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Mapping Splicing Quantitative Trait Loci in RNA-Seq

BACKGROUND: One of the major mechanisms of generating mRNA diversity is alternative splicing, a regulated process that allows for the flexibility of producing functionally different proteins from the same genomic sequences. This process is often altered in cancer cells to produce aberrant proteins t...

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Autores principales: Jia, Cheng, Hu, Yu, Liu, Yichuan, Li, Mingyao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Libertas Academica 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4218654/
https://www.ncbi.nlm.nih.gov/pubmed/25452687
http://dx.doi.org/10.4137/CIN.S13971
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author Jia, Cheng
Hu, Yu
Liu, Yichuan
Li, Mingyao
author_facet Jia, Cheng
Hu, Yu
Liu, Yichuan
Li, Mingyao
author_sort Jia, Cheng
collection PubMed
description BACKGROUND: One of the major mechanisms of generating mRNA diversity is alternative splicing, a regulated process that allows for the flexibility of producing functionally different proteins from the same genomic sequences. This process is often altered in cancer cells to produce aberrant proteins that drive the progression of cancer. A better understanding of the misregulation of alternative splicing will shed light on the development of novel targets for pharmacological interventions of cancer. METHODS: In this study, we evaluated three statistical methods, random effects meta-regression, beta regression, and generalized linear mixed effects model, for the analysis of splicing quantitative trait loci (sQTL) using RNA-Seq data. All the three methods use exon-inclusion levels estimated by the PennSeq algorithm, a statistical method that utilizes paired-end reads and accounts for non-uniform sequencing coverage. RESULTS: Using both simulated and real RNA-Seq datasets, we compared these three methods with GLiMMPS, a recently developed method for sQTL analysis. Our results indicate that the most reliable and powerful method was the random effects meta-regression approach, which identified sQTLs at low false discovery rates but higher power when compared to GLiMMPS. CONCLUSIONS: We have evaluated three statistical methods for the analysis of sQTLs in RNA-Seq. Results from our study will be instructive for researchers in selecting the appropriate statistical methods for sQTL analysis.
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spelling pubmed-42186542014-12-01 Mapping Splicing Quantitative Trait Loci in RNA-Seq Jia, Cheng Hu, Yu Liu, Yichuan Li, Mingyao Cancer Inform Original Research BACKGROUND: One of the major mechanisms of generating mRNA diversity is alternative splicing, a regulated process that allows for the flexibility of producing functionally different proteins from the same genomic sequences. This process is often altered in cancer cells to produce aberrant proteins that drive the progression of cancer. A better understanding of the misregulation of alternative splicing will shed light on the development of novel targets for pharmacological interventions of cancer. METHODS: In this study, we evaluated three statistical methods, random effects meta-regression, beta regression, and generalized linear mixed effects model, for the analysis of splicing quantitative trait loci (sQTL) using RNA-Seq data. All the three methods use exon-inclusion levels estimated by the PennSeq algorithm, a statistical method that utilizes paired-end reads and accounts for non-uniform sequencing coverage. RESULTS: Using both simulated and real RNA-Seq datasets, we compared these three methods with GLiMMPS, a recently developed method for sQTL analysis. Our results indicate that the most reliable and powerful method was the random effects meta-regression approach, which identified sQTLs at low false discovery rates but higher power when compared to GLiMMPS. CONCLUSIONS: We have evaluated three statistical methods for the analysis of sQTLs in RNA-Seq. Results from our study will be instructive for researchers in selecting the appropriate statistical methods for sQTL analysis. Libertas Academica 2014-10-15 /pmc/articles/PMC4218654/ /pubmed/25452687 http://dx.doi.org/10.4137/CIN.S13971 Text en © 2014 the author(s), publisher and licensee Libertas Academica Ltd. This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License.
spellingShingle Original Research
Jia, Cheng
Hu, Yu
Liu, Yichuan
Li, Mingyao
Mapping Splicing Quantitative Trait Loci in RNA-Seq
title Mapping Splicing Quantitative Trait Loci in RNA-Seq
title_full Mapping Splicing Quantitative Trait Loci in RNA-Seq
title_fullStr Mapping Splicing Quantitative Trait Loci in RNA-Seq
title_full_unstemmed Mapping Splicing Quantitative Trait Loci in RNA-Seq
title_short Mapping Splicing Quantitative Trait Loci in RNA-Seq
title_sort mapping splicing quantitative trait loci in rna-seq
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4218654/
https://www.ncbi.nlm.nih.gov/pubmed/25452687
http://dx.doi.org/10.4137/CIN.S13971
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