Inference of Tumor Phylogenies with Improved Somatic Mutation Discovery

Raheleh Salari; Syed Shayon Saleh; Dorna Kashef-Haghighi; David Khavari; Daniel E. Newburger; Robert B. West; Arend Sidow; Serafim Batzoglou

doi:10.1007/978-3-642-37195-0_21

Inference of Tumor Phylogenies with Improved Somatic Mutation Discovery

Raheleh Salari, Syed Shayon Saleh, Dorna Kashef-Haghighi, David Khavari, Daniel E. Newburger, Robert B. West, Arend Sidow, Serafim Batzoglou

Research output: Chapter in Book/Report/Conference proceeding › Chapter

4 Scopus citations

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.

Original language	English (US)
Title of host publication	Research in Computational Molecular Biology
Publisher	Springer Science + Business Media
Pages	249-263
Number of pages	15
ISBN (Print)	9783642371943
DOIs	https://doi.org/10.1007/978-3-642-37195-0_21 https://doi.org/10.1089/cmb.2013.0106
State	Published - 2013
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: 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.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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Salari, R., Saleh, S. S., Kashef-Haghighi, D., Khavari, D., Newburger, D. E., West, R. B., Sidow, A., & Batzoglou, S. (2013). Inference of Tumor Phylogenies with Improved Somatic Mutation Discovery. In Research in Computational Molecular Biology (pp. 249-263). Springer Science + Business Media. https://doi.org/10.1007/978-3-642-37195-0_21, https://doi.org/10.1089/cmb.2013.0106

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title = "Inference of Tumor Phylogenies with Improved Somatic Mutation Discovery",

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. {\textcopyright} 2013 Springer-Verlag.",

author = "Raheleh Salari and Saleh, {Syed Shayon} and Dorna Kashef-Haghighi and David Khavari and Newburger, {Daniel E.} and West, {Robert B.} and Arend Sidow and Serafim Batzoglou",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: 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. This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

year = "2013",

doi = "10.1007/978-3-642-37195-0_21",

language = "English (US)",

isbn = "9783642371943",

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AU - Saleh, Syed Shayon

AU - Kashef-Haghighi, Dorna

AU - Khavari, David

AU - Newburger, Daniel E.

AU - West, Robert B.

AU - Sidow, Arend

AU - Batzoglou, Serafim

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: 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. This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

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BT - Research in Computational Molecular Biology

PB - Springer Science + Business Media

ER -

Inference of Tumor Phylogenies with Improved Somatic Mutation Discovery

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