Accurate spliced alignment of long RNA sequencing reads.

Kristoffer Sahlin,Veli Mäkinen

doi:10.1093/bioinformatics/btab540

Abstract

MotivationLong-read RNA sequencing technologies are establishing themselves as the primary techniques to detect novel isoforms, and many such analyses are dependent on read alignments. However, the error rate and sequencing length of the reads create new challenges for accurately aligning them, particularly around small exons.ResultsWe present an alignment method uLTRA for long RNA sequencing reads based on a novel two-pass collinear chaining algorithm. We show that uLTRA produces higher accuracy over state-of-the-art aligners with substantially higher accuracy for small exons on simulated and synthetic data. On simulated data, uLTRA achieves an accuracy of about 60% for exons of length 10 nucleotides or smaller and close to 90% accuracy for exons of length between 11 and 20 nucleotides. On biological data where true read location is unknown, we show several examples where uLTRA aligns to known and novel isoforms containing small exons that are not detected with other aligners. While uLTRA obtains its accuracy using annotations, it can also be used as a wrapper around minimap2 to align reads outside annotated regions.Availabilityand implementationuLTRA is available at https://github.com/ksahlin/ultra.Supplementary information Supplementary data are available at Bioinformatics online.

Highlights

The transcriptome has been identified as an important link between DNA and phenotype and is analyzed in various biological and biomedical studies
Spliced alignment is a challenging computational problem, and a plethora of different alignment algorithms have been proposed for splice alignment of short-read RNA-seq, with some of the key algorithmic advances given in TopHat (Trapnell et al, 2009), STAR (Dobin et al, 2013), HISAT (Kim et al, 2015), GMAP (Wu et al, 2016) and HISAT2 (Kim et al, 2019)
We have presented a novel splice alignment algorithm, and its implementation uLTRA. uLTRA aligns long transcriptomic reads to a genome using an annotation of coding regions

Summary

Introduction

The transcriptome has been identified as an important link between DNA and phenotype and is analyzed in various biological and biomedical studies. For these analyses, RNA sequencing has established itself as the primary experimental method. Some of the most common transcriptome analyses using RNA sequencing data include predicting and detecting isoforms and quantifying their abundance in the sample. These analyses are fundamentally underpinned by the alignment of reads to genomes. While short-read RNA sequencing has shown unprecedented insights into transcriptional complexities of various organisms, the readlength makes it difficult to detect isoforms with complicated splicing structure and limits quantification of isoform abundance (Zhang et al, 2017)

Results

Discussion

Conclusion