Computational toolkit for somatic mutation identification

Detecting somatic mutations in non-cancerous or pre-cancerous tissue samples is technically challenging due to their low frequency and difficulties in distinguishing them from germline mutations and artifacts induced by next-generation sequencing. To overcome these obstacles, our team has developed MosaicHunter, the first algorithm capable of systematically identifying somatic single-nucleotide variations from control-free bulk whole-genome and whole-exome sequencing data. We are currently broadening our toolkit's capabilities to identify somatic mutations from various types of bulk and single-cell sequencing data.

Related Publications

Identification of Somatic Mutations From Bulk and Single-Cell Sequencing Data

Huang AY*, Lee EA#. Front Aging. 2022

Parallel RNA and DNA analysis after Deep-sequencing (PRDD-seq) reveals cell type-specific lineage patterns in human brain

Huang AY*, Li P*, Rodin RE, Kim SN, Dou Y, Kenny CJ, Akula SK, Hodge RD, Bakken TE, Miller JA, Lein ES, Park PJ, Lee EA, Walsh CA#. PNAS. 2020

MosaicHunter: accurate detection of postzygotic single-nucleotide mosaicism through next-generation sequencing of unpaired, trio, and paired samples

Huang AY*, Zhang Z*, Ye AY*, Dou Y*, Yan L, Yang X, Zhang Y, Wei L#. Nucleic Acids Res. 2017

Postzygotic single-nucleotide mosaicism in whole-genome sequences of clinically unremarkable individuals

Huang AY*, Xu X*, Ye AY*, Wu Q*, Yan L, Zhao B, Yang X, He Y, Wang S, Zhang Z, Gu B, Zhao HQ, Wang  M, Gao H, Gao G, Zhang Z, Yang X, Wu X, Zhang Y#, Wei L#. Cell Res. 2014