Reference Mapping Considering Swaps of Adjacent Bases

Youngho Kim,Soo Jun Park,Munseong Kang,Dae Woong Kang,Ju-Hui Jeong,Jeong Seop Sim

doi:10.3390/app11115038

Abstract

Since the time of the HGP, research into next-generation sequencing, which can reduce the cost and time of sequence analysis using computer algorithms, has been actively conducted. Mapping is a next-generation sequencing method that identifies sequences by aligning short reads with a reference genome for which sequence information is known. Mapping can be applied to tasks such as SNP calling, motif searches, and gene identification. Research on mapping that utilizes BWT and GPU has been undertaken in order to obtain faster mapping. In this paper, we propose a new mapping algorithm with additional consideration for base swaps. The experimental results demonstrate that when the penalty score for swaps was −1, −2, and −3 in paired-end alignment, for the human whole genome, SOAP3-swap aligned 4667, 2318, and 972 more read pairs, respectively, than SOAP3-dp, and for the drosophila genome, SOAP3-swap aligned 1253, 454, and 129 more read pairs, respectively, than SOAP3-dp. SOAP3-swap has the same functionality as that of SOAP3-dp and also improves the alignment ratio by taking biologically significant swaps into account for the first time.

Highlights

Since the time of the Human Genome Project (HGP) [1,2], next-generation sequencing, which can reduce the cost and time of sequence analysis using computer algorithms, has been a very active area of research [3,4]
The data used in these experiments were the human genome sequence and the drosophila genome sequence
In order to examine the performance and effectiveness of SOAP3-swap, the algorithm was compared to SOAP3-dp, SOAP3, CUSHAW2-graphics processing units (GPUs), BarraCUDA, Burrows–Wheeler aligner (BWA), Bowtie2, and CUSHAW2

Summary

Introduction

Since the time of the Human Genome Project (HGP) [1,2], next-generation sequencing, which can reduce the cost and time of sequence analysis using computer algorithms, has been a very active area of research [3,4]. Mapping is a next-generation sequencing method that identifies sequences by aligning short reads with a reference genome for which sequence information is known. Bowtie [7] performs mapping based on the Ferragina and Manzini’s algorithm [11] and takes mismatches into account. The Burrows–Wheeler aligner (BWA) [8] first generates a prefix trie for the reference sequence and performs mapping, taking mismatches and gaps into account with the use of a top-down traversal method. SOAP2 [9] considers mismatches and gaps using a bi-directional BWT search (or 2way-BWT search) [12] and the Smith–Waterman algorithm [13], and it reduces space usage through a sampled suffix array. Tophat2 [14], considering very large deletions, inversions on the same chromosome and translocations involving different chromosomes were introduced

Methods

Results

Discussion

Conclusion