Linked-read analysis identifies mutations in single-cell DNA-sequencing data
Whole-genome sequencing of DNA from single cells has the potential to reshape our understanding of mutational heterogeneity in normal and diseased tissues. However, a major difficulty is distinguishing amplification artifacts from biologically derived somatic mutations. Here, we describe linked-read...
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| Veröffentlicht in: | Nature genetics 2019-04, Vol.51 (4), p.749-754 |
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| creator | Bohrson, Craig L. Barton, Alison R. Lodato, Michael A. Rodin, Rachel E. Luquette, Lovelace J. Viswanadham, Vinay V. Gulhan, Doga C. Cortés-Ciriano, Isidro Sherman, Maxwell A. Kwon, Minseok Coulter, Michael E. Galor, Alon Walsh, Christopher A. Park, Peter J. |
| description | Whole-genome sequencing of DNA from single cells has the potential to reshape our understanding of mutational heterogeneity in normal and diseased tissues. However, a major difficulty is distinguishing amplification artifacts from biologically derived somatic mutations. Here, we describe linked-read analysis (LiRA), a method that accurately identifies somatic single-nucleotide variants (sSNVs) by using read-level phasing with nearby germline heterozygous polymorphisms, thereby enabling the characterization of mutational signatures and estimation of somatic mutation rates in single cells.
Linked-read analysis is a method for analyzing single-cell DNA-sequencing data that accurately identifies somatic single-nucleotide variants by using read-level phasing with nearby germline variants, enabling the characterization of mutational signatures and estimation of somatic mutation rates in single cells. |
| doi_str_mv | 10.1038/s41588-019-0366-2 |
| format | Article |
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Linked-read analysis is a method for analyzing single-cell DNA-sequencing data that accurately identifies somatic single-nucleotide variants by using read-level phasing with nearby germline variants, enabling the characterization of mutational signatures and estimation of somatic mutation rates in single cells.</description><identifier>ISSN: 1061-4036</identifier><identifier>EISSN: 1546-1718</identifier><identifier>DOI: 10.1038/s41588-019-0366-2</identifier><identifier>PMID: 30886424</identifier><language>eng</language><publisher>New York: Nature Publishing Group US</publisher><subject>45/23 ; 631/1647/48 ; 631/1647/514/1948 ; Agriculture ; Animal Genetics and Genomics ; Biochemistry ; Bioinformatics ; Biomedical and Life Sciences ; Biomedicine ; Bladder cancer ; Cancer Research ; Chromosomes ; Deoxyribonucleic acid ; DNA ; DNA damage ; DNA Mutational Analysis - methods ; DNA sequencing ; Gene Function ; Gene mutation ; Genetic research ; Genomes ; Genomics ; Haplotypes ; Heterogeneity ; Heterozygote ; Human Genetics ; Humans ; Methods ; Mutation ; Mutation - genetics ; Mutation Rate ; Mutation rates ; Polymorphism, Single Nucleotide - genetics ; Sequence Analysis, DNA - methods ; Single-Cell Analysis - methods ; technical-report ; Whole Genome Sequencing - methods</subject><ispartof>Nature genetics, 2019-04, Vol.51 (4), p.749-754</ispartof><rights>The Author(s), under exclusive licence to Springer Nature America, Inc. 2019</rights><rights>COPYRIGHT 2019 Nature Publishing Group</rights><rights>Copyright Nature Publishing Group Apr 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c566t-aa011861229af0b1fd5acad1ec24dbe4496fd7c5693ef3daf7a024bee35550323</citedby><cites>FETCH-LOGICAL-c566t-aa011861229af0b1fd5acad1ec24dbe4496fd7c5693ef3daf7a024bee35550323</cites><orcidid>0000-0002-2036-494X ; 0000-0002-0156-2238 ; 0000-0001-9378-960X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1038/s41588-019-0366-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1038/s41588-019-0366-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,776,780,881,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30886424$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bohrson, Craig L.</creatorcontrib><creatorcontrib>Barton, Alison R.</creatorcontrib><creatorcontrib>Lodato, Michael A.</creatorcontrib><creatorcontrib>Rodin, Rachel E.</creatorcontrib><creatorcontrib>Luquette, Lovelace J.</creatorcontrib><creatorcontrib>Viswanadham, Vinay V.</creatorcontrib><creatorcontrib>Gulhan, Doga C.</creatorcontrib><creatorcontrib>Cortés-Ciriano, Isidro</creatorcontrib><creatorcontrib>Sherman, Maxwell A.</creatorcontrib><creatorcontrib>Kwon, Minseok</creatorcontrib><creatorcontrib>Coulter, Michael E.</creatorcontrib><creatorcontrib>Galor, Alon</creatorcontrib><creatorcontrib>Walsh, Christopher A.</creatorcontrib><creatorcontrib>Park, Peter J.</creatorcontrib><title>Linked-read analysis identifies mutations in single-cell DNA-sequencing data</title><title>Nature genetics</title><addtitle>Nat Genet</addtitle><addtitle>Nat Genet</addtitle><description>Whole-genome sequencing of DNA from single cells has the potential to reshape our understanding of mutational heterogeneity in normal and diseased tissues. However, a major difficulty is distinguishing amplification artifacts from biologically derived somatic mutations. Here, we describe linked-read analysis (LiRA), a method that accurately identifies somatic single-nucleotide variants (sSNVs) by using read-level phasing with nearby germline heterozygous polymorphisms, thereby enabling the characterization of mutational signatures and estimation of somatic mutation rates in single cells.
Linked-read analysis is a method for analyzing single-cell DNA-sequencing data that accurately identifies somatic single-nucleotide variants by using read-level phasing with nearby germline variants, enabling the characterization of mutational signatures and estimation of somatic mutation rates in single cells.</description><subject>45/23</subject><subject>631/1647/48</subject><subject>631/1647/514/1948</subject><subject>Agriculture</subject><subject>Animal Genetics and Genomics</subject><subject>Biochemistry</subject><subject>Bioinformatics</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Bladder cancer</subject><subject>Cancer Research</subject><subject>Chromosomes</subject><subject>Deoxyribonucleic acid</subject><subject>DNA</subject><subject>DNA damage</subject><subject>DNA Mutational Analysis - methods</subject><subject>DNA sequencing</subject><subject>Gene Function</subject><subject>Gene mutation</subject><subject>Genetic research</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Haplotypes</subject><subject>Heterogeneity</subject><subject>Heterozygote</subject><subject>Human Genetics</subject><subject>Humans</subject><subject>Methods</subject><subject>Mutation</subject><subject>Mutation - 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However, a major difficulty is distinguishing amplification artifacts from biologically derived somatic mutations. Here, we describe linked-read analysis (LiRA), a method that accurately identifies somatic single-nucleotide variants (sSNVs) by using read-level phasing with nearby germline heterozygous polymorphisms, thereby enabling the characterization of mutational signatures and estimation of somatic mutation rates in single cells.
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| subjects | 45/23 631/1647/48 631/1647/514/1948 Agriculture Animal Genetics and Genomics Biochemistry Bioinformatics Biomedical and Life Sciences Biomedicine Bladder cancer Cancer Research Chromosomes Deoxyribonucleic acid DNA DNA damage DNA Mutational Analysis - methods DNA sequencing Gene Function Gene mutation Genetic research Genomes Genomics Haplotypes Heterogeneity Heterozygote Human Genetics Humans Methods Mutation Mutation - genetics Mutation Rate Mutation rates Polymorphism, Single Nucleotide - genetics Sequence Analysis, DNA - methods Single-Cell Analysis - methods technical-report Whole Genome Sequencing - methods |
| title | Linked-read analysis identifies mutations in single-cell DNA-sequencing data |
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