2018
Huang, P. -J.; Chiu, L. -Y.; Lee, C. -C.; Yeh, Y. -M.; Huang, K. -Y.; Chiu, C. -H.; Tang, P.
MSignatureDB: A database for deciphering mutational signatures in human cancers Journal Article
In: Nucleic Acids Research, 46 (D1), pp. D964-D970, 2018, ISSN: 03051048, (cited By 26).
Abstract | Links | BibTeX | 標籤: Article; bioinformatics; cancer genetics; cancer research; gene sequence; genetic database; human; mutational analysis; priority journal; reference database; computer interface; genetics; mutation; neoplasm; nucleic acid database, Databases, Nucleic Acid; Humans; Mutation; Neoplasms; User-Computer Interface
@article{Huang2018D964,
title = {MSignatureDB: A database for deciphering mutational signatures in human cancers},
author = {P. -J. Huang and L. -Y. Chiu and C. -C. Lee and Y. -M. Yeh and K. -Y. Huang and C. -H. Chiu and P. Tang},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040945094&doi=10.1093%2fnar%2fgkx1133&partnerID=40&md5=8a9d6d3a1fa21c1b8ae7f7acbf870b6a},
doi = {10.1093/nar/gkx1133},
issn = {03051048},
year = {2018},
date = {2018-01-01},
journal = {Nucleic Acids Research},
volume = {46},
number = {D1},
pages = {D964-D970},
publisher = {Oxford University Press},
abstract = {Cancer is a genetic disease caused by somatic mutations; however, the understanding of the causative biological processes generating these mutations is limited. A cancer genome bears the cumulative effects of mutational processes during tumor development. Deciphering mutational signatures in cancer is a new topic in cancer research. The Wellcome Trust Sanger Institute (WTSI) has categorized 30 reference signatures in the COSMIC database based on the analyses of ∼410 000 sequencing datasets from TCGA and ICGC. Large cohorts and bioinformatics skills are required to perform the same analysis as WTSI. The quantification of known signatures in custom cohorts is not possible under the current framework of the COSMIC database, which motivates us to construct a database for mutational signatures in cancers and make such analyses more accessible to general researchers. mSignatureDB (http://tardis.cgu.edu.tw/msignaturedb) integrates R packages and in-house scripts to determine the contributions of the published signatures in 15 780 individual tumors from 73 TCGA/ICGC cancer projects, making comparison of signature patterns within and between projects become possible. mSignatureDB also allows users to perform signature analysis on their own datasets, quantifying contributions of signatures at sample resolution, which is a unique feature of mSignatureDB not available in other related databases. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.},
note = {cited By 26},
keywords = {Article; bioinformatics; cancer genetics; cancer research; gene sequence; genetic database; human; mutational analysis; priority journal; reference database; computer interface; genetics; mutation; neoplasm; nucleic acid database, Databases, Nucleic Acid; Humans; Mutation; Neoplasms; User-Computer Interface},
pubstate = {published},
tppubtype = {article}
}
Cancer is a genetic disease caused by somatic mutations; however, the understanding of the causative biological processes generating these mutations is limited. A cancer genome bears the cumulative effects of mutational processes during tumor development. Deciphering mutational signatures in cancer is a new topic in cancer research. The Wellcome Trust Sanger Institute (WTSI) has categorized 30 reference signatures in the COSMIC database based on the analyses of ∼410 000 sequencing datasets from TCGA and ICGC. Large cohorts and bioinformatics skills are required to perform the same analysis as WTSI. The quantification of known signatures in custom cohorts is not possible under the current framework of the COSMIC database, which motivates us to construct a database for mutational signatures in cancers and make such analyses more accessible to general researchers. mSignatureDB (http://tardis.cgu.edu.tw/msignaturedb) integrates R packages and in-house scripts to determine the contributions of the published signatures in 15 780 individual tumors from 73 TCGA/ICGC cancer projects, making comparison of signature patterns within and between projects become possible. mSignatureDB also allows users to perform signature analysis on their own datasets, quantifying contributions of signatures at sample resolution, which is a unique feature of mSignatureDB not available in other related databases. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.