indexed as

Cloud ComputingHigh-Throughput Nucleotide SequencingSoftwareChromatinChromatin Immunoprecipitation SequencingComputational BiologyHumansSingle-Cell AnalysisChromatinATACseqchromatinGoogle CloudNIH Cloud LabNIH stridessingle-cell ATAC

Identifiers

PMID39041910
PMCPMC11264300

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.