GateKeeper-GPU: Fast and Accurate Pre-Alignment Filtering in Short Read Mapping

TL;DR

GateKeeper-GPU is a GPU-accelerated pre-alignment filtering tool that significantly speeds up short read mapping by reducing the need for costly sequence alignments, achieving up to 2.9x faster alignment and 1.4x faster overall mapping.

Contribution

It enhances filtering accuracy over previous methods and leverages GPU parallelism to accelerate the pre-alignment filtering process in short read mapping.

Findings

2.9x acceleration of sequence alignment

Up to 1.4x speedup in end-to-end read mapping

Improved filtering accuracy over prior methods

Abstract

At the last step of short read mapping, the candidate locations of the reads on the reference genome are verified to compute their differences from the corresponding reference segments using sequence alignment algorithms. Calculating the similarities and differences between two sequences is still computationally expensive since approximate string matching techniques traditionally inherit dynamic programming algorithms with quadratic time and space complexity. We introduce GateKeeper-GPU, a fast and accurate pre-alignment filter that efficiently reduces the need for expensive sequence alignment. GateKeeper-GPU provides two main contributions: first, improving the filtering accuracy of GateKeeper (a lightweight pre-alignment filter), and second, exploiting the massive parallelism provided by the large number of GPU threads of modern GPUs to examine numerous sequence pairs rapidly and concurrently. By reducing the work, GateKeeper-GPU provides an acceleration of 2.9x to sequence alignment and up to 1.4x speedup to the end-to-end execution time of a comprehensive read mapper (mrFAST). GateKeeper-GPU is available at https://github.com/BilkentCompGen/GateKeeper-GPU.