scholarly journals Statistically identifying tumor suppressors and oncogenes from pan-cancer genome-sequencing data

2015 ◽  
pp. btv430 ◽  
Author(s):  
Runjun D. Kumar ◽  
Adam C. Searleman ◽  
S. Joshua Swamidass ◽  
Obi L. Griffith ◽  
Ron Bose
Keyword(s):  
Genome Sequencing ◽  
Tumor Suppressors ◽  
Cancer Genome ◽  
Sequencing Data ◽  
Cancer Genome Sequencing ◽  
Pan Cancer

scholarly journals FaSD-somatic: a fast and accurate somatic SNV detection algorithm for cancer genome sequencing data

2014 ◽  
Vol 30 (17) ◽  
pp. 2498-2500 ◽  
Author(s):  
Weixin Wang ◽  
Panwen Wang ◽  
Feng Xu ◽  
Ruibang Luo ◽  
Maria Pik Wong ◽  
...  
Keyword(s):  
Genome Sequencing ◽  
Detection Algorithm ◽  
Cancer Genome ◽  
Sequencing Data ◽  
Cancer Genome Sequencing

scholarly journals Genomon ITDetector: a tool for somatic internal tandem duplication detection from cancer genome sequencing data

2014 ◽  
Vol 31 (1) ◽  
pp. 116-118 ◽  
Author(s):  
Kenichi Chiba ◽  
Yuichi Shiraishi ◽  
Yasunobu Nagata ◽  
Kenichi Yoshida ◽  
Seiya Imoto ◽  
...  
Keyword(s):  
Genome Sequencing ◽  
Tandem Duplication ◽  
Cancer Genome ◽  
Sequencing Data ◽  
Internal Tandem Duplication ◽  
Cancer Genome Sequencing ◽  
Duplication Detection

scholarly journals Detecting somatic point mutations in cancer genome sequencing data: a comparison of mutation callers

10.1186/gm495 ◽  
2013 ◽  
Vol 5 (10) ◽  
pp. 91 ◽  
Author(s):  
Qingguo Wang ◽  
Peilin Jia ◽  
Fei Li ◽  
Haiquan Chen ◽  
Hongbin Ji ◽  
...  
Keyword(s):  
Genome Sequencing ◽  
Point Mutations ◽  
Cancer Genome ◽  
Sequencing Data ◽  
Cancer Genome Sequencing

scholarly journals Unique K-mer sequences for validating cancer-related substitution, insertion and deletion mutations

2020 ◽  
Author(s):  
HoJoon Lee ◽  
Ahmed Shuaibi ◽  
John M. Bell ◽  
Dmitri S. Pavlichin ◽  
Hanlee P. Ji
Keyword(s):  
Genome Sequencing ◽  
Somatic Mutations ◽  
Variant Calling ◽  
Cancer Genome ◽  
Sequencing Data ◽  
Deletion Mutations ◽  
Insertion And Deletion ◽  
Cancer Genome Sequencing ◽  
Significant Difference ◽  
Model Tuning

ABSTRACTThe cancer genome sequencing has led to important discoveries such as identifying cancer gene. However, challenges remain in the analysis of cancer genome sequencing. One significant issue is that mutations identified by multiple variant callers are frequently discordant even when using the same genome sequencing data. For insertion and deletion mutations, oftentimes there is no agreement among different callers. Identifying somatic mutations involves read mapping and variant calling, a complicated process that uses many parameters and model tuning. To validate the identification of true mutations, we developed a method using k-mer sequences. First, we characterized the landscape of unique versus non-unique k-mers in the human genome. Second, we developed a software package, KmerVC, to validate the given somatic mutations from sequencing data. Our program validates the occurrence of a mutation based on statistically significant difference in frequency of k-mers with and without a mutation from matched normal and tumor sequences. Third, we tested our method on both simulated and cancer genome sequencing data. Counting k-mer involving mutations effectively validated true positive mutations including insertions and deletions across different individual samples in a reproducible manner. Thus, we demonstrated a straightforward approach for rapidly validating mutations from cancer genome sequencing data.

Comprehensive fundamental somatic variant calling and quality management strategies for human cancer genomes

2020 ◽  
Author(s):  
Xiaoyu He ◽  
Shanyu Chen ◽  
Ruilin Li ◽  
Xinyin Han ◽  
Zhipeng He ◽  
...  
Keyword(s):  
Genome Sequencing ◽  
High Throughput Sequencing ◽  
Cancer Genomics ◽  
Sequence Data ◽  
Human Cancer ◽  
Management Strategies ◽  
Variant Calling ◽  
Cancer Genome ◽  
Sequencing Data ◽  
Cancer Genome Sequencing

Abstract Next-generation sequencing (NGS) technology has revolutionised human cancer research, particularly via detection of genomic variants with its ultra-high-throughput sequencing and increasing affordability. However, the inundation of rich cancer genomics data has resulted in significant challenges in its exploration and translation into biological insights. One of the difficulties in cancer genome sequencing is software selection. Currently, multiple tools are widely used to process NGS data in four stages: raw sequence data pre-processing and quality control (QC), sequence alignment, variant calling and annotation and visualisation. However, the differences between these NGS tools, including their installation, merits, drawbacks and application, have not been fully appreciated. Therefore, a systematic review of the functionality and performance of NGS tools is required to provide cancer researchers with guidance on software and strategy selection. Another challenge is the multidimensional QC of sequencing data because QC can not only report varied sequence data characteristics but also reveal deviations in diverse features and is essential for a meaningful and successful study. However, monitoring of QC metrics in specific steps including alignment and variant calling is neglected in certain pipelines such as the ‘Best Practices Workflows’ in GATK. In this review, we investigated the most widely used software for the fundamental analysis and QC of cancer genome sequencing data and provided instructions for selecting the most appropriate software and pipelines to ensure precise and efficient conclusions. We further discussed the prospects and new research directions for cancer genomics.

scholarly journals Prioritizing Potentially Druggable Mutations with dGene: An Annotation Tool for Cancer Genome Sequencing Data

PLoS ONE ◽  
2013 ◽  
Vol 8 (6) ◽  
pp. e67980 ◽  
Author(s):  
Runjun D. Kumar ◽  
Li-Wei Chang ◽  
Matthew J. Ellis ◽  
Ron Bose
Keyword(s):  
Genome Sequencing ◽  
Cancer Genome ◽  
Annotation Tool ◽  
Sequencing Data ◽  
Cancer Genome Sequencing

scholarly journals An empirical Bayesian framework for somatic mutation detection from cancer genome sequencing data

2013 ◽  
Vol 41 (7) ◽  
pp. e89-e89 ◽  
Author(s):  
Yuichi Shiraishi ◽  
Yusuke Sato ◽  
Kenichi Chiba ◽  
Yusuke Okuno ◽  
Yasunobu Nagata ◽  
...  
Keyword(s):  
Genome Sequencing ◽  
Somatic Mutation ◽  
Mutation Detection ◽  
Bayesian Framework ◽  
Cancer Genome ◽  
Sequencing Data ◽  
Empirical Bayesian ◽  
Cancer Genome Sequencing ◽  
Somatic Mutation Detection

scholarly journals Unique k-mer sequences for validating cancer-related substitution, insertion and deletion mutations

NAR Cancer ◽  
2020 ◽  
Vol 2 (4) ◽  
Author(s):  
HoJoon Lee ◽  
Ahmed Shuaibi ◽  
John M Bell ◽  
Dmitri S Pavlichin ◽  
Hanlee P Ji
Keyword(s):  
Genome Sequencing ◽  
Somatic Mutations ◽  
Variant Calling ◽  
Cancer Genome ◽  
Cancer Genes ◽  
Sequencing Data ◽  
Deletion Mutations ◽  
Insertion And Deletion ◽  
Cancer Genome Sequencing ◽  
Significant Difference

Abstract Cancer genome sequencing has led to important discoveries such as the identification of cancer genes. However, challenges remain in the analysis of cancer genome sequencing. One significant issue is that mutations identified by multiple variant callers are frequently discordant even when using the same genome sequencing data. For insertion and deletion mutations, oftentimes there is no agreement among different callers. Identifying somatic mutations involves read mapping and variant calling, a complicated process that uses many parameters and model tuning. To validate the identification of true mutations, we developed a method using k-mer sequences. First, we characterized the landscape of unique versus non-unique k-mers in the human genome. Second, we developed a software package, KmerVC, to validate the given somatic mutations from sequencing data. Our program validates the occurrence of a mutation based on statistically significant difference in frequency of k-mers with and without a mutation from matched normal and tumor sequences. Third, we tested our method on both simulated and cancer genome sequencing data. Counting k-mer involving mutations effectively validated true positive mutations including insertions and deletions across different individual samples in a reproducible manner. Thus, we demonstrated a straightforward approach for rapidly validating mutations from cancer genome sequencing data.

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