Evidence map›Paper›PMID 32484809›Full record

ArticlePloS one2020

RefShannon: A genome-guided transcriptome assembler using sparse flow decomposition.

Shunfu Mao, Lior Pachter, David Tse, Sreeram Kannan

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. 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
1.1field-weighted citation impact, top 24% 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

8 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
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  5. Efficient Minimum Flow Decomposition via Integer Linear Programming.Journal of computational biology : a journal of computational molecular cell biology · 2022
    Article
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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

4 authors at 3 institutions in 1 country.

Shunfu MaoDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States of America.ORCID 0000-0002-8203-0507
Lior PachterDivision of Biology and Biological Engineering, Caltech, Pasadena, CA, United States of America.
David TseDepartment of Electrical Engineering, Stanford University, Stanford, CA, United States of America.
Sreeram KannanDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States of America.
University of Washington · USCalifornia Institute of Technology · USStanford University · US

Funding

Algorithms and Software for Provably Accurate De Novo RNA-Seq AssemblyR01HG008164 · NHGRI · UNIVERSITY OF CALIFORNIA BERKELEY · PI KANNAN, SREERAM, PACHTER, LIOR S · 2015 to 2017
$1.4M
NHGRI NIH HHS R01 HG008164
6 · The paper itself

Abstract

High throughput sequencing of RNA (RNA-Seq) has become a staple in modern molecular biology, with applications not only in quantifying gene expression but also in isoform-level analysis of the RNA transcripts. To enable such an isoform-level analysis, a transcriptome assembly algorithm is utilized to stitch together the observed short reads into the corresponding transcripts. This task is complicated due to the complexity of alternative splicing - a mechanism by which the same gene may generate multiple distinct RNA transcripts. We develop a novel genome-guided transcriptome assembler, RefShannon, that exploits the varying abundances of the different transcripts, in enabling an accurate reconstruction of the transcripts. Our evaluation shows RefShannon is able to improve sensitivity effectively (up to 22%) at a given specificity in comparison with other state-of-the-art assemblers. RefShannon is written in Python and is available from Github (https://github.com/shunfumao/RefShannon).

Indexed as

TranscriptomeAlternative SplicingComputational BiologyComputer SimulationHEK293 CellsHigh-Throughput Nucleotide SequencingHuman Embryonic Stem CellsHumansKidneyProtein IsoformsRNA, MessengerSequence AlignmentSequence Analysis, RNASoftwareProtein IsoformsRNA, Messenger

Identifiers

PMID32484809
PMCPMC7266320
OpenAlexW3029935300

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.