Alignment and mapping methodology influence transcript abundance estimation

Abstract

Background: The accuracy of transcript quantification using RNA-seq data depends on many factors, such as the choice of alignment or mapping method and the quantification model being adopted. While the choice of quantification model has been shown to be important, considerably less attention has been given to comparing the effect of various read alignment approaches on quantification accuracy.Results: We investigate the influence of mapping and alignment on the accuracy of transcript quantification in both simulated and experimental data, as well as the effect on subsequent differential expression analysis. We observe that, even when the quantification model itself is held fixed, the effect of choosing a different alignment methodology, or aligning reads using different parameters, on quantification estimates can sometimes be large, and can affect downstream differential expression analyses as well. These effects can go unnoticed when assessment is focused too heavily on simulated data, where the alignment task is often simpler than in experimentally-acquired samples. We also introduce a new alignment methodology, called selective alignment, to overcome the shortcomings of lightweight approaches without incurring the computational cost of traditional alignment.Conclusion: We observe that, on experimental datasets, the performance of lightweight mapping and alignment-based approaches varies significantly and highlight some of the underlying factors. We show this variation both in terms of quantification and downstream differential expression analysis. In all comparisons, we also show the improved performance of our proposed selective alignment method and suggest best practices for performing RNA-seq quantification. * Contributed equally.

Preprocessing choices affect RNA velocity results for droplet scRNA-seq data

“…an index for Salmon , considering the transcripts as the features of interest and providing the introns as decoy sequences (Srivastava et al 2019b)…”

“…In addition to the indices based on transcripts and introns, we built one Salmon index from the original Gencode FASTA file with the annotated transcripts, and one Salmon index from a FASTA file combining the annotated transcripts and fully unspliced versions of all transcripts. For all Salmon indices, the complete genome sequence was provided as a decoy sequence (Srivastava et al 2019b), with the aim to exclude reads coming from intergenic regions of the genome. Across data sets and reference specifications, this excluded between 1 and 2.5% of the reads from the quantification.…”

Read Mapping and Transcript Assembly: A Scalable and High-Throughput Workflow for the Processing and Analysis of Ribonucleic Acid Sequencing Data

“…As an alternative to genome-guided read mapping and transcript assembly, RMTA also allows for read alignment directly to a transcriptome using the quasi-aligner and transcript abundance quantifier Salmon (Patro et al, 2017;Srivastava et al, 2019). Minimum input for Salmon includes a reference transcriptome (in FASTA format) and then RNA-seq reads (as above).…”

Effects of High-Dose Ionizing Radiation in Human Gene Expression: A Meta-Analysis

“…Reads were directly mapped into Homo sapiens reference genome and transcriptome FASTA-formatted sequences. To this end, we used the latest release of Salmon (version 1.1.0) [47] which adopts a selective-alignment algorithm in order to overcome the shortcomings of lightweight approaches, without the additional computational burden of traditional alignment [68]. We produced the transcriptome index for Salmon via the partial selective alignment method, mapping the transcriptome to the genome, extracting the relevant portion out of the genome, and, finally, indexing it along with the transcriptome.…”