Software tools for proteome analysis
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Computational workflows has become indispensable for the analysis of large-scale proteomics experiments. Accordingly, our lab has developed several R and Python software tools to enable specialized proteomics applications, some of which are highlighted below.
JCAST (Junction Centric Alternative Splicing Translator)
JCAST is a Python tool to perform in silico translation of alternative splicing transcripts into custom protein sequences. Developed in collaboration with Dr. Maggie Lam's lab, JCAST is a software tool that allows users to take RNA sequencing data to produce custom protein sequence databases, in order to facilitate the identification of alternative splicing derived proteoforms in mass spectrometry experiments. As illustrated in the figure, JCAST takes in RNA sequencing data processed by an upstream STAR/rMATS workflow and produces protein sequence databases in FASTA formats that can be used in bottom-up proteomics database search workflows.Splice junctions in the cell are thought to follow a bimodal distribution of total junction read counts, with the low abundance junctions thought to be less likely translatable into functional polypeptide chains. The current version of JCAST implements read count modeling using a Gamma/Gaussian mixture model to discard low-abundance splice junctions and limit database size. This software tool has been used to identify alternative splicing derived proteoforms in various human tissues as well as during human induced pluripotent stem cell differentiation.
A new publication from our lab on Software Impact describes the current version of JCAST (v.0.3.3). See here for the JCAST software description paper published in Software Impacts. A reproducible repository is also given on CodeOcean.
Riana (Relative Isotope Abundance Analyzer)
In D2O labeling experiments, the rate constants and half-life of proteins are derived from the isotope incorporation signals in mass spectrometry data acquired at different labeling durations. To help with data analysis and comparisons of protein kinetics data across mouse tissues, our lab has developed an open-source Python software tool Riana (Relative Isotope Abundance Analyzer). Riana is compatible with multiple stable isotope labeling workflows including D2O and SILAC.
Working together with Prof. Rob Beynon's group at the University of Liverpoo, we have used Riana to perform a controlled comparison of heavy water vs amino acid labeling for measuring protein turnover rates across four different tissues (heart, liver, kidney, muscle) in the mouse.
For details of the label comparison study, please see our paper published in Molecular & Cellular Proteomics. A GitHub repository for Riana is available here.