Biosurfer for systematic tracking of regulatory mechanisms leading to protein isoform diversity

Abstract

Long-read RNA-seq has shed light on transcriptomic complexity, but questions remain about the functionality of downstream protein products. We introduce Biosurfer, a computational approach for comparing protein isoforms, while systematically tracking the transcriptional, splicing, and translational variations that underlie differences in the sequences of the protein products. Using Biosurfer, we analyzed the differences in 35,082 pairs of GENCODE annotated protein isoforms, finding a majority (70%) of variable N-termini are due to the alternative transcription start sites, while only 9% arise from 5′ UTR alternative splicing (AS). Biosurfer's detailed tracking of nucleotide-to-residue relationships helps reveal an uncommonly tracked source of single amino acid residue changes arising from the codon splits at junctions. For 17% of internal sequence changes, such split codon patterns lead to single residue differences, termed “ragged codons.” Of variable C-termini, 72% involve splice- or intron retention-induced reading frameshifts. We systematically characterize an unusual pattern of reading frame changes, in which the first frameshift is closely followed by a distinct second frameshift that restores the original frame, which we term a “snapback” frameshift. We analyze the long-read RNA-seq-predicted proteome of a human cell line and find similar trends as compared to our GENCODE analysis, with the exception of a higher proportion of transcripts predicted to undergo nonsense-mediated decay. Biosurfer's comprehensive characterization of long-read RNA-seq data sets should accelerate insights of the functional role of protein isoforms, providing mechanistic explanation of the origins of the proteomic diversity driven by the AS. Biosurfer is available as a Python package.