Evidence map›Paper›PMID 37308696›Full record

ArticleNature methods2023

Context-aware transcript quantification from long-read RNA-seq data with Bambu.

Ying Chen, Andre Sim, Yuk Kei Wan, Keith Yeo, Joseph Jing Xian Lee, Min Hao Ling, Michael I Love, Jonathan Göke

Abstract read
In one paragraph

Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 129 papers.

0numbers the graph read from it
0cells of the map it votes in
129citing 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

129 citing papers in PubMed.

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  18. Analysis of isoform complexity in pan-transcriptome graphs with atroplex.bioRxiv : the preprint server for biology · 2026
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  19. Review
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69 more citing papers are in PubMed but not listed here.

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.

Ying Chen *Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.ORCID http://orcid.org/0000-0001-7876-4877
Andre Sim *Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.ORCID http://orcid.org/0000-0002-4062-469X
Yuk Kei WanGenome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.
Keith YeoGenome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.ORCID http://orcid.org/0000-0002-7903-1090
Joseph Jing Xian LeeGenome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.ORCID http://orcid.org/0000-0002-4983-4714
Min Hao LingGenome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.
Michael I LoveDepartment of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.ORCID http://orcid.org/0000-0001-8401-0545
Jonathan GökeGenome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore. gokej@gis.a-star.edu.sg.ORCID http://orcid.org/0000-0002-0825-4991

Funding

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
NHGRI NIH HHS R01 HG009937
6 · The paper itself

Abstract

Most approaches to transcript quantification rely on fixed reference annotations; however, the transcriptome is dynamic and depending on the context, such static annotations contain inactive isoforms for some genes, whereas they are incomplete for others. Here we present Bambu, a method that performs machine-learning-based transcript discovery to enable quantification specific to the context of interest using long-read RNA-sequencing. To identify novel transcripts, Bambu estimates the novel discovery rate, which replaces arbitrary per-sample thresholds with a single, interpretable, precision-calibrated parameter. Bambu retains the full-length and unique read counts, enabling accurate quantification in presence of inactive isoforms. Compared to existing methods for transcript discovery, Bambu achieves greater precision without sacrificing sensitivity. We show that context-aware annotations improve quantification for both novel and known transcripts. We apply Bambu to quantify isoforms from repetitive HERVH-LTR7 retrotransposons in human embryonic stem cells, demonstrating the ability for context-specific transcript expression analysis.

Indexed as

Gene Expression ProfilingTranscriptomeHumansProtein IsoformsRNA-SeqSequence Analysis, RNAProtein Isoforms

Identifiers

PMID37308696
PMCPMC10448944

What OpenQuestion holds

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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.