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bioRxiv · 10.1101/017194

Index-based map-to-sequence alignment in large eukaryotic genomes

Davide Verzotto·Axel M Hillmer·Audrey S M Teo·Niranjan Nagarajan
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Abstract

Resolution of complex repeat structures and rearrangements in the assembly and analysis of large eukaryotic genomes is often aided by a combination of high-throughput sequencing and mapping technologies (e.g. optical restriction mapping). In particular, mapping technologies can generate sparse maps of large DNA fragments (150 kbp-2 Mbp) and thus provide a unique source of information for disambiguating complex rearrangements in cancer genomes. Despite their utility, combining high-throughput sequencing and mapping technologies has been challenging due to the lack of efficient and freely available software for robustly aligning maps to sequences. Here we introduce two new map-to-sequence alignment algorithms that efficiently and accurately align high-throughput mapping datasets to large, eukaryotic genomes while accounting for high error rates. In order to do so, these methods (OPTIMA for glocal and OPTIMA-Overlap for overlap alignment) exploit the ability to create efficient data structures that index continuous-valued mapping data while accounting for errors. We also introduce an approach for evaluating the significance of alignments that avoids expensive permutation-based tests while being agnostic to technology-dependent error rates. Our benchmarking results suggest that OPTIMA and OPTIMA-Overlap outperform state-of-the-art approaches in sensitivity (1.6-2x improvement) while simultaneously being more efficient (170-200%) and precise in their alignments (99% precision). These advantages are independent of the quality of the data, suggesting that our indexing approach and statistical evaluation are robust and provide improved sensitivity while guaranteeing high precision.

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Davide Verzotto, Axel M Hillmer, Audrey S M Teo, Niranjan Nagarajan. 2015-03-27. Index-based map-to-sequence alignment in large eukaryotic genomes. https://doi.org/10.1101/017194

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