QAlign: Aligning nanopore reads accurately using current-level modeling

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 an efficient aligner.

In this paper, 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 nucleobase reads into discretized current levels that capture the error modes of the nanopore sequencer before running it through a sequence aligner.

We show that QAlign improves alignment rates from around 80% to 90% with nanopore reads when aligning to the genome. We also show that QAlign improves the average overlap quality by 9.2%,2.5% and 10.8% in three datasets for read-to-read alignment. Read to transcriptome alignment rates are improved from 50.8% to 86.3% and 82.3% to 95.3% in two datasets.