Evidence map›Paper›PMID 34480063›Full record

ArticleScientific reports2021

A Bayesian approach for accurate de novo transcriptome assembly.

Xu Shi, Xiao Wang, Andrew F Neuwald, Leena Halakivi-Clarke, Robert Clarke, Jianhua Xuan

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 56% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Review
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

6 authors at 4 institutions in 1 country.

Xu Shi *Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA, 22203, USA.
Xiao Wang *Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA, 22203, USA.
Andrew F NeuwaldInstitute for Genome Sciences and Department Biochemistry and Molecular Biology, University of Maryland School of Medicine, 670 W. Baltimore Street, Baltimore, MD, 21201, USA.
Leena Halakivi-ClarkeHormel Institute, University of Minnesota, 16th Street N, Austin, MN, 55912, USA.
Robert ClarkeHormel Institute, University of Minnesota, 16th Street N, Austin, MN, 55912, USA.
Jianhua XuanBradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA, 22203, USA. xuan@vt.edu.
Virginia Tech · USHormel (United States) · USUniversity of Maryland, Baltimore · USUniversity of Minnesota · US

Funding

Novel Targets in Endocrine ResponsivenessU54CA149147 · NCI · GEORGETOWN UNIVERSITY · PI CLARKE, ROBERT R. · 2010 to 2014
$6.9M
Transgenerational effects of maternal high fat diet during pregnancy on breast caR01CA164384 · NCI · GEORGETOWN UNIVERSITY · PI HILAKIVI-CLARKE, LEENA A. · 2012 to 2015
$2.0M
Uncovering Estrogen Receptor-Signaling Networks to Overcome Endocrine ResistanceR01CA149653 · NCI · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI XUAN, JASON JIANHUA · 2011 to 2015
$1.5M
Advanced correlation analyses to infer sequence and structural determinants of protein functionR01GM125878 · NIGMS · UNIVERSITY OF MARYLAND BALTIMORE · PI NEUWALD, ANDREW F · 2018 to 2021
$1.3M
NCI NIH HHS R01 CA149653NCI NIH HHS R01 CA164384NCI NIH HHS U54 CA149147NIGMS NIH HHS R01 GM125878
6 · The paper itself

Abstract

De novo transcriptome assembly from billions of RNA-seq reads is very challenging due to alternative splicing and various levels of expression, which often leads to incorrect, mis-assembled transcripts. BayesDenovo addresses this problem by using both a read-guided strategy to accurately reconstruct splicing graphs from the RNA-seq data and a Bayesian strategy to estimate, from these graphs, the probability of transcript expression without penalizing poorly expressed transcripts. Simulation and cell line benchmark studies demonstrate that BayesDenovo is very effective in reducing false positives and achieves much higher accuracy than other assemblers, especially for alternatively spliced genes and for highly or poorly expressed transcripts. Moreover, BayesDenovo is more robust on multiple replicates by assembling a larger portion of common transcripts. When applied to breast cancer data, BayesDenovo identifies phenotype-specific transcripts associated with breast cancer recurrence.

Indexed as

High-Throughput Nucleotide SequencingTranscriptomeBayes TheoremComputer SimulationGene Expression ProfilingHumansSequence Analysis, RNA

Identifiers

PMID34480063
PMCPMC8417280
OpenAlexW3198661284

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.