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

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.

SUBMITTER: Salari F

PROVIDER: S-EPMC6220446 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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

Salari Farzaneh F Zare-Mirakabad Fatemeh F Sadeghi Mehdi M Rokni-Zadeh Hassan H

BMC bioinformatics 20181106 1

<h4>Background</h4>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.<h4>Results</h4>In this paper, o ...[more]

PMID: 30400807

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Project description:MotivationIllumina Sequencing data can provide high coverage of a genome by relatively short (most often 100 bp to 150 bp) reads at a low cost. Even with low (advertised 1%) error rate, 100 × coverage Illumina data on average has an error in some read at every base in the genome. These errors make handling the data more complicated because they result in a large number of low-count erroneous k-mers in the reads. However, there is enough information in the reads to correct most of the sequencing errors, thus making subsequent use of the data (e.g. for mapping or assembly) easier. Here we use the term "error correction" to denote the reduction in errors due to both changes in individual bases and trimming of unusable sequence. We developed an error correction software called QuorUM. QuorUM is mainly aimed at error correcting Illumina reads for subsequent assembly. It is designed around the novel idea of minimizing the number of distinct erroneous k-mers in the output reads and preserving the most true k-mers, and we introduce a composite statistic π that measures how successful we are at achieving this dual goal. We evaluate the performance of QuorUM by correcting actual Illumina reads from genomes for which a reference assembly is available.ResultsWe produce trimmed and error-corrected reads that result in assemblies with longer contigs and fewer errors. We compared QuorUM against several published error correctors and found that it is the best performer in most metrics we use. QuorUM is efficiently implemented making use of current multi-core computing architectures and it is suitable for large data sets (1 billion bases checked and corrected per day per core). We also demonstrate that a third-party assembler (SOAPdenovo) benefits significantly from using QuorUM error-corrected reads. QuorUM error corrected reads result in a factor of 1.1 to 4 improvement in N50 contig size compared to using the original reads with SOAPdenovo for the data sets investigated.AvailabilityQuorUM is distributed as an independent software package and as a module of the MaSuRCA assembly software. Both are available under the GPL open source license at http://www.genome.umd.edu.Contactgmarcais@umd.edu.

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

Publications

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

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