Single-nucleotide polymorphisms (SNPs) are the most abundant form of human genetic variation and a resource for mapping complex genetic traits1. The large volume of data produced by high-throughput sequencing projects is a rich and largely untapped source of SNPs (refs 2, 3, 4, 5). We present here a unified approach to the discovery of variations in genetic sequence data of arbitrary DNA sources. We propose to use the rapidly emerging genomic sequence6,7 as a template on which to layer often unmapped, fragmentary sequence data8,9,10,11 and to use base quality values12 to discern true allelic variations from sequencing errors. By taking advantage of the genomic sequence we are able to use simpler yet more accurate methods for sequence organization: fragment clustering, paralogue identification and multiple alignment. We analyse these sequences with a novel, Bayesian inference engine, POLYBAYES, to calculate the probability that a given site is polymorphic. Rigorous treatment of base quality permits completely automated evaluation of the full length of all sequences, without limitations on alignment depth. We demonstrate this approach by accurate SNP predictions in human ESTs aligned to finished and working-draft quality genomic sequences, a data set representative of the typical challenges of sequence-based SNP discovery.
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We thank T. Blackwell and S. Eddy for informative discussions during the development of the mathematical framework of the technique. This work was supported by NIH grants P50HG01458 (L.H. and W.R.G.), R01HG1720 (P.-Y.K.) and T32AR07284 (Z.G.), and an equipment loan from Compaq Computer Corporation.
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