TY - CHAP
T1 - Inference of Tumor Phylogenies with Improved Somatic Mutation Discovery
AU - Salari, Raheleh
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.
UR - http://hdl.handle.net/10754/598617
UR - http://link.springer.com/10.1007/978-3-642-37195-0_21
UR - http://www.scopus.com/inward/record.url?scp=84875506806&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-37195-0_21
DO - 10.1007/978-3-642-37195-0_21
M3 - Chapter
SN - 9783642371943
SP - 249
EP - 263
BT - Research in Computational Molecular Biology
PB - Springer Science + Business Media
ER -
