RT Journal Article SR Electronic T1 HapCHAT: Adaptive haplotype assembly for efficiently leveraging high coverage in long reads JF bioRxiv FD Cold Spring Harbor Laboratory SP 170225 DO 10.1101/170225 A1 Stefano Beretta A1 Murray Patterson A1 Simone Zaccaria A1 Gianluca Della Vedova A1 Paola Bonizzoni YR 2017 UL http://biorxiv.org/content/early/2017/07/30/170225.abstract AB Motivation Haplotype assembly is the process of reconstructing the haplotypes of an individual from sequencing reads. Computational methods for this problem have shown to achieve high accuracy on long reads, which are becoming cheaper to produce and more widely available. Larger amounts of data, usually originating from increased coverage, are highly beneficial for improving the quality of the detection of the genetic variations that are intrinsic to the diploid nature of the human genome. However, the high accuracy of such methods comes at a cost of computational resources. The increased error rates that affect all current long-read technologies require even higher coverage: making the analysis of such data the key computational task to be solved in order to improve the accuracy of the predictions made by haplotype assembly methods.Results We propose a new computational approach for assembling haplotypes that is specifically designed to cope with a different error rate at each variant site, while minimizing the total number of these corrections necessary for a feasible solution. The complete strategy has been implemented in HapCHAT: Haplotype Assembly Coverage Handling by Adapting Thresholds. An experimental analysis on sequencing reads with up to 60 × coverage reveals accuracy improvements with increasing coverage. Moreover, despite the fact that this adaptive approach is slightly heuristic, the extent of its efficiency over current state-of-the-art long-read haplotype assembly methods allows the leveraging of higher enough coverage that it pays of in terms of accuracy prediction, while decreasing the running time.Availability HapCHAT is available at https://hapchat.algolab.eu under the GPL license.Contact murray.patterson{at}unimib.it, bonizzoni{at}disco.unimib.it