Length biases in single-cell RNA sequencing of pre-mRNA

Gennady Gorin; Lior Pachter

Length biases in single-cell RNA sequencing of pre-mRNA

2023

Gennady Gorin | Lior Pachter

Single-cell RNA sequencing data can be modeled using Markov chains to yield genome-wide insights into transcriptional physics. However, quantitative inference with such data requires careful assessment of noise sources. We find that long pre-mRNA transcripts are over-represented in sequencing data. To explain this trend, we propose a length-based model of capture bias, which may produce false-positive observations. We solve this model and use it to find concordant parameter trends as well as systematic, mechanistically interpretable technical and biological differences in paired data sets.

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