Test Data for Long-Read Proteogenomics Workflow on Jurkat Samples

Test data for a long-read proteogenomics approach integrating matched long-read RNA-seq and mass spectrometry proteomics data. The data was released by Rachel Miller of the University of Wisconsin–Madison to accompany an open-source Nextflow pipeline for isoform-resolved analysis. It is intended for validating a workflow that enhances the characterization of protein isoforms.

Use Cases

• Benchmarking protein isoform classification schemes based on integrated long-read transcriptome and proteomic data.
• Testing protein inference algorithms that directly incorporate long-read sequencing data.
• Validating workflows for discovering novel protein isoforms that are difficult to detect with mass spectrometry alone.

Strengths

• Data is matched, integrating long-read RNA-seq and mass spectrometry proteomics from the same sample.
• Accompanies a released open-source Nextflow pipeline, providing a reproducible analysis framework.
• Focuses on Jurkat cell line samples, a common model system in biomedical research.

Limitations

• Row count and dataset size are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

University of Wisconsin–Madison

Collection Method

Likely generated from long-read RNA sequencing (e.g., PacBio, Oxford Nanopore) and mass spectrometry-based proteomics of Jurkat cell samples.