Dataset Information

Using Poisson mixed-effects model to quantify transcript-level gene expression in RNA-Seq.

ABSTRACT:

Motivation

RNA sequencing (RNA-Seq) is a powerful new technology for mapping and quantifying transcriptomes using ultra high-throughput next-generation sequencing technologies. Using deep sequencing, gene expression levels of all transcripts including novel ones can be quantified digitally. Although extremely promising, the massive amounts of data generated by RNA-Seq, substantial biases and uncertainty in short read alignment pose challenges for data analysis. In particular, large base-specific variation and between-base dependence make simple approaches, such as those that use averaging to normalize RNA-Seq data and quantify gene expressions, ineffective.

Results

In this study, we propose a Poisson mixed-effects (POME) model to characterize base-level read coverage within each transcript. The underlying expression level is included as a key parameter in this model. Since the proposed model is capable of incorporating base-specific variation as well as between-base dependence that affect read coverage profile throughout the transcript, it can lead to improved quantification of the true underlying expression level.

Availability and implementation

POME can be freely downloaded at http://www.stat.purdue.edu/~yuzhu/pome.html.

Contact

yuzhu@purdue.edu; zhaohui.qin@emory.edu

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Hu M

PROVIDER: S-EPMC3244770 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature

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Publications

Using Poisson mixed-effects model to quantify transcript-level gene expression in RNA-Seq.

Hu Ming M Zhu Yu Y Taylor Jeremy M G JM Liu Jun S JS Qin Zhaohui S ZS

Bioinformatics (Oxford, England) 20111108 1

<h4>Motivation</h4>RNA sequencing (RNA-Seq) is a powerful new technology for mapping and quantifying transcriptomes using ultra high-throughput next-generation sequencing technologies. Using deep sequencing, gene expression levels of all transcripts including novel ones can be quantified digitally. Although extremely promising, the massive amounts of data generated by RNA-Seq, substantial biases and uncertainty in short read alignment pose challenges for data analysis. In particular, large base- ...[more]

PMID: 22072384

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Project description:Polymorphisms in the interferon regulatory factor 5 (IRF5) gene have been consistently replicated and shown to confer risk for or protection from the development of systemic lupus erythematosus (SLE). IRF5 expression is significantly upregulated in SLE patients and upregulation associates with IRF5-SLE risk haplotypes. IRF5 alternative splicing has also been shown to be elevated in SLE patients. Given that human IRF5 exists as multiple alternatively spliced transcripts with distinct function(s), it is important to determine whether the IRF5 transcript profile expressed in healthy donor immune cells is different from that expressed in SLE patients. Moreover, it is not currently known whether an IRF5-SLE risk haplotype defines the profile of IRF5 transcripts expressed. Using standard molecular cloning techniques, we identified and isolated 14 new differentially spliced IRF5 transcript variants from purified monocytes of healthy donors and SLE patients to generate an IRF5 variant transcriptome. Next-generation sequencing was then used to perform in-depth and quantitative analysis of full-length IRF5 transcript expression in primary immune cells of SLE patients and healthy donors by next-generation sequencing. Evidence for additional alternatively spliced transcripts was obtained from de novo junction discovery. Data from these studies support the overall complexity of IRF5 alternative splicing in SLE. Results from next-generation sequencing correlated with cloning and gave similar abundance rankings in SLE patients thus supporting the use of this new technology for in-depth single gene transcript profiling. Results from this study provide the first proof that 1) SLE patients express an IRF5 transcript signature that is distinct from healthy donors, 2) an IRF5-SLE risk haplotype defines the top four most abundant IRF5 transcripts expressed in SLE patients, and 3) an IRF5 transcript signature enables clustering of SLE patients with the H2 risk haplotype.

Dataset Information

Using Poisson mixed-effects model to quantify transcript-level gene expression in RNA-Seq.

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

Using Poisson mixed-effects model to quantify transcript-level gene expression in RNA-Seq.

Similar Datasets

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