Network-Based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis

Wei Zhang, Jae Woong Chang, Lilong Lin, Kay Minn, Baolin Wu, Jeremy Chien, Jeongsik Yong, Hui Zheng, Rui Kuang

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

High-throughput mRNA sequencing (RNA-Seq) is widely used for transcript quantification of gene isoforms. Since RNA-Seq data alone is often not sufficient to accurately identify the read origins from the isoforms for quantification, we propose to explore protein domain-domain interactions as prior knowledge for integrative analysis with RNA-Seq data. We introduce a Network-based method for RNA-Seq-based Transcript Quantification (Net-RSTQ) to integrate protein domain-domain interaction network with short read alignments for transcript abundance estimation. Based on our observation that the abundances of the neighboring isoforms by domain-domain interactions in the network are positively correlated, Net-RSTQ models the expression of the neighboring transcripts as Dirichlet priors on the likelihood of the observed read alignments against the transcripts in one gene. The transcript abundances of all the genes are then jointly estimated with alternating optimization of multiple EM problems. In simulation Net-RSTQ effectively improved isoform transcript quantifications when isoform co-expressions correlate with their interactions. qRT-PCR results on 25 multi-isoform genes in a stem cell line, an ovarian cancer cell line, and a breast cancer cell line also showed that Net-RSTQ estimated more consistent isoform proportions with RNA-Seq data. In the experiments on the RNA-Seq data in The Cancer Genome Atlas (TCGA), the transcript abundances estimated by Net-RSTQ are more informative for patient sample classification of ovarian cancer, breast cancer and lung cancer. All experimental results collectively support that Net-RSTQ is a promising approach for isoform quantification. Net-RSTQ toolbox is available at http://compbio.cs.umn.edu/Net-RSTQ/.

Original languageEnglish (US)
Article numbere1004465
JournalPLoS computational biology
Volume11
Issue number12
DOIs
StatePublished - Jan 1 2015
Externally publishedYes

Fingerprint

Gene Expression Profiling
RNA
transcriptomics
Quantification
cancer
Cancer
Protein Isoforms
neoplasms
Neoplasms
Genes
Gene
Ovarian Cancer
ovarian neoplasms
cell lines
gene
Breast Neoplasms
methodology
Breast Cancer
Interaction
Cell Line

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

Cite this

Network-Based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis. / Zhang, Wei; Chang, Jae Woong; Lin, Lilong; Minn, Kay; Wu, Baolin; Chien, Jeremy; Yong, Jeongsik; Zheng, Hui; Kuang, Rui.

In: PLoS computational biology, Vol. 11, No. 12, e1004465, 01.01.2015.

Research output: Contribution to journalArticle

Zhang, W, Chang, JW, Lin, L, Minn, K, Wu, B, Chien, J, Yong, J, Zheng, H & Kuang, R 2015, 'Network-Based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis', PLoS computational biology, vol. 11, no. 12, e1004465. https://doi.org/10.1371/journal.pcbi.1004465
Zhang, Wei ; Chang, Jae Woong ; Lin, Lilong ; Minn, Kay ; Wu, Baolin ; Chien, Jeremy ; Yong, Jeongsik ; Zheng, Hui ; Kuang, Rui. / Network-Based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis. In: PLoS computational biology. 2015 ; Vol. 11, No. 12.
@article{111960500558432a8094e22697b4500e,
title = "Network-Based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis",
abstract = "High-throughput mRNA sequencing (RNA-Seq) is widely used for transcript quantification of gene isoforms. Since RNA-Seq data alone is often not sufficient to accurately identify the read origins from the isoforms for quantification, we propose to explore protein domain-domain interactions as prior knowledge for integrative analysis with RNA-Seq data. We introduce a Network-based method for RNA-Seq-based Transcript Quantification (Net-RSTQ) to integrate protein domain-domain interaction network with short read alignments for transcript abundance estimation. Based on our observation that the abundances of the neighboring isoforms by domain-domain interactions in the network are positively correlated, Net-RSTQ models the expression of the neighboring transcripts as Dirichlet priors on the likelihood of the observed read alignments against the transcripts in one gene. The transcript abundances of all the genes are then jointly estimated with alternating optimization of multiple EM problems. In simulation Net-RSTQ effectively improved isoform transcript quantifications when isoform co-expressions correlate with their interactions. qRT-PCR results on 25 multi-isoform genes in a stem cell line, an ovarian cancer cell line, and a breast cancer cell line also showed that Net-RSTQ estimated more consistent isoform proportions with RNA-Seq data. In the experiments on the RNA-Seq data in The Cancer Genome Atlas (TCGA), the transcript abundances estimated by Net-RSTQ are more informative for patient sample classification of ovarian cancer, breast cancer and lung cancer. All experimental results collectively support that Net-RSTQ is a promising approach for isoform quantification. Net-RSTQ toolbox is available at http://compbio.cs.umn.edu/Net-RSTQ/.",
author = "Wei Zhang and Chang, {Jae Woong} and Lilong Lin and Kay Minn and Baolin Wu and Jeremy Chien and Jeongsik Yong and Hui Zheng and Rui Kuang",
year = "2015",
month = "1",
day = "1",
doi = "10.1371/journal.pcbi.1004465",
language = "English (US)",
volume = "11",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "12",

}

TY - JOUR

T1 - Network-Based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis

AU - Zhang, Wei

AU - Chang, Jae Woong

AU - Lin, Lilong

AU - Minn, Kay

AU - Wu, Baolin

AU - Chien, Jeremy

AU - Yong, Jeongsik

AU - Zheng, Hui

AU - Kuang, Rui

PY - 2015/1/1

Y1 - 2015/1/1

N2 - High-throughput mRNA sequencing (RNA-Seq) is widely used for transcript quantification of gene isoforms. Since RNA-Seq data alone is often not sufficient to accurately identify the read origins from the isoforms for quantification, we propose to explore protein domain-domain interactions as prior knowledge for integrative analysis with RNA-Seq data. We introduce a Network-based method for RNA-Seq-based Transcript Quantification (Net-RSTQ) to integrate protein domain-domain interaction network with short read alignments for transcript abundance estimation. Based on our observation that the abundances of the neighboring isoforms by domain-domain interactions in the network are positively correlated, Net-RSTQ models the expression of the neighboring transcripts as Dirichlet priors on the likelihood of the observed read alignments against the transcripts in one gene. The transcript abundances of all the genes are then jointly estimated with alternating optimization of multiple EM problems. In simulation Net-RSTQ effectively improved isoform transcript quantifications when isoform co-expressions correlate with their interactions. qRT-PCR results on 25 multi-isoform genes in a stem cell line, an ovarian cancer cell line, and a breast cancer cell line also showed that Net-RSTQ estimated more consistent isoform proportions with RNA-Seq data. In the experiments on the RNA-Seq data in The Cancer Genome Atlas (TCGA), the transcript abundances estimated by Net-RSTQ are more informative for patient sample classification of ovarian cancer, breast cancer and lung cancer. All experimental results collectively support that Net-RSTQ is a promising approach for isoform quantification. Net-RSTQ toolbox is available at http://compbio.cs.umn.edu/Net-RSTQ/.

AB - High-throughput mRNA sequencing (RNA-Seq) is widely used for transcript quantification of gene isoforms. Since RNA-Seq data alone is often not sufficient to accurately identify the read origins from the isoforms for quantification, we propose to explore protein domain-domain interactions as prior knowledge for integrative analysis with RNA-Seq data. We introduce a Network-based method for RNA-Seq-based Transcript Quantification (Net-RSTQ) to integrate protein domain-domain interaction network with short read alignments for transcript abundance estimation. Based on our observation that the abundances of the neighboring isoforms by domain-domain interactions in the network are positively correlated, Net-RSTQ models the expression of the neighboring transcripts as Dirichlet priors on the likelihood of the observed read alignments against the transcripts in one gene. The transcript abundances of all the genes are then jointly estimated with alternating optimization of multiple EM problems. In simulation Net-RSTQ effectively improved isoform transcript quantifications when isoform co-expressions correlate with their interactions. qRT-PCR results on 25 multi-isoform genes in a stem cell line, an ovarian cancer cell line, and a breast cancer cell line also showed that Net-RSTQ estimated more consistent isoform proportions with RNA-Seq data. In the experiments on the RNA-Seq data in The Cancer Genome Atlas (TCGA), the transcript abundances estimated by Net-RSTQ are more informative for patient sample classification of ovarian cancer, breast cancer and lung cancer. All experimental results collectively support that Net-RSTQ is a promising approach for isoform quantification. Net-RSTQ toolbox is available at http://compbio.cs.umn.edu/Net-RSTQ/.

UR - http://www.scopus.com/inward/record.url?scp=84953206419&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84953206419&partnerID=8YFLogxK

U2 - 10.1371/journal.pcbi.1004465

DO - 10.1371/journal.pcbi.1004465

M3 - Article

C2 - 26699225

AN - SCOPUS:84953206419

VL - 11

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 12

M1 - e1004465

ER -