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Personalized Pathway Enrichment Map of Putative Cancer Genes from Next Generation Sequencing Data

BACKGROUND: Pathway analysis of a set of genes represents an important area in large-scale omic data analysis. However, the application of traditional pathway enrichment methods to next-generation sequencing (NGS) data is prone to several potential biases, including genomic/genetic factors (e.g., th...

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Autores principales: Jia, Peilin, Zhao, Zhongming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3356304/
https://www.ncbi.nlm.nih.gov/pubmed/22624051
http://dx.doi.org/10.1371/journal.pone.0037595
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author Jia, Peilin
Zhao, Zhongming
author_facet Jia, Peilin
Zhao, Zhongming
author_sort Jia, Peilin
collection PubMed
description BACKGROUND: Pathway analysis of a set of genes represents an important area in large-scale omic data analysis. However, the application of traditional pathway enrichment methods to next-generation sequencing (NGS) data is prone to several potential biases, including genomic/genetic factors (e.g., the particular disease and gene length) and environmental factors (e.g., personal life-style and frequency and dosage of exposure to mutagens). Therefore, novel methods are urgently needed for these new data types, especially for individual-specific genome data. METHODOLOGY: In this study, we proposed a novel method for the pathway analysis of NGS mutation data by explicitly taking into account the gene-wise mutation rate. We estimated the gene-wise mutation rate based on the individual-specific background mutation rate along with the gene length. Taking the mutation rate as a weight for each gene, our weighted resampling strategy builds the null distribution for each pathway while matching the gene length patterns. The empirical P value obtained then provides an adjusted statistical evaluation. PRINCIPAL FINDINGS/CONCLUSIONS: We demonstrated our weighted resampling method to a lung adenocarcinomas dataset and a glioblastoma dataset, and compared it to other widely applied methods. By explicitly adjusting gene-length, the weighted resampling method performs as well as the standard methods for significant pathways with strong evidence. Importantly, our method could effectively reject many marginally significant pathways detected by standard methods, including several long-gene-based, cancer-unrelated pathways. We further demonstrated that by reducing such biases, pathway crosstalk for each individual and pathway co-mutation map across multiple individuals can be objectively explored and evaluated. This method performs pathway analysis in a sample-centered fashion, and provides an alternative way for accurate analysis of cancer-personalized genomes. It can be extended to other types of genomic data (genotyping and methylation) that have similar bias problems.
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spelling pubmed-33563042012-05-23 Personalized Pathway Enrichment Map of Putative Cancer Genes from Next Generation Sequencing Data Jia, Peilin Zhao, Zhongming PLoS One Research Article BACKGROUND: Pathway analysis of a set of genes represents an important area in large-scale omic data analysis. However, the application of traditional pathway enrichment methods to next-generation sequencing (NGS) data is prone to several potential biases, including genomic/genetic factors (e.g., the particular disease and gene length) and environmental factors (e.g., personal life-style and frequency and dosage of exposure to mutagens). Therefore, novel methods are urgently needed for these new data types, especially for individual-specific genome data. METHODOLOGY: In this study, we proposed a novel method for the pathway analysis of NGS mutation data by explicitly taking into account the gene-wise mutation rate. We estimated the gene-wise mutation rate based on the individual-specific background mutation rate along with the gene length. Taking the mutation rate as a weight for each gene, our weighted resampling strategy builds the null distribution for each pathway while matching the gene length patterns. The empirical P value obtained then provides an adjusted statistical evaluation. PRINCIPAL FINDINGS/CONCLUSIONS: We demonstrated our weighted resampling method to a lung adenocarcinomas dataset and a glioblastoma dataset, and compared it to other widely applied methods. By explicitly adjusting gene-length, the weighted resampling method performs as well as the standard methods for significant pathways with strong evidence. Importantly, our method could effectively reject many marginally significant pathways detected by standard methods, including several long-gene-based, cancer-unrelated pathways. We further demonstrated that by reducing such biases, pathway crosstalk for each individual and pathway co-mutation map across multiple individuals can be objectively explored and evaluated. This method performs pathway analysis in a sample-centered fashion, and provides an alternative way for accurate analysis of cancer-personalized genomes. It can be extended to other types of genomic data (genotyping and methylation) that have similar bias problems. Public Library of Science 2012-05-18 /pmc/articles/PMC3356304/ /pubmed/22624051 http://dx.doi.org/10.1371/journal.pone.0037595 Text en Jia, Zhao. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Jia, Peilin
Zhao, Zhongming
Personalized Pathway Enrichment Map of Putative Cancer Genes from Next Generation Sequencing Data
title Personalized Pathway Enrichment Map of Putative Cancer Genes from Next Generation Sequencing Data
title_full Personalized Pathway Enrichment Map of Putative Cancer Genes from Next Generation Sequencing Data
title_fullStr Personalized Pathway Enrichment Map of Putative Cancer Genes from Next Generation Sequencing Data
title_full_unstemmed Personalized Pathway Enrichment Map of Putative Cancer Genes from Next Generation Sequencing Data
title_short Personalized Pathway Enrichment Map of Putative Cancer Genes from Next Generation Sequencing Data
title_sort personalized pathway enrichment map of putative cancer genes from next generation sequencing data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3356304/
https://www.ncbi.nlm.nih.gov/pubmed/22624051
http://dx.doi.org/10.1371/journal.pone.0037595
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