Assessing the impact of exact reads on reducing the error rate of read mapping

BMC Bioinformatics. 2018 Nov 6;19(1):406. doi: 10.1186/s12859-018-2432-7.

Abstract

Background: Nowadays, according to valuable resources of high-quality genome sequences, reference-based assembly methods with high accuracy and efficiency are strongly required. Many different algorithms have been designed for mapping reads onto a genome sequence which try to enhance the accuracy of reconstructed genomes. In this problem, one of the challenges occurs when some reads are aligned to multiple locations due to repetitive regions in the genomes.

Results: In this paper, our goal is to decrease the error rate of rebuilt genomes by resolving multi-mapping reads. To achieve this purpose, we reduce the search space for the reads which can be aligned against the genome with mismatches, insertions or deletions to decrease the probability of incorrect read mapping. We propose a pipeline divided to three steps: ExactMapping, InExactMapping, and MergingContigs, where exact and inexact reads are aligned in two separate phases. We test our pipeline on some simulated and real data sets by applying some read mappers. The results show that the two-step mapping of reads onto the contigs generated by a mapper such as Bowtie2, BWA and Yara is effective in improving the contigs in terms of error rate.

Conclusions: Assessment results of our pipeline suggest that reducing the error rate of read mapping, not only can improve the genomes reconstructed by reference-based assembly in a reasonable running time, but can also have an impact on improving the genomes generated by de novo assembly. In fact, our pipeline produces genomes comparable to those of a multi-mapping reads resolution tool, namely MMR by decreasing the number of multi-mapping reads. Consequently, we introduce EIM as a post-processing step to genomes reconstructed by mappers.

Keywords: Multi-mapping reads; Read mapping; Reference-based assembly.

MeSH terms

  • Algorithms*
  • Chromosome Mapping
  • Computational Biology / methods*
  • Escherichia coli / genetics*
  • Escherichia coli Proteins / genetics*
  • Genome, Bacterial*
  • High-Throughput Nucleotide Sequencing / methods*
  • Humans
  • Sequence Analysis, DNA / methods
  • Software

Substances

  • Escherichia coli Proteins