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Miller, Rachel M; Jordan, Ben T; Mehlferber, Madison M; Jeffery, Erin D; Chatzipantsiou, Christina; Kaur, Simi; Millikin, Robert J; Dai, Yunxiang; Tiberi, Simone; Castaldi, Peter J; Shortreed, Michael R; Luckey, Chance John; Conesa, Ana; Smith, Lloyd M; Deslattes Mays, Anne; Sheynkman, Gloria M
Genome Biology, 03/2022, Letnik: 23, Številka: 1Journal Article
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.
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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