COSINE: A web server for clonal and subclonal structure inference and evolution in cancer genomics
[...]these methods had different programming languages and data input formats, which limited their use and comparison. [...]we established a web server for Clonal and Subclonal Structure Inference and Evolution (COSINE) of cancer genomic data, which incorporated twelve popular subclonal reconstructi...
Gespeichert in:
Veröffentlicht in: | Dōngwùxué yánjiū 2022-01, Vol.43 (1), p.75-77 |
---|---|
Hauptverfasser: | , , , , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | [...]these methods had different programming languages and data input formats, which limited their use and comparison. [...]we established a web server for Clonal and Subclonal Structure Inference and Evolution (COSINE) of cancer genomic data, which incorporated twelve popular subclonal reconstruction methods. Generally, subclonal reconstruction involves three steps: first, calculate the fraction of variant alleles of somatic mutations with relevant copy number changes and tumor purity; second, calculate the cancer cell fraction (CCF) in the tumor (using structural variation information correction); third, cluster the CCFs to identify subclonal structures and construct related phylogenetic trees. [...]the accuracy and resolution of each subclonal inference method depends on the experimental design and mutation characteristics of the specific tumor being reconstructed. Figure 1A, B show the general workflow for the inference of clonal and subclonal structure, which includes five steps: (1) somatic mutation calling from matched normal-tumor tissue samples based on next-generation sequencing (NGS) data; (2) gene copy number calling using NGS data; (3) CCF estimation; (4) clonal and subclonal structure inference via CCF clustering; and (5) clonal and subclonal evolutionary tree construction. Xi-Guo Yuan1,#, Yuan Zhao1#, Yang Guo1, Lin-Mei Ge2, Wei Liu3, Shi-Yu Wen3, Qi Li1, Zhang-Bo Wan1, Pei-Na Zheng1, Tao Guo3, Zhi-Da Li3, Martin Peifer4, Yu-Peng Cun5,2· 1 School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China 2 iFlora Bioinformatics Center, Germplasm Bank of Wild Species, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Yunan 650201, China 3 Yuxi Rongjian Information Technology Co., Ltd., Yuxi, Yunan 653100, China 4 Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne 50931, Germany 5 Pediatric Research Institute, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Children's Hospital of Chongqing Medical University, Chongqing 400014, China #Authors contributed equally to this work *Corresponding author, E-mail: cunyp@cqmu.edu.cn This is an open-access article distributed und |
---|---|
ISSN: | 2095-8137 0254-5853 |
DOI: | 10.24272/j.issn.2095-8137.2021.250 |