QAlign: aligning nanopore reads accurately using current-level modeling
Author(s) -
Dhaivat Joshi,
Shunfu Mao,
Sreeram Kannan,
Suhas Diggavi
Publication year - 2020
Publication title -
bioinformatics
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btaa875
Subject(s) - nanopore sequencing , computer science , nanopore , sequence assembly , reference genome , pipeline (software) , dna sequencing , noise (video) , genome , software , process (computing) , alignment free sequence analysis , data mining , computational biology , transcriptome , sequence alignment , artificial intelligence , dna , gene , genetics , biology , gene expression , materials science , image (mathematics) , programming language , nanotechnology , operating system , peptide sequence
Efficient and accurate alignment of DNA/RNA sequence reads to each other or to a reference genome/transcriptome is an important problem in genomic analysis. Nanopore sequencing has emerged as a major sequencing technology and many long-read aligners have been designed for aligning nanopore reads. However, the high error rate makes accurate and efficient alignment difficult. Utilizing the noise and error characteristics inherent in the sequencing process properly can play a vital role in constructing a robust aligner. In this article, we design QAlign, a pre-processor that can be used with any long-read aligner for aligning long reads to a genome/transcriptome or to other long reads. The key idea in QAlign is to convert the nucleotide reads into discretized current levels that capture the error modes of the nanopore sequencer before running it through a sequence aligner.
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