ISOWN: accurate somatic mutation identification in the absence of normal tissue controls
A 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...
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Published in | Genome medicine Vol. 9; no. 1; p. 59 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
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BioMed Central Ltd
29.06.2017
BioMed Central BMC |
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Abstract | A 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.
In 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).
In 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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AbstractList | 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 . Background A 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. Results In 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). Conclusions In 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. Abstract Background A 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. Results In 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). Conclusions In 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 . A 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. In 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). In 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 . |
ArticleNumber | 59 |
Audience | Academic |
Author | McPherson, John D Trinh, Quang M Bartlett, John M S Kalatskaya, Irina Spears, Melanie Stein, Lincoln |
Author_xml | – sequence: 1 givenname: Irina orcidid: 0000-0002-1663-859X surname: Kalatskaya fullname: Kalatskaya, Irina email: ikalats@gmail.com organization: Informatics and Bio-computing, Ontario Institute for Cancer Research, Toronto, Ontario, Canada. ikalats@gmail.com – sequence: 2 givenname: Quang M surname: Trinh fullname: Trinh, Quang M organization: Informatics and Bio-computing, Ontario Institute for Cancer Research, Toronto, Ontario, Canada – sequence: 3 givenname: Melanie surname: Spears fullname: Spears, Melanie organization: Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada – sequence: 4 givenname: John D surname: McPherson fullname: McPherson, John D organization: Department of Biochemistry and Molecular Medicine, University of California Davis, Sacramento, California, USA – sequence: 5 givenname: John M S surname: Bartlett fullname: Bartlett, John M S organization: Edinburgh Cancer Research UK Centre, MRC IGMM, University of Edinburgh, Edinburgh, UK – sequence: 6 givenname: Lincoln surname: Stein fullname: Stein, Lincoln organization: Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada |
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Keywords | Somatic mutation Variant classification Matching normal tissue Next-generation sequencing |
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Snippet | A 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... Background A 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... 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... Abstract Background A key step in cancer genome analysis is the identification of somatic mutations in the tumor. This is typically done by comparing the... |
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SubjectTerms | Artificial intelligence Breast cancer Cancer Clinical trials Datasets DNA Mutational Analysis - methods Formaldehyde Gene mutations Genetic aspects Genetic polymorphisms Genomes High-Throughput Nucleotide Sequencing - methods Humans Learning algorithms Matching normal tissue Mutation Neoplasms - genetics Next-generation sequencing Nucleotide sequence Paraffin Software Somatic mutation Studies Supervised Machine Learning Tumors Variant classification |
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Title | ISOWN: accurate somatic mutation identification in the absence of normal tissue controls |
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