ClinCNV: novel method for allele-specific somatic copy-number alterations detection
Demidov, G.; Ossowski, S.
Show abstract
MotivationLarge somatic copy number alterations (CNA), short indels and single nucleotide variants (SNVs) are playing important role in cancer development and can serve as a predictor for targeted therapy selection as well as prognostic factor. Genomic microarrays, FISH, MLPA and many other technologies are widely used for detection of CNAs. Whole-genome sequencing (WGS), whole-exome sequencing (WES) and targeted panel sequencing (TPS) are well established, highly accurate tools for detection of SNVs and small indels, but detection of larger structural variants using WGS, WES and TPS data remains challenging. We developed a tool for high-resolution allele-specific detection of somatic CNAs in NGS data using statistical approach.\n\nResultsWe have developed a new method for read-depth and B-allele frequency (BAF) based multi-sample detection of copy-number changes in paired normal-tumor NGS data and showed its performance using large cohorts of WES and TPS sequenced samples.\n\nAvailabilityClinCNV is freely available on https://github.com/imgag/ClinCNV.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Variant calling tool evaluation for variable size indel calling from next generation whole genome and targeted sequencing data 94%
- Association Tests Using Copy Number Profile Curves (CONCUR) Enhances Power in Rare Copy Number Variant Analysis 94%
- CNAViz: An interactive webtool for user-guided segmentation of tumor DNA sequencing data 94%
Similar papers in this journal
- Identification of single nucleotide variants using position-specific error estimation in deep sequencing data 96%
- Accuracy and Reproducibility of Somatic Point Mutation Calling in Clinical-Type Targeted Sequencing Data 96%
- Bioinformatics workflows for genomic analysis of tumors from Patient Derived Xenografts (PDX): challenges and guidelines 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.