Journal article

A population model for genotyping indels from next-generation sequence data

Haojing Shao, Evangelos Bellos, Hanjiudai Yin, Xiao Liu, Jing Zou, Yingrui Li, Jun Wang, Lachlan JM Coin

NUCLEIC ACIDS RESEARCH | OXFORD UNIV PRESS | Published : 2013

Abstract

Insertion and deletion polymorphisms (indels) are an important source of genomic variation in plant and animal genomes, but accurate genotyping from low-coverage and exome next-generation sequence data remains challenging. We introduce an efficient population clustering algorithm for diploids and polyploids which was tested on a dataset of 2000 exomes. Compared with existing methods, we report a 4-fold reduction in overall indel genotype error rates with a 9-fold reduction in low coverage regions.

University of Melbourne Researchers

Grants

Awarded by Major State Basic Research Development Program of China-973 Program


Awarded by National Natural Science Foundation of China


Awarded by Shenzhen Key Laboratory of Transomics Biotechnologies


Awarded by BBSRC


Awarded by Biotechnology and Biological Sciences Research Council


Funding Acknowledgements

Major State Basic Research Development Program of China-973 Program [2011CB809201, 2011CB809202, 2011CB809203]; Major Program of National Natural Science Foundation of China [30890032]; Shenzhen Key Laboratory of Transomics Biotechnologies [CXB201108250096A]; the BBSRC research grant [award number BB/H024808/1 to L.J.M.C. and E. B.]. Funding for open access charge: Imperial College open access fund.