Using population data for assessing next-generation sequencing performance
Author(s) -
Darren T. Houniet,
Thahira Rahman,
Saeed Al Turki,
Matthew E. Hurles,
Yaobo Xu,
Judith Goodship,
Bernard Keavney,
Mauro Santibáñez Koref
Publication year - 2014
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btu606
Subject(s) - exome sequencing , exome , computer science , perl , population , data mining , computational biology , dna sequencing , biology , statistics , genetics , medicine , mutation , mathematics , dna , environmental health , world wide web , gene
During the past 4 years, whole-exome sequencing has become a standard tool for finding rare variants causing Mendelian disorders. In that time, there has also been a proliferation of both sequencing platforms and approaches to analyse their output. This requires approaches to assess the performance of different methods. Traditionally, criteria such as comparison with microarray data or a number of known polymorphic sites have been used. Here we expand such approaches, developing a maximum likelihood framework and using it to estimate the sensitivity and specificity of whole-exome sequencing data.
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