ArticleGenome medicine2017
ISOWN: accurate somatic mutation identification in the absence of normal tissue controls.
Article in Genome medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Somatic likelihood tiering: an interpretable post-calling triage protocol for tumor-only whole-exome variant review.Briefings in bioinformatics · 2026Article
- Comprehensive genomic profiling of neuroendocrine neoplasms of the colorectum.Frontiers in genetics · 2026Article
- ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling.Nature communications · 2025Article
- Precise identification of somatic and germline variants in the absence of matched normal samples.Briefings in bioinformatics · 2024Article
- Detecting Somatic Mutations Without Matched Normal Samples Using Long Reads.bioRxiv : the preprint server for biology · 2024Article
- A critical spotlight on the paradigms of FFPE-DNA sequencing.Nucleic acids research · 2023Review
- mitoSomatic: a tool for accurate identification of mitochondrial DNA somatic mutations without paired controls.Molecular oncology · 2023Article
- DeepSom: a CNN-based approach to somatic variant calling in WGS samples without a matched normal.Bioinformatics (Oxford, England) · 2023Article
- Precision oncology strategy in cetuximab-resistant ERRFI1-mutant colon cancer: case report of disease progression on afatinib.American journal of translational research · 2023Article
- Incidental germline findings during molecular profiling of tumor tissues for precision oncology: molecular survey and methodological obstacles.Journal of translational medicine · 2022Article
- Somatic and Germline Variant Calling from Next-Generation Sequencing Data.Advances in experimental medicine and biology · 2022Article
- UNMASC: tumor-only variant calling with unmatched normal controls.NAR cancer · 2021Article
- A novel machine learning approach (svmSomatic) to distinguish somatic and germline mutations using next-generation sequencing data.Zoological research · 2021Article
- Whole-exome mutational landscape of neuroendocrine carcinomas of the gallbladder.Signal transduction and targeted therapy · 2021Article
- Article
- Mutational profiling of micro-dissected pre-malignant lesions from archived specimens.BMC medical genomics · 2020Article
- Reliable Analysis of Clinical Tumor-Only Whole-Exome Sequencing Data.JCO clinical cancer informatics · 2020Article
- Somatic genetic aberrations in benign breast disease and the risk of subsequent breast cancer.NPJ breast cancer · 2020Article
- Assessment of somatic single-nucleotide variation in brain tissue of cases with schizophrenia.Translational psychiatry · 2019Article
- Calling Variants in the Clinic: Informed Variant Calling Decisions Based on Biological, Clinical, and Laboratory Variables.Computational and structural biotechnology journal · 2019Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA key step in cancer genome analysis is the identification of somatic mutations in the tumor. This is typically done by comparing the genome of the tumor to the reference genome sequence derived from a normal tissue taken from the same donor. However, there are a variety of common scenarios in which matched normal tissue is not available for comparison.
resultsIn this work, we describe an algorithm to distinguish somatic single nucleotide variants (SNVs) in next-generation sequencing data from germline polymorphisms in the absence of normal samples using a machine learning approach. Our algorithm was evaluated using a family of supervised learning classifications across six different cancer types and ~1600 samples, including cell lines, fresh frozen tissues, and formalin-fixed paraffin-embedded tissues; we tested our algorithm with both deep targeted and whole-exome sequencing data. Our algorithm correctly classified between 95 and 98% of somatic mutations with F1-measure ranges from 75.9 to 98.6% depending on the tumor type. We have released the algorithm as a software package called ISOWN (Identification of SOmatic mutations Without matching Normal tissues).
conclusionsIn this work, we describe the development, implementation, and validation of ISOWN, an accurate algorithm for predicting somatic mutations in cancer tissues in the absence of matching normal tissues. ISOWN is available as Open Source under Apache License 2.0 from https://github.com/ikalatskaya/ISOWN .
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