Abstract
With the remarkable development in inexpensive sequencing technologies and supporting computational tools, we have the promise of medicine being personalized by knowledge of the individual genome. Current technologies provide high throughput, but short reads. Reconstruction of the donor genome is based either on de novo assembly of the (short) reads, or on mapping donor reads to a standard reference. While such techniques demonstrate high success rates for inferring 'simple' genomic segments, they are confounded by segments with complex duplication patterns, including regions of direct medical relevance, like the HLA and the KIR regions.In this work, we address this problem with a method for assessing the quality of a predicted genome sequence for complex regions of the genome. This method combines two natural types of evidence: sequence similarity of the mapped reads to the predicted donor genome, and distribution of reads across the predicted genome. We define a new scoring function for read-to-genome matchings, which penalizes for sequence dissimilarities and deviations from expected read location distribution, and present an efficient algorithm for finding matchings that minimize the penalty. The algorithm is based on a formal problem, first defined in this paper, called Coverage Sensitive many-to-many min-cost bipartite Matching (CSM). This new problem variant generalizes the standard (one-to-one) weighted bipartite matching problem, and can be solved using network flows. The resulting Java-based tool, called SAGE (Scoring function for Assembled GEnomes), is freely available upon request. We demonstrate over simulated data that SAGE can be used to infer correct haplotypes of the highly repetitive KIR region on the Human chromosome 19.
Highlights
The inexorable drop in costs and rise in throughput of DNA sequencing is driving a future in which every individual person will have their genome sequenced, perhaps multiple times in their lifetimes [1]
The difficulty even increases for diploid genomes, limiting the investigation of many important genomic regions, such as the killer cell immunoglobulin like receptor (KIR) region, the 3.6Mbp Human Leucocyte Antigen
We show that our scoring function allows us to correctly identify KIR haplotype templates in diploid genomes, differentiating correct predictions form incorrect ones based on their computed score, while the naive approach fails in many cases to predict the correct template
Summary
The inexorable drop in costs and rise in throughput of DNA sequencing is driving a future in which every individual person will have their genome sequenced, perhaps multiple times in their lifetimes [1]. If the prediction is the correct genomic sequence from which the reads were extracted, and this region is non-repetitive, it is expected that a mapping would assign to each read a unique position that is the true position from which it was extracted. Given a mapping between the reads and the region, we define a read-to-genome matching as a function that selects for each read at most one corresponding position among its set of positions given by the mapping, from which it was presumably extracted. For a segment y Î Y, it is possible to compute the expected coverage cy of y, and generate a convex score function f(i) whose minimum point is at i = cy, and set wc (y, i) = f (i) for every nonnegative integer i. The inputs to SAGE are a set of reads, R, mapped to a genomic template, G, in the BAM format [16] along with a parameter file containing alignment costs, unmatched read penalty, genome segmentation, expected segment coverage values, and a choice of coverage cost functions (currently linear and polynomial cost functions)
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