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ChimerDB 4.0: an updated and expanded database of fusion genes.
Jang, Ye Eun; Jang, Insu; Kim, Sunkyu; Cho, Subin; Kim, Daehan; Kim, Keonwoo; Kim, Jaewon; Hwang, Jimin; Kim, Sangok; Kim, Jaesang; Kang, Jaewoo; Lee, Byungwook; Lee, Sanghyuk.
Affiliation
  • Jang YE; Department of Bio-Information Science, Ewha Womans University, Seoul 03760, Republic of Korea.
  • Jang I; Korean Bioinformation Center, Korean Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea.
  • Kim S; Department of Computer Science and Engineering, Korea University, Seoul 02841, Republic of Korea.
  • Cho S; Department of Bio-Information Science, Ewha Womans University, Seoul 03760, Republic of Korea.
  • Kim D; Department of Computer Science and Engineering, Korea University, Seoul 02841, Republic of Korea.
  • Kim K; Department of Computer Science and Engineering, Korea University, Seoul 02841, Republic of Korea.
  • Kim J; Department of Bio-Information Science, Ewha Womans University, Seoul 03760, Republic of Korea.
  • Hwang J; Department of Bio-Information Science, Ewha Womans University, Seoul 03760, Republic of Korea.
  • Kim S; Department of Life Science, Ewha Womans University, Seoul 03760, Republic of Korea.
  • Kim J; Department of Life Science, Ewha Womans University, Seoul 03760, Republic of Korea.
  • Kang J; Department of Computer Science and Engineering, Korea University, Seoul 02841, Republic of Korea.
  • Lee B; Korean Bioinformation Center, Korean Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea.
  • Lee S; Department of Bio-Information Science, Ewha Womans University, Seoul 03760, Republic of Korea.
Nucleic Acids Res ; 48(D1): D817-D824, 2020 01 08.
Article in En | MEDLINE | ID: mdl-31680157
ABSTRACT
Fusion genes represent an important class of biomarkers and therapeutic targets in cancer. ChimerDB is a comprehensive database of fusion genes encompassing analysis of deep sequencing data (ChimerSeq) and text mining of publications (ChimerPub) with extensive manual annotations (ChimerKB). In this update, we present all three modules substantially enhanced by incorporating the recent flood of deep sequencing data and related publications. ChimerSeq now covers all 10 565 patients in the TCGA project, with compilation of computational results from two reliable programs of STAR-Fusion and FusionScan with several public resources. In sum, ChimerSeq includes 65 945 fusion candidates, 21 106 of which were predicted by multiple programs (ChimerSeq-Plus). ChimerPub has been upgraded by applying a deep learning method for text mining followed by extensive manual curation, which yielded 1257 fusion genes including 777 cases with experimental supports (ChimerPub-Plus). ChimerKB includes 1597 fusion genes with publication support, experimental evidences and breakpoint information. Importantly, we implemented several new features to aid estimation of functional significance, including the fusion structure viewer with domain information, gene expression plot of fusion positive versus negative patients and a STRING network viewer. The user interface also was greatly enhanced by applying responsive web design. ChimerDB 4.0 is available at http//www.kobic.re.kr/chimerdb/.
Subject(s)

Full text: 1 Database: MEDLINE Main subject: Biomarkers, Tumor / Computational Biology / Databases, Genetic / Data Management / Neoplasms Type of study: Guideline Limits: Humans Language: En Year: 2020 Type: Article

Full text: 1 Database: MEDLINE Main subject: Biomarkers, Tumor / Computational Biology / Databases, Genetic / Data Management / Neoplasms Type of study: Guideline Limits: Humans Language: En Year: 2020 Type: Article