Enhanced protein isoform characterization through long-read proteogenomics.

The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g., PacBio or Oxford Nanopore) provides full-length transcripts which can be used to predict full-length protein isoforms. We describe here a long-read proteogenomics approach for integrating sample-matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data to enable detection of protein isoforms previously intractable to MS-based detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis. Our work suggests that the incorporation of long-read sequencing and proteomic data can facilitate improved characterization of human protein isoform diversity. Our first-generation pipeline provides a strong foundation for future development of long-read proteogenomics and its adoption for both basic and translational research. © 2022. The Author(s).

Citation

Rachel M Miller, Ben T Jordan, Madison M Mehlferber, Erin D Jeffery, Christina Chatzipantsiou, Simi Kaur, Robert J Millikin, Yunxiang Dai, Simone Tiberi, Peter J Castaldi, Michael R Shortreed, Chance John Luckey, Ana Conesa, Lloyd M Smith, Anne Deslattes Mays, Gloria M Sheynkman. Enhanced protein isoform characterization through long-read proteogenomics. Genome biology. 2022 Mar 03;23(1):69

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PMID: 35241129

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