Robust and rapid algorithms facilitate large-scale whole genome sequencing downstream analysis in an integrative framework

Miaoxin Li, Jiang Li, Mulin Jun Li, Zhicheng Pan, Jacob Shujui Hsu, Dajiang J. Liu, Xiaowei Zhan, Wang Junwen, Song Youqiang, Pak Chung Sham

Research output: Contribution to journalArticle

11 Scopus citations


Whole genome sequencing (WGS) is a promising strategy to unravel variants or genes responsible for human diseases and traits. However, there is a lack of robust platforms for a comprehensive downstream analysis. In the present study, we first proposed three novel algorithms, sequence gap-filled gene feature annotation, bit-block encoded genotypes and sectional fast access to text lines to address three fundamental problems. The three algorithms then formed the infrastructure of a robust parallel computing framework, KGGSeq, for integrating downstream analysis functions for whole genome sequencing data. KGGSeq has been equipped with a comprehensive set of analysis functions for quality control, filtration, annotation, pathogenic prediction and statistical tests. In the tests with whole genome sequencing data from 1000 Genomes Project, KGGSeq annotated several thousand more reliable nonsynonymous variants than other widely used tools (e.g. ANNOVAR and SNPEff). It took only around half an hour on a small server with 10 CPUs to access genotypes of 60 million variants of 2504 subjects, while a popular alternative tool required around one day. KGGSeq's bit-block genotype format used 1.5% or less space to flexibly represent phased or unphased genotypes with multiple alleles and achieved a speed of over 1000 times faster to calculate genotypic correlation.

Original languageEnglish (US)
Article numbere75
JournalNucleic acids research
Issue number9
StatePublished - May 19 2017


All Science Journal Classification (ASJC) codes

  • Genetics

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