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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2012
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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. |
format | Online Article Text |
id | pubmed-3356304 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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