Evidence map›Paper›PMID 35441669›Full record

ArticleBioinformatics (Oxford, England)2022

STAAR workflow: a cloud-based workflow for scalable and reproducible rare variant analysis.

Sheila M Gaynor, Kenneth E Westerman, Lea L Ackovic, Xihao Li, Zilin Li, Alisa K Manning, Anthony Philippakis, Xihong Lin

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  4. Article
  5. Article
  6. IMMerge: merging imputation data at scale.Bioinformatics (Oxford, England) · 2023
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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

8 authors.

Sheila M GaynorDepartment of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA 02115, USA.ORCID 0000-0002-7869-5223
Kenneth E WestermanDepartment of Medicine, Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA 02114, USA.ORCID 0000-0001-7619-1868
Lea L AckovicSeven Bridges, Charlestown, MA 02129, USA.
Xihao LiDepartment of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA 02115, USA.ORCID 0000-0001-8151-0106
Zilin LiDepartment of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA 02115, USA.
Alisa K ManningDepartment of Medicine, Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA 02114, USA.
Anthony PhilippakisThe Broad Institute of MIT and Harvard, Cambridge, MA 02124, USA.
Xihong LinDepartment of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA 02115, USA.ORCID 0000-0001-7067-7752

Funding

Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer ResearchR35CA197449 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI XIHONG LIN · 2015 to 2026
$10.9M
Powering whole genome sequence-based genetic discovery for common human diseases- Extended 2021-2022.U01HG009088 · NHGRI · HARVARD SCHOOL OF PUBLIC HEALTH · PI LIN, XIHONG, NEALE, BENJAMIN MICHAEL · 2016 to 2021
$5.1M
Statistical Methods for Integrative Analysis of Large-Scale Whole Genome Sequencing Studies and Biobanks of Common DiseasesR01HL163560 · NHLBI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI XIHONG LIN · 2022 to 2026
$2.6M
Statistical Methods to Integrate Rich Functional and Phenotypic Data in Whole Genome Sequencing AnalysesK99HL151877 · NHLBI · HARVARD SCHOOL OF PUBLIC HEALTH · PI GAYNOR, SHEILA · 2021 to 2021
$163k
NCI NIH HHS R35 CA197449NHGRI NIH HHS U01 HG009088NHLBI BioData Catalyst ecosystem. This work was supported by the National Institutes of Health funding sources 1OT3 HL147154-01NHLBI NIH HHS K99 HL151877NHLBI NIH HHS R01 HL163560
6 · The paper itself

Abstract

summaryWe developed the variant-Set Test for Association using Annotation infoRmation (STAAR) workflow description language (WDL) workflow to facilitate the analysis of rare variants in whole genome sequencing association studies. The open-access STAAR workflow written in the WDL allows a user to perform rare variant testing for both gene-centric and genetic region approaches, enabling genome-wide, candidate and conditional analyses. It incorporates functional annotations into the workflow as introduced in the STAAR method in order to boost the rare variant analysis power. This tool was specifically developed and optimized to be implemented on cloud-based platforms such as BioData Catalyst Powered by Terra. It provides easy-to-use functionality for rare variant analysis that can be incorporated into an exhaustive whole genome sequencing analysis pipeline. AVAILABILITY AND IMPLEMENTATION: The workflow is freely available from https://dockstore.org/workflows/github.com/sheilagaynor/STAAR_workflow. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

Cloud ComputingSoftwareGenomeGenome-Wide Association StudyWorkflow

Identifiers

PMID35441669
PMCPMC9991895

What OpenQuestion holds

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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.