Evidence map›Paper›PMID 37089874›Full record

ArticleJournal of biomolecular techniques : JBT2023

Functional Annotation Routines Used by ABRF Bioinformatics Core Facilities - Observations, Comparisons, and Considerations.

Charles A Whittaker, Alper Kucukural, Chris Gates, Owen Michael Wilkins, George W Bell, John N Hutchinson, Shawn W Polson, Julie Dragon

Open access · diamondAbstract read
In one paragraph

Article in Journal of biomolecular techniques : JBT, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 98% of its field
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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 8 institutions in 1 country.

Charles A WhittakerBarbara K. Ostrom (1978) Bioinformatics and Computing Core Facility Swanson Biotechnology Center Koch Institute at the Massachusetts Institute of Technology CambridgeMassachusetts02139 USA.
Alper KucukuralBioinformatics Core University of Massachusetts Medical School WorcesterMassachusetts01605 USA.
Chris GatesBRCF Bioinformatics Core University of Michigan Ann ArborMichigan48109 USA.
Owen Michael WilkinsDepartment of Biomedical Data Science Geisel School of Medicine at Dartmouth HanoverNew Hampshire03755 USA.
George W BellBioinformatics and Research Computing Whitehead Institute CambridgeMassachusetts02142 USA.
John N HutchinsonHarvard T.H. Chan School of Public Health Department of Biostatistics BostonMassachusetts02115 USA.
Shawn W PolsonBioinformatics Core Center for Bioinformatics and Computational Biology University of Delaware Delaware Biotechnology Institute NewarkDelaware19713 USA.
Julie DragonVermont Integrative Genomics Resource and Vermont Biomedical Research Network Bioinformatic Core University of Vermont BurlingtonVermont05405 USA.
Dartmouth College · USHarvard University · USMassachusetts Institute of Technology · USResearch Network (United States) · USUniversity of Delaware · USUniversity of Massachusetts Chan Medical School · USUniversity of Michigan–Ann Arbor · USWhitehead Institute for Biomedical Research · US

Funding

VIRUS PRODUCTION COREP30CA014051 · NCI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI MATTHEW G. VANDER HEIDEN · 1985 to 2026
$93.9M
Translational Engineering in Cancer (TEC)P30CA023108 · NCI · DARTMOUTH COLLEGE · PI Steven D Leach · 1985 to 2026
$91.3M
Predictive Modeling & Optimal Control Framework for Model-Based Epidemic Response in DelawareP20GM103446 · NIGMS · UNIVERSITY OF DELAWARE · PI Anjana Narayan Bhat · 2012 to 2026
$67.2M
Vermont INBRE Administrative Supplement 2024 AWD 118P20GM103449 · NIGMS · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI CHRISTOPHER S FRANCKLYN · 2012 to 2026
$58.4M
University of Massachusetts Center for Clinical Science and Translational SupplementUL1TR001453 · NCATS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LUZURIAGA, KATHERINE F · 2015 to 2024
$39.0M
Zhao - Proj 2P20GM130454 · NIGMS · DARTMOUTH COLLEGE · PI Chengxiang Qiu · 2019 to 2026
$27.2M
SUBSTRATE SPECIFICITY OF THE BER OXIDATIVE DNA GLYCOSYLASESP01CA098993 · NCI · UNIVERSITY OF VERMONT &ST AGRIC COLLEGE · PI DOUBLIE, SYLVIE · 2004 to 2021
$25.9M
NCATS NIH HHS UL1 TR001453NCI NIH HHS P01 CA098993NCI NIH HHS P30 CA014051NCI NIH HHS P30 CA023108NIGMS NIH HHS P20 GM103446NIGMS NIH HHS P20 GM103449NIGMS NIH HHS P20 GM130454
6 · The paper itself

Abstract

The functional annotation of gene lists is a common analysis routine required for most genomics experiments, and bioinformatics core facilities must support these analyses. In contrast to methods such as the quantitation of RNA-Seq reads or differential expression analysis, our research group noted a lack of consensus in our preferred approaches to functional annotation. To investigate this observation, we selected 4 experiments that represent a range of experimental designs encountered by our cores and analyzed those data with 6 tools used by members of the Association of Biomolecular Resource Facilities (ABRF) Genomic Bioinformatics Research Group (GBIRG). To facilitate comparisons between tools, we focused on a single biological result for each experiment. These results were represented by a gene set, and we analyzed these gene sets with each tool considered in our study to map the result to the annotation categories presented by each tool. In most cases, each tool produces data that would facilitate identification of the selected biological result for each experiment. For the exceptions, Fisher's exact test parameters could be adjusted to detect the result. Because Fisher's exact test is used by many functional annotation tools, we investigated input parameters and demonstrate that, while background set size is unlikely to have a significant impact on the results, the numbers of differentially expressed genes in an annotation category and the total number of differentially expressed genes under consideration are both critical parameters that may need to be modified during analyses. In addition, we note that differences in the annotation categories tested by each tool, as well as the composition of those categories, can have a significant impact on results.

Indexed as

Computational BiologyGenomicsMolecular Sequence AnnotationRNA-Seqgene expression profilinggene ontologyknowledge bases

Identifiers

PMID37089874
PMCPMC10121236
OpenAlexW4361192415

What OpenQuestion holds

Textmetadata
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.