Enhanced Protein Isoform Characterization Through Long-Read Proteogenomics - Jurkat Samples and Reference Data
Creators
- 1. University of Wisconsin - Madison
- 2. University of Virginia
- 3. Lifebit Biotech Ltd.
- 4. University of Zurich
- 5. University of Florida
- 6. Science and Technology Consulting LLC
Description
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, Oxford Nanopore) provide full-length transcript sequencing, which can be used to predict full-length proteins. Here, we describe a long-read proteogenomics approach for integrating matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discovery novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data in protein inference to enable detection of protein isoforms that are intractable to MS detection. We have released an open-source Nextflow pipeline that integrates long-read RNA-sequencing in a proteomic workflow for isoform-resolved analysis.
Companion Repositories:
- Long-Read-Proteogenomics Workflow GitHub Repository Release
- Long-Read-Proteogenomics Analysis GitHub Repository Release
Companion Datasets
- TEST Data for Long-Read-Proteogenomics Workflow GitHub Actions
- Long-Read-Proteogenomics Workflow Results using Jurkat Sample data
This Repository contains the Jurkat Samples and Reference Data
Files
jurkat_classification.txt
Files
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