ComB: SNP Calling and Mapping Analysis for Color and Nucleotide Space Platforms

The determination of single nucleotide polymorphisms (SNPs) has become faster and more cost effective since the advent of short read data from next generation sequencing platforms such as Roche's 454 Sequencer, Illumina's Solexa platform, and Applied Biosystems SOLiD sequencer. The SOLiD sequencing platform, which is capable of producing more than 6 GB of sequence data in a single run, uses a unique encoding scheme where color reads represent transitions between adjacent nucleotides. The determination of SNPs from color reads usually involves the translation of color alignments to likely nucleotide strings to facilitate the use of tools designed for nucleotide reads. This technique results in the loss of significant information in the color read, producing many incorrect SNP calls, especially if regions exist with dense or adjacent polymorphism. Additionally, color reads align ambiguously and incorrectly more often than nucleotide reads making integrated SNP calling a difficult challenge. We have developed ComB, a SNP calling tool which operates directly in color space, using a Bayesian model to incorporate unique and ambiguous reads to iteratively determine SNP identity. ComB is capable of accurately calling short consecutive nucleotide polymorphisms and densely clustered SNPs; both of which other SNP calling tools fail to identify. ComB, which is capable of using billions of short reads to accurately and efficiently perform whole human genome SNP calling in parallel, is also capable of using sequence data or even integrating sequence and color space data sets. We use real and simulated data to demonstrate that ComB's iterative strategy and recalibration of quality scores allow it to discover more true SNPs while calling fewer false positives than tools which use only color alignments as well as tools which translate color reads to nucleotide strings.