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BRCA-Pathway: a structural integration and visualization system of TCGA breast cancer data on KEGG pathways
BACKGROUND: Bioinformatics research for finding biological mechanisms can be done by analysis of transcriptome data with pathway based interpretation. Therefore, researchers have tried to develop tools to analyze transcriptome data with pathway based interpretation. Over the years, the amount of omi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5836821/ https://www.ncbi.nlm.nih.gov/pubmed/29504910 http://dx.doi.org/10.1186/s12859-018-2016-6 |
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author | Kim, Inyoung Choi, Saemi Kim, Sun |
author_facet | Kim, Inyoung Choi, Saemi Kim, Sun |
author_sort | Kim, Inyoung |
collection | PubMed |
description | BACKGROUND: Bioinformatics research for finding biological mechanisms can be done by analysis of transcriptome data with pathway based interpretation. Therefore, researchers have tried to develop tools to analyze transcriptome data with pathway based interpretation. Over the years, the amount of omics data has become huge, e.g., TCGA, and the data types to be analyzed have come in many varieties, including mutations, copy number variations, and transcriptome. We also need to consider a complex relationship with regulators of genes, particularly Transcription Factors(TF). However, there has not been a system for pathway based exploration and analysis of TCGA multi-omics data. In this reason, We have developed a web based system BRCA-Pathway to fulfill the need for pathway based analysis of TCGA multi-omics data. RESULTS: BRCA-Pathway is a structured integration and visual exploration system of TCGA breast cancer data on KEGG pathways. For data integration, a relational database is designed and used to integrate multi-omics data of TCGA-BRCA, KEGG pathway data, Hallmark gene sets, transcription factors, driver genes, and PAM50 subtypes. For data exploration, multi-omics data such as SNV, CNV and gene expression can be visualized simultaneously in KEGG pathway maps, together with transcription factors-target genes (TF-TG) correlation and relationships among cancer driver genes. In addition, ’Pathways summary’ and ’Oncoprint’ with mutual exclusivity sort can be generated dynamically with a request by the user. Data in BRCA-Pathway can be downloaded by REST API for further analysis. CONCLUSIONS: BRCA-Pathway helps researchers navigate omics data towards potentially important genes, regulators, and discover complex patterns involving mutations, CNV, and gene expression data of various patient groups in the biological pathway context. In addition, mutually exclusive genomic alteration patterns in a specific pathway can be generated. BRCA-Pathway can provide an integrative perspective on the breast cancer omics data, which can help researchers discover new insights on the biological mechanisms of breast cancer. |
format | Online Article Text |
id | pubmed-5836821 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-58368212018-03-07 BRCA-Pathway: a structural integration and visualization system of TCGA breast cancer data on KEGG pathways Kim, Inyoung Choi, Saemi Kim, Sun BMC Bioinformatics Methodology BACKGROUND: Bioinformatics research for finding biological mechanisms can be done by analysis of transcriptome data with pathway based interpretation. Therefore, researchers have tried to develop tools to analyze transcriptome data with pathway based interpretation. Over the years, the amount of omics data has become huge, e.g., TCGA, and the data types to be analyzed have come in many varieties, including mutations, copy number variations, and transcriptome. We also need to consider a complex relationship with regulators of genes, particularly Transcription Factors(TF). However, there has not been a system for pathway based exploration and analysis of TCGA multi-omics data. In this reason, We have developed a web based system BRCA-Pathway to fulfill the need for pathway based analysis of TCGA multi-omics data. RESULTS: BRCA-Pathway is a structured integration and visual exploration system of TCGA breast cancer data on KEGG pathways. For data integration, a relational database is designed and used to integrate multi-omics data of TCGA-BRCA, KEGG pathway data, Hallmark gene sets, transcription factors, driver genes, and PAM50 subtypes. For data exploration, multi-omics data such as SNV, CNV and gene expression can be visualized simultaneously in KEGG pathway maps, together with transcription factors-target genes (TF-TG) correlation and relationships among cancer driver genes. In addition, ’Pathways summary’ and ’Oncoprint’ with mutual exclusivity sort can be generated dynamically with a request by the user. Data in BRCA-Pathway can be downloaded by REST API for further analysis. CONCLUSIONS: BRCA-Pathway helps researchers navigate omics data towards potentially important genes, regulators, and discover complex patterns involving mutations, CNV, and gene expression data of various patient groups in the biological pathway context. In addition, mutually exclusive genomic alteration patterns in a specific pathway can be generated. BRCA-Pathway can provide an integrative perspective on the breast cancer omics data, which can help researchers discover new insights on the biological mechanisms of breast cancer. BioMed Central 2018-02-19 /pmc/articles/PMC5836821/ /pubmed/29504910 http://dx.doi.org/10.1186/s12859-018-2016-6 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Kim, Inyoung Choi, Saemi Kim, Sun BRCA-Pathway: a structural integration and visualization system of TCGA breast cancer data on KEGG pathways |
title | BRCA-Pathway: a structural integration and visualization system of TCGA breast cancer data on KEGG pathways |
title_full | BRCA-Pathway: a structural integration and visualization system of TCGA breast cancer data on KEGG pathways |
title_fullStr | BRCA-Pathway: a structural integration and visualization system of TCGA breast cancer data on KEGG pathways |
title_full_unstemmed | BRCA-Pathway: a structural integration and visualization system of TCGA breast cancer data on KEGG pathways |
title_short | BRCA-Pathway: a structural integration and visualization system of TCGA breast cancer data on KEGG pathways |
title_sort | brca-pathway: a structural integration and visualization system of tcga breast cancer data on kegg pathways |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5836821/ https://www.ncbi.nlm.nih.gov/pubmed/29504910 http://dx.doi.org/10.1186/s12859-018-2016-6 |
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