Evidence map›Paper›PMID 39041911›Full record

ArticleBriefings in bioinformatics2024

Cloud-based introduction to BASH programming for biologists.

Owen M Wilkins, Ross Campbell, Zelaikha Yosufzai, Valena Doe, Shannon M Soucy

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Owen M WilkinsGenomic Data Science Core, Center for Quantitative Biology (COBRE), Dartmouth College, 1 Medical Center Drive, Lebanon, NH 03766, United States.
Ross CampbellNational Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, United States.
Zelaikha YosufzaiHealth Data and AI, Deloitte Consulting LLP, 1919 N Lynn St, Suite 1500, Arlington, VA 22209, United States.
Valena DoeGoogle Cloud, 1900 Reston Metro Plaza, Reston, VA 20190, United States.
Shannon M SoucyGenomic Data Science Core, Center for Quantitative Biology (COBRE), Dartmouth College, 1 Medical Center Drive, Lebanon, NH 03766, United States.ORCID 0000-0001-8672-316X

Funding

Ultrastructural analysis of axonal Kv2-mediated endoplasmic reticulum/plasma membrane (ER/PM) junctionsP20GM103506 · NIGMS · DARTMOUTH COLLEGE · PI STEVEN FIERING · 2012 to 2026
$60.1M
Zhao - Proj 2P20GM130454 · NIGMS · DARTMOUTH COLLEGE · PI MICHAEL L WHITFIELD · 2019 to 2026
$27.2M
National Institutes of General Medical Science P20GM130454NIGMS NIH HHS P20 GM103506NIGMS NIH HHS P20 GM130454
6 · The paper itself

Abstract

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 authored by National Institute of General Medical Sciences: NIGMS Sandbox: A Learning Platform toward Democratizing Cloud Computing for Biomedical Research at the beginning of this supplement. This module delivers learning materials introducing the utility of the BASH (Bourne Again Shell) programming language for genomic data analysis in an interactive format that uses appropriate cloud resources for data access and analyses. The next-generation sequencing revolution has generated massive amounts of novel biological data from a multitude of platforms that survey an ever-growing list of genomic modalities. These data require significant downstream computational and statistical analyses to glean meaningful biological insights. However, the skill sets required to generate these data are vastly different from the skills required to analyze these data. Bench scientists that generate next-generation data often lack the training required to perform analysis of these datasets and require support from bioinformatics specialists. Dedicated computational training is required to empower biologists in the area of genomic data analysis, however, learning to efficiently leverage a command line interface is a significant barrier in learning how to leverage common analytical tools. Cloud platforms have the potential to democratize access to the technical tools and computational resources necessary to work with modern sequencing data, providing an effective framework for bioinformatics education. This module aims to provide an interactive platform that slowly builds technical skills and knowledge needed to interact with genomics data on the command line in the Cloud. The sandbox format of this module enables users to move through the material at their own pace and test their grasp of the material with knowledge self-checks before building on that material in the next sub-module. 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 ComputingComputational BiologySoftwareGenomicsHigh-Throughput Nucleotide SequencingHumansProgramming Languagesbioinformaticscloud computingdata Scienceeducationtraining

Identifiers

PMID39041911
PMCPMC11264290

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.