SNIT: SNP identification for strain typing

Rapid and accurate identification of an infectious agent is of the utmost importance for the surveillance and treatment of infectious diseases. Traditionally, strain typing has been performed using assays that probe a few previously known polymorphic loci [1]. However, due to the inherent limitations of using only a few loci, these methods offer low specificity.Because of the rapid decrease in costs of genome sequencing, strain typing can now be performed in silico by first sequencing the sample, and then comparing the genome sequence with other available genomes of the same species to identify the closest strain. This approach has the potential to offer a much higher specificity because it uses the entire genome rather than a few pre-selected loci. Moreover, a comprehensive listing of all polymorphisms in a newly sequenced genome might also be useful in predicting the virulence or pathogenicity of the new strain.Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variation. Many previous methods have used "in-house" pipelines to identify and catalog the SNPs between pathogens of the same species [2,3]. However, with the exception of SNPsFinder [4] and inGAP [5], these pipelines are seldom publicly available. The SNPsFinder pipeline is a Web-based application that requires users to upload the genome sequences that need to be compared, which might be time consuming when a large number of genomes are involved. In addition, the use of a public server is not desirable if confidentiality of the data is a concern. The inGAP pipeline provides many useful functionalities for the analysis of next-generation sequencing data, however, the SNP identification routines do not scale well with the number of genomes because of their reliance on multiple sequence alignments. In our comparative investigation, inGAP successfully produced SNPs for four Shigella flexneri genomes, but repeatedly crashed when run for seven Burkholderia mallei genomes (the Results S