Weixin Wang1, Panwen Wang1, Feng Xu1, Ruibang Luo1, Maria Pik Wong2, Tak-Wah Lam1, Junwen Wang3. 1. Department of Biochemistry, LKS Faculty of Medicine, Hong Kong SAR, Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, Guangdong 518057, HKU-BGI Bioinformatics Algorithms and Core Technology Research Laboratory, Department of Computer Science, Department of Pathology and Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China Department of Biochemistry, LKS Faculty of Medicine, Hong Kong SAR, Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, Guangdong 518057, HKU-BGI Bioinformatics Algorithms and Core Technology Research Laboratory, Department of Computer Science, Department of Pathology and Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China. 2. Department of Biochemistry, LKS Faculty of Medicine, Hong Kong SAR, Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, Guangdong 518057, HKU-BGI Bioinformatics Algorithms and Core Technology Research Laboratory, Department of Computer Science, Department of Pathology and Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China. 3. Department of Biochemistry, LKS Faculty of Medicine, Hong Kong SAR, Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, Guangdong 518057, HKU-BGI Bioinformatics Algorithms and Core Technology Research Laboratory, Department of Computer Science, Department of Pathology and Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China Department of Biochemistry, LKS Faculty of Medicine, Hong Kong SAR, Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, Guangdong 518057, HKU-BGI Bioinformatics Algorithms and Core Technology Research Laboratory, Department of Computer Science, Department of Pathology and Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China Department of Biochemistry, LKS Faculty of Medicine, Hong Kong SAR, Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, Guangdong 518057, HKU-BGI Bioinformatics Algorithms and Core Technology Research Laboratory, Department of Computer Science, Department of Pathology and Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Abstract
UNLABELLED: Recent advances in high-throughput sequencing technologies have enabled us to sequence large number of cancer samples to reveal novel insights into oncogenetic mechanisms. However, the presence of intratumoral heterogeneity, normal cell contamination and insufficient sequencing depth, together pose a challenge for detecting somatic mutations. Here we propose a fast and an accurate somatic single-nucleotide variations (SNVs) detection program, FaSD-somatic. The performance of FaSD-somatic is extensively assessed on various types of cancer against several state-of-the-art somatic SNV detection programs. Benchmarked by somatic SNVs from either existing databases or de novo higher-depth sequencing data, FaSD-somatic has the best overall performance. Furthermore, FaSD-somatic is efficient, it finishes somatic SNV calling within 14 h on 50X whole genome sequencing data in paired samples. AVAILABILITY AND IMPLEMENTATION: The program, datasets and supplementary files are available at http://jjwanglab.org/FaSD-somatic/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
UNLABELLED: Recent advances in high-throughput sequencing technologies have enabled us to sequence large number of cancer samples to reveal novel insights into oncogenetic mechanisms. However, the presence of intratumoral heterogeneity, normal cell contamination and insufficient sequencing depth, together pose a challenge for detecting somatic mutations. Here we propose a fast and an accurate somatic single-nucleotide variations (SNVs) detection program, FaSD-somatic. The performance of FaSD-somatic is extensively assessed on various types of cancer against several state-of-the-art somatic SNV detection programs. Benchmarked by somatic SNVs from either existing databases or de novo higher-depth sequencing data, FaSD-somatic has the best overall performance. Furthermore, FaSD-somatic is efficient, it finishes somatic SNV calling within 14 h on 50X whole genome sequencing data in paired samples. AVAILABILITY AND IMPLEMENTATION: The program, datasets and supplementary files are available at http://jjwanglab.org/FaSD-somatic/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Thomas Schmutzer; Birgit Samans; Emmanuelle Dyrszka; Chris Ulpinnis; Stephan Weise; Doreen Stengel; Christian Colmsee; Denis Lespinasse; Zeljko Micic; Stefan Abel; Peter Duchscherer; Frank Breuer; Amine Abbadi; Gunhild Leckband; Rod Snowdon; Uwe Scholz Journal: Sci Data Date: 2015-12-08 Impact factor: 6.444