Bias correction and reference hashing for accurate RNA-seq quantification
Channel: Michael Love
Date: 2021-05-02

Background: Quantification of transcript abundance is a critical task in RNA-seq data analysis. While much progress has been made improving speed of estimation, sample-specific technical biases and genetic variation in sample transcript sequence nevertheless hinder accurate estimates of abundance. Additionally, the provenance of the reference transcripts used for quantification is not always documented, which can impede computational reproducibility and downstream re-use of quantification data by other groups.

Results: We describe new methods in the Salmon, Alevin, and Tximeta software packages, allowing for fast and accurate quantification of bulk and single cell RNA-seq: (1) sample-specific bias modeling accounting for the most common technical artifacts seen in large RNA-seq datasets including GEUVADIS and SEQC; (2) reference sequence hashing and subsequent lookup, facilitating data sharing and computational reproducibility. Due to time, mapping validation in Salmon will not be covered, which is described in a recent publication by Srivastava et al (2020).

Conclusion: Our proposed methods for bias correction, mapping validation, and reference hashing provide a fast and easy-to-use pipeline for quantification of bulk and single-cell RNA-seq. Our pipeline can correctly identify expressed isoforms across datasets despite sample-specific biases, can provide equally accurate estimation in presence of genetic variation to methods that compute full alignments, and can provide post-hoc identification of reference sequence provenance and automatic association of correct annotation metadata.

Availability: Tximeta is available within Bioconductor at https://bioconductor.org/packages/tximeta

Slides: https://bit.ly/fragbias and https://bit.ly/tximeta