GenomeProt: User friendly proteogenomics for canonical and non-canonical proteoform characterisation
Kore, H.; Gleeson, J.; Yin Wan, C.; De Paoli-Iseppi, R.; Dutt, M.; Prawer, Y. D. J.; Alkaraki, A.; Lonsdale, A.; Wells, C.; Smith, L.; Clark, M.; Parker, B.
Show abstract
Quantifying the diversity of RNAs and proteins produced by cells is fundamental to the biological and clinical sciences. However, many RNAs and proteins remain uncharacterised, especially proteins translated from alternate RNA isoforms; untranslated regions of mRNAs and non-coding RNAs, as well as the effects of DNA variation on protein sequences. Proteogenomics aims to characterise the complete proteome by integrating genomics and/or transcriptomics with proteomics, but current tools have limitations in useability, analysis features and visualisation of resulting data. To address these gaps, we developed GenomeProt, a user-friendly GUI-based tool for integrative proteogenomic analysis. We demonstrate its utility by integrating long-read RNA sequencing with mass-spectrometry-based proteomics to pinpoint proteoform expression generated by alternative splicing; discover novel, unannotated proteins in human brain samples; and quantify variant-containing peptides associated with treatment resistance in a melanoma xenograft model. GenomeProt brings the discovery power of proteogenomics to biologists, illuminating the hidden proteome.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A computational platform for high-throughput analysis of RNA sequences and modifications by mass spectrometry 96%
- The Integration of Proteogenomics and Ribosome Profiling Circumvents Key Limitations to Increase the Coverage and Confidence of Novel Microproteins 94%
- An adaptive, continuous-learning framework for clinical decision-making from proteome-wide biofluid data 94%
Similar papers in this journal
- The Personalized Proteome: Comparing Proteogenomics and Open Variant Search Approaches for Single Amino Acid Variant Detection 94%
- Bridging Simplicity and Depth in Single-Cell Proteomics: A Cost-Effective Workflow and Expanded Framework for Data Evaluation 93%
- Machine learning on large-scale proteomics data identifies tissue- and cell type-specific proteins 93%
Similar papers in this journal
Similar papers in this journal
- ProteoDisco: A flexible R approach to generate customized protein databases for extended search space of novel and variant proteins in proteogenomic studies 93%
- Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides. 93%
- PROTRIDER: Protein abundance outlier detection from mass spectrometry-based proteomics data with a conditional autoencoder 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.