A direct comparison of genome alignment and transcriptome pseudoalignment

Yi, Lynn; Liu, Lauren; Melsted, Páll; Pachter, Lior

doi:10.1101/444620

Cited by 15 publications

(18 citation statements)

References 30 publications

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“…While it has been previously reported [42] that pseudoalignment to the transcriptome results in comparable quantification accuracy to alignment to the genome, the analyses performed in this manuscript suggest that alignment to the transcriptome, lightweight mapping to the transcriptome, and alignment to the genome yield quantification results that are sometimes markedly different. There are a few reasons that the analyses carried out in this paper lead to a different conclusion on this question.…”

Section: Discussionmentioning

confidence: 54%

Alignment and mapping methodology influence transcript abundance estimation

et al. 2020

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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.

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Section: Discussionmentioning

confidence: 54%

Alignment and mapping methodology influence transcript abundance estimation

et al. 2020

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“…Since detailed base-pair alignment is not necessary to generate a count matrix, pseudoalignment to a reference transcriptome 8 suffices. Moreover, pseudoalignment has been shown to be highly concordant with alignment for the purposes of quantification in bulk RNA-seq 15 . To test this hypothesis we compared counts obtained by pseudoalignment using the kallisto program 8 with counts produced via Cell Ranger which is based on the STAR aligner 16 .…”

Section: Resultsmentioning

confidence: 95%

Modular and efficient pre-processing of single-cell RNA-seq

Melsted

Booeshaghi

Gao

et al. 2019

Preprint

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102

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Analysis of single-cell RNA-seq data begins with the pre-processing of reads to generate count matrices. We investigate algorithm choices for the challenges of pre-processing, and describe a workflow that balances efficiency and accuracy. Our workflow is based on the kallisto and bustools programs, and is near-optimal in speed and memory. The workflow is modular, and we demonstrate its flexibility by showing how it can be used for RNA velocity analyses.

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“…Second, while lightweight mapping to the transcriptome and alignment to the genome do yield different quantification results, we also considered traditional alignment to the transcriptome, expanding upon the different common approaches that are taken when aligning reads prior to transcript quantification. Finally, Yi et al 38 preprocess both alignments and pseudoalignments into equivalence-class counts (the count of fragments deemed compatible with different subsets of transcripts). Then, from these reduced statistics, abundance estimation is performed.…”

Section: Discussionmentioning

confidence: 99%

Alignment and mapping methodology influence transcript abundance estimation

Srivastava

Malik

Sarkar

et al. 2019

Preprint

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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.

show abstract

A direct comparison of genome alignment and transcriptome pseudoalignment

Cited by 15 publications

References 30 publications

Alignment and mapping methodology influence transcript abundance estimation

Alignment and mapping methodology influence transcript abundance estimation

Modular and efficient pre-processing of single-cell RNA-seq

Alignment and mapping methodology influence transcript abundance estimation

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