Evidence map›Paper›PMID 32657377›Full record

ArticleBioinformatics (Oxford, England)2020

Terminus enables the discovery of data-driven, robust transcript groups from RNA-seq data.

Hirak Sarkar, Avi Srivastava, Héctor Corrada Bravo, Michael I Love, Rob Patro

Open access · hybridAbstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.0field-weighted citation impact, top 25% 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

13 citing papers in PubMed, 25 citations in OpenAlex.

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  11. Deriving Ranges of Optimal Estimated Transcript Expression due to Nonidentifiability.Journal of computational biology : a journal of computational molecular cell biology · 2022
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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

5 authors at 3 institutions in 1 country.

Hirak SarkarDepartment of Computer Science, University of Maryland, College Park, MD 20742, USA.
Avi SrivastavaDepartment of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA.
Héctor Corrada BravoDepartment of Computer Science, University of Maryland, College Park, MD 20742, USA.
Michael I LoveDepartment of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC 27516, USA.
Rob PatroDepartment of Computer Science, University of Maryland, College Park, MD 20742, USA.
University of Maryland, College Park · USStony Brook University · USUniversity of North Carolina at Chapel Hill · US

Funding

UNC-CH CENTER FOR ENVIRONMENTAL HEALTH &SUSCEPTIBILITYP30ES010126 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Melissa A. Troester · 2001 to 2026
$36.3M
Statistical Methods for Cancer Clinical TrialsP01CA142538 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI DAVIDIAN, MARIE, KOSOROK, MICHAEL R · 2010 to 2019
$21.3M
A Modular Framework for Accurate, Interpretable, and Reproducible Analysis of Long Read RNA-Seq DataR01HG009937 · NHGRI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Michael Isaiah Love, Robert Patro · 2018 to 2026
$2.8M
pathQTL: Integrative Multi-Omics Causal Inference of Molecular Mechanisms Leading to Neuropsychiatric IllnessR01MH118349 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LOVE, MICHAEL ISAIAH, STEIN, JASON LOUIS · 2019 to 2023
$2.4M
Illuminating Neurodevelopment through Integrated Analysis and Vizualization of Multi-Omic DataR24MH114815 · NIMH · UNIVERSITY OF MARYLAND BALTIMORE · PI HERTZANO, RONNA, WHITE, OWEN R · 2018 to 2020
$2.3M
Integrative Visual and Computational Exploratory Analysis of Genomics DataR01GM114267 · NIGMS · UNIV OF MARYLAND, COLLEGE PARK · PI CORRADA BRAVO, HECTOR · 2015 to 2018
$1.8M
NCI NIH HHS P01 CA142538NHGRI NIH HHS R01 HG009937NIEHS NIH HHS P30 ES010126NIGMS NIH HHS R01 GM114267NIMH NIH HHS R01 MH118349NIMH NIH HHS R24 MH114815
6 · The paper itself

Abstract

motivationAdvances in sequencing technology, inference algorithms and differential testing methodology have enabled transcript-level analysis of RNA-seq data. Yet, the inherent inferential uncertainty in transcript-level abundance estimation, even among the most accurate approaches, means that robust transcript-level analysis often remains a challenge. Conversely, gene-level analysis remains a common and robust approach for understanding RNA-seq data, but it coarsens the resulting analysis to the level of genes, even if the data strongly support specific transcript-level effects.

resultsWe introduce a new data-driven approach for grouping together transcripts in an experiment based on their inferential uncertainty. Transcripts that share large numbers of ambiguously-mapping fragments with other transcripts, in complex patterns, often cannot have their abundances confidently estimated. Yet, the total transcriptional output of that group of transcripts will have greatly reduced inferential uncertainty, thus allowing more robust and confident downstream analysis. Our approach, implemented in the tool terminus, groups together transcripts in a data-driven manner allowing transcript-level analysis where it can be confidently supported, and deriving transcriptional groups where the inferential uncertainty is too high to support a transcript-level result. AVAILABILITY AND IMPLEMENTATION: Terminus is implemented in Rust, and is freely available and open source. It can be obtained from https://github.com/COMBINE-lab/Terminus. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

Gene Expression ProfilingSoftwareAlgorithmsRNA-SeqSequence Analysis, RNA

Identifiers

PMID32657377
PMCPMC7355257
OpenAlexW3043726824

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.