Long-Read Proteogenomics Workflow Results for Jurkat Cell Isoform Analysis

Long-read proteogenomics integrates matched long-read RNA sequencing and mass spectrometry proteomics to characterize protein isoforms. The dataset contains the complete output from executing the open-source Nextflow workflow on Jurkat cell samples, including a 6.5 GB BAM file of full-length non-concatemer reads. This approach enables the discovery of novel protein isoforms and provides a classification scheme and inference algorithm for isoforms intractable to mass spectrometry alone.

Use Cases

• Benchmarking protein isoform classification schemes based on integrated long-read transcriptome and proteomic evidence.
• Developing novel protein inference algorithms that directly incorporate long-read sequencing data.
• Validating the detection of protein isoforms that are difficult to identify with mass spectrometry alone.
• Reproducing the analysis from the Long-Read Proteogenomics workflow for method verification.

Strengths

• Integrates two high-resolution data modalities: long-read RNA-seq and mass spectrometry-based proteomics.
• Includes a specific, large input file (jurkat.flnc.bam at 6.5 GB) providing full-length transcript data.
• Accompanies an open-source, reproducible Nextflow pipeline for isoform-resolved analysis.

Limitations

• Key metadata such as row count, column names, and specific file formats are unknown across all sources.
• Dataset size and structure details are sparse, complicating assessment of its scale and immediate usability.
• The last updated date is unknown, limiting assessment of data freshness.

Provenance

Source

University of Wisconsin–Madison

Collection Method

Generated by executing the Long-Read Proteogenomics workflow on Jurkat cell samples, integrating long-read RNA sequencing and mass spectrometry proteomics.