Oarfish: Enhanced probabilistic modeling leads to improved accuracy in long read transcriptome quantification

Long read sequencing technology is becoming an increasingly indispensable tool in genomic and transcriptomic analysis. In transcriptomics in particular, long reads offer the possibility of sequencing full-length isoforms, which can vastly simplify the identification of novel transcripts and transcript quantification. However, despite this promise, the focus of much long read method development to date has been on transcript identification, with comparatively little attention paid to quantification. Yet, due to differences in the underlying protocols and technologies, lower throughput (i.e. fewer reads sequenced per sample compared to short read technologies), as well as technical artifacts, long read quantification remains a challenge, motivating the continued development and assessment of quantification methods tailored to this increasingly prevalent type of data.

We introduce a new method and software tool for long read transcript quantification called oarfish. Our model incorporates a novel and innovative coverage score, which affects the conditional probability of fragment assignment in the underlying probabilistic model. We demonstrate that by accounting for this coverage information, oarfish is able to produce more accurate quantification estimates than existing long read quantification methods, particularly when one considers the primary isoforms present in a particular cell line or tissue type.

Oarfish is implemented in the Rust programming language, and is made available as free and open-source software under the BSD 3-clause license. The source code is available at https://www.github.com/COMBINE-lab/oarfish.