Evidence map›Paper›PMID 27807048›Full record

ArticleG3 (Bethesda, Md.)2016

snpGeneSets: An R Package for Genome-Wide Study Annotation.

Hao Mei, Lianna Li, Fan Jiang, Jeannette Simino, Michael Griswold, Thomas Mosley, Shijian Liu

Abstract read
In one paragraph

Article in G3 (Bethesda, Md.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. 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

7 authors.

Hao MeiDepartment of Data Science, University of Mississippi Medical Center, Jackson, Mississippi 39216 hmei@umc.edu arrow64@163.com.
Lianna LiDepartment of Biology, Tougaloo College, Jackson, Mississippi.
Fan JiangShanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, Shanghai Shi, China, 200023.
Jeannette SiminoDepartment of Data Science, University of Mississippi Medical Center, Jackson, Mississippi 39216.
Michael GriswoldDepartment of Data Science, University of Mississippi Medical Center, Jackson, Mississippi 39216.
Thomas MosleyDepartment of Neurology, University of Mississippi Medical Center, Jackson, Mississippi 39174.
Shijian LiuShanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, Shanghai Shi, China, 200023 hmei@umc.edu arrow64@163.com.

Funding

Training and Mentoring Core P20GM103476 · NIGMS · UNIVERSITY OF SOUTHERN MISSISSIPPI · PI Vijay Rangachari · 2012 to 2026
$60.2M
ARIC Neurocognitive Study (ARIC-NCS) Renewal 2 of 5U01HL096917 · NHLBI · UNIVERSITY OF MISSISSIPPI MED CTR · PI MOSLEY, THOMAS H · 2010 to 2018
$11.9M
FIELD CENTERN01HC055021 · HC · UNIVERSITY OF MISSISSIPPI MEDICAL CENTER · PI JONES, DANIEL W · 1985 to 2000
$617k
NHLBI NIH HHS N01 HC055021NHLBI NIH HHS U01 HL096917NIGMS NIH HHS P20 GM103476
6 · The paper itself

Abstract

Genome-wide studies (GWS) of SNP associations and differential gene expressions have generated abundant results; next-generation sequencing technology has further boosted the number of variants and genes identified. Effective interpretation requires massive annotation and downstream analysis of these genome-wide results, a computationally challenging task. We developed the snpGeneSets package to simplify annotation and analysis of GWS results. Our package integrates local copies of knowledge bases for SNPs, genes, and gene sets, and implements wrapper functions in the R language to enable transparent access to low-level databases for efficient annotation of large genomic data. The package contains functions that execute three types of annotations: (1) genomic mapping annotation for SNPs and genes and functional annotation for gene sets; (2) bidirectional mapping between SNPs and genes, and genes and gene sets; and (3) calculation of gene effect measures from SNP associations and performance of gene set enrichment analyses to identify functional pathways. We applied snpGeneSets to type 2 diabetes (T2D) results from the NHGRI genome-wide association study (GWAS) catalog, a Finnish GWAS, and a genome-wide expression study (GWES). These studies demonstrate the usefulness of snpGeneSets for annotating and performing enrichment analysis of GWS results. The package is open-source, free, and can be downloaded at: https://www.umc.edu/biostats_software/.

Indexed as

SoftwareCodon, TerminatorComputational BiologyDiabetes Mellitus, Type 2Genome-Wide Association StudyGenomicsMolecular Sequence AnnotationPolymorphism, Single NucleotideTranscription Initiation SiteWeb BrowserCodon, Terminatorgenegene setgenetic annotationSNP

Identifiers

PMID27807048
PMCPMC5144977

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.