Evidence map›Paper›PMID 30988467›Full record

ArticleNature methods2019

Integrated transcriptomic-genomic tool Texomer profiles cancer tissues.

Fang Wang, Shaojun Zhang, Tae-Beom Kim, Yu-Yu Lin, Ramiz Iqbal, Zixing Wang, Vakul Mohanty, Kanishka Sircar, Jose A Karam, Michael C Wendl and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature methods, 2019. 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
–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

8 citing papers in PubMed.

  1. 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

15 authors.

Fang WangDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Shaojun ZhangDepartment of Genome Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0001-5760-5797
Tae-Beom KimDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Yu-Yu LinDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Ramiz IqbalDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Zixing WangDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Vakul MohantyDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Kanishka SircarDepartment of Pathology and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Jose A KaramDepartment of Urology and Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Michael C WendlDepartment of Medicine and Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
Funda Meric-BernstamDepartment of Investigational Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
John N WeinsteinDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Li DingDepartment of Medicine and Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.ORCID http://orcid.org/0000-0003-1517-2975
Gordon B MillsDepartment of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Ken ChenDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. kchen3@mdanderson.org.ORCID http://orcid.org/0000-0003-4013-5279

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
Integrative bioinformatics and functional characterization of oncogenic driver aberrations in cancerU01CA217842 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DENEEN, BENJAMIN, MILLS, GORDON B. · 2017 to 2021
$3.6M
Deep Discovery and Clinical Interpretation of Germline and Somatic Cancer DriversU24CA211006 · NCI · WASHINGTON UNIVERSITY · PI DING, LI, GOVINDAN, RAMASWAMY · 2016 to 2020
$1.8M
Integrated analysis of protein expression data from the Reverse Phase Protein Array (RPPA) platformU24CA210950 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI AKBANI, REHAN, MILLS, GORDON B. · 2016 to 2020
$1.8M
Delineating Heterogeneous Structural Complexity in Cancer GenomesR01CA172652 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI CHEN, KEN · 2013 to 2016
$1.4M
NCI NIH HHS P30 CA016672NCI NIH HHS R01 CA172652NCI NIH HHS U01 CA217842NCI NIH HHS U24 CA210950NCI NIH HHS U24 CA211006
6 · The paper itself

Abstract

Profiling of both the genome and the transcriptome promises a comprehensive, functional readout of a tissue sample, yet analytical approaches are required to translate the increased data dimensionality, heterogeneity and complexity into patient benefits. We developed a statistical approach called Texomer ( https://github.com/KChen-lab/Texomer ) that performs allele-specific, tumor-deconvoluted transcriptome-exome integration of autologous bulk whole-exome and transcriptome sequencing data. Texomer results in substantially improved accuracy in sample categorization and functional variant prioritization.

Indexed as

Genome, HumanAllelesDNA, NeoplasmExomeGene Expression ProfilingGenomicsHigh-Throughput Nucleotide SequencingHumansMutationNeoplasmsPolymorphism, Single NucleotideTranscriptomeDNA, Neoplasm

Identifiers

PMID30988467
PMCPMC7337246

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.