Inference of Tumor Phylogenies with Improved Somatic Mutation Discovery

Handle URI:
http://hdl.handle.net/10754/598617
Title:
Inference of Tumor Phylogenies with Improved Somatic Mutation Discovery
Authors:
Salari, Raheleh; Saleh, Syed Shayon; Kashef-Haghighi, Dorna; Khavari, David; Newburger, Daniel E.; West, Robert B.; Sidow, Arend; Batzoglou, Serafim
Abstract:
Next-generation sequencing technologies provide a powerful tool for studying genome evolution during progression of advanced diseases such as cancer. Although many recent studies have employed new sequencing technologies to detect mutations across multiple, genetically related tumors, current methods do not exploit available phylogenetic information to improve the accuracy of their variant calls. Here, we present a novel algorithm that uses somatic single nucleotide variations (SNVs) in multiple, related tissue samples as lineage markers for phylogenetic tree reconstruction. Our method then leverages the inferred phylogeny to improve the accuracy of SNV discovery. Experimental analyses demonstrate that our method achieves up to 32% improvement for somatic SNV calling of multiple related samples over the accuracy of GATK's Unified Genotyper, the state of the art multisample SNV caller. © 2013 Springer-Verlag.
Citation:
Salari R, Saleh SS, Kashef-Haghighi D, Khavari D, Newburger DE, et al. (2013) Inference of Tumor Phylogenies with Improved Somatic Mutation Discovery. Research in Computational Molecular Biology: 249–263. Available: http://dx.doi.org/10.1007/978-3-642-37195-0_21.
Publisher:
Springer Science + Business Media
Journal:
Research in Computational Molecular Biology
Issue Date:
2013
DOI:
10.1007/978-3-642-37195-0_21
Type:
Book Chapter
ISSN:
0302-9743; 1611-3349
Sponsors:
RS was supported by NSERC postdoctoral fellowship (PDF). DKH was supported by a STMicroelectronics Stanford Graduate Fellowship. SS and DK were supported by Stanford CURIS program. DEN was supported by training grant from NIH/NLM and a Bio-X Stanford Interdisciplinary Graduate Fellowship. This work was funded by a grant from KAUST to SB, and the Sequencing Initiative of the Stanford Department of Pathology to RW and AS.
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Full metadata record

DC FieldValue Language
dc.contributor.authorSalari, Rahelehen
dc.contributor.authorSaleh, Syed Shayonen
dc.contributor.authorKashef-Haghighi, Dornaen
dc.contributor.authorKhavari, Daviden
dc.contributor.authorNewburger, Daniel E.en
dc.contributor.authorWest, Robert B.en
dc.contributor.authorSidow, Arenden
dc.contributor.authorBatzoglou, Serafimen
dc.date.accessioned2016-02-25T13:33:12Zen
dc.date.available2016-02-25T13:33:12Zen
dc.date.issued2013en
dc.identifier.citationSalari R, Saleh SS, Kashef-Haghighi D, Khavari D, Newburger DE, et al. (2013) Inference of Tumor Phylogenies with Improved Somatic Mutation Discovery. Research in Computational Molecular Biology: 249–263. Available: http://dx.doi.org/10.1007/978-3-642-37195-0_21.en
dc.identifier.issn0302-9743en
dc.identifier.issn1611-3349en
dc.identifier.doi10.1007/978-3-642-37195-0_21en
dc.identifier.urihttp://hdl.handle.net/10754/598617en
dc.description.abstractNext-generation sequencing technologies provide a powerful tool for studying genome evolution during progression of advanced diseases such as cancer. Although many recent studies have employed new sequencing technologies to detect mutations across multiple, genetically related tumors, current methods do not exploit available phylogenetic information to improve the accuracy of their variant calls. Here, we present a novel algorithm that uses somatic single nucleotide variations (SNVs) in multiple, related tissue samples as lineage markers for phylogenetic tree reconstruction. Our method then leverages the inferred phylogeny to improve the accuracy of SNV discovery. Experimental analyses demonstrate that our method achieves up to 32% improvement for somatic SNV calling of multiple related samples over the accuracy of GATK's Unified Genotyper, the state of the art multisample SNV caller. © 2013 Springer-Verlag.en
dc.description.sponsorshipRS was supported by NSERC postdoctoral fellowship (PDF). DKH was supported by a STMicroelectronics Stanford Graduate Fellowship. SS and DK were supported by Stanford CURIS program. DEN was supported by training grant from NIH/NLM and a Bio-X Stanford Interdisciplinary Graduate Fellowship. This work was funded by a grant from KAUST to SB, and the Sequencing Initiative of the Stanford Department of Pathology to RW and AS.en
dc.publisherSpringer Science + Business Mediaen
dc.subjectcancer evolutionen
dc.subjectgenetic variationsen
dc.subjecttumor phylogenyen
dc.titleInference of Tumor Phylogenies with Improved Somatic Mutation Discoveryen
dc.typeBook Chapteren
dc.identifier.journalResearch in Computational Molecular Biologyen
dc.contributor.institutionStanford University, Palo Alto, United Statesen
dc.contributor.institutionStanford University School of Medicine, Stanford, United Statesen
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