Evidence map›Paper›PMID 35648784›Full record

ArticlePLoS computational biology2022

Improved transcriptome assembly using a hybrid of long and short reads with StringTie.

Alaina Shumate, Brandon Wong, Geo Pertea, Mihaela Pertea

Abstract read
In one paragraph

Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 303 papers.

0numbers the graph read from it
0cells of the map it votes in
303citing 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

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3 · Its place in the literature

Who cites it

303 citing papers in PubMed.

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243 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

4 authors.

Alaina ShumateDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.ORCID 0000-0002-4450-1857
Brandon WongDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.ORCID 0000-0002-7644-1916
Geo PerteaThe Lieber Institute for Brain Development, Baltimore, Maryland, United States of America.ORCID 0000-0002-3210-7182
Mihaela PerteaDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.ORCID 0000-0003-0762-8637

Funding

Computational Methods for Genome Assembly, Transcript Assembly, and Variant DiscoveryR01HG006677 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI SALZBERG, STEVEN L. · 2011 to 2025
$10.7M
Comprehensive Human Expressed Sequences in Brain (CHESS-BRAIN) and their roles in neuropsychiatric illnessR01MH123567 · NIMH · JOHNS HOPKINS UNIVERSITY · PI SALZBERG, STEVEN L. · 2021 to 2025
$2.8M
NHGRI NIH HHS R01 HG006677NIMH NIH HHS R01 MH123567
6 · The paper itself

Abstract

Short-read RNA sequencing and long-read RNA sequencing each have their strengths and weaknesses for transcriptome assembly. While short reads are highly accurate, they are rarely able to span multiple exons. Long-read technology can capture full-length transcripts, but its relatively high error rate often leads to mis-identified splice sites. Here we present a new release of StringTie that performs hybrid-read assembly. By taking advantage of the strengths of both long and short reads, hybrid-read assembly with StringTie is more accurate than long-read only or short-read only assembly, and on some datasets it can more than double the number of correctly assembled transcripts, while obtaining substantially higher precision than the long-read data assembly alone. Here we demonstrate the improved accuracy on simulated data and real data from Arabidopsis thaliana, Mus musculus, and human. We also show that hybrid-read assembly is more accurate than correcting long reads prior to assembly while also being substantially faster. StringTie is freely available as open source software at https://github.com/gpertea/stringtie.

Indexed as

High-Throughput Nucleotide SequencingTranscriptomeAlgorithmsAnimalsExonsHumansMiceSequence Analysis, DNASequence Analysis, RNASoftware

Identifiers

PMID35648784
PMCPMC9191730

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.