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Incorporating a phosphopeptide enrichment step prior to LC-MS (liquid chromatography - mass spectrometry) enhances the sensitivity of detection for the treatment effect on the abundance of phosphorylated proteins (Cheng et al. 2018). However, the raw data are produced at such scale that quantitative data processing is quite cumbersome, so that very few labs are capable of analyzing phosphoproteome data without collaborations with laboratories specialized in the field or with core facilities at considerable cost.
To alleviate these bottlenecks, we have established Galaxy workflows comprising an existing MaxQuant wrapper and new wrappers for subsequent steps: (1) phosphorylation-site localization, (2) mapping of phosphopeptides to proteins and to known phosphorylation and substrate motifs, and (3) ANOVA. The process of wrapping preexisting scripts occasioned the review and improvement of the sequence-search algorithm's efficiency, substantially reducing the time required to execute the pipeline. An additional Kinome-Set Enrichment Analysis module is in development. This pipeline will be made available for deployment to almost any Galaxy instance allowing users at other institutions to analyze phosphoproteomic datasets.
Cheng, L. C., Li, Z., Graeber, T. G., Graham, N. A., & Drake, J. M. (2018). Phosphopeptide Enrichment Coupled with Label-free Quantitative Mass Spectrometry to Investigate the Phosphoproteome in Prostate Cancer. Journal of Visualized Experiments, (138). https://doi.org/10.3791/57996
Antimicrobial Resistance (AMR) is an increasing threat to our ability to treat common infectious diseases. Monitoring worldwide AMR is significantly aided by the use of genomic sequencing to help detect resistance genes and mutations and predict AMR to particular drugs. Searching for AMR-associated genomic features in sequence data can be difficult for large collections of genomes. The Galaxy platform provides a common interface for the analysis of genomics data, which can be leveraged to process large collections of genomes and prepare reports on predicted AMR.
We have developed the StarAMR pipeline to aid in the identification of AMR, plasmids, and the multi-locus sequence types of microbial genomes. StarAMR is a Python-based tool that makes use of the databases provided by the Center for Genomic Epidemiology (ResFinder, PointFinder, PlasmidFinder), PubMLST, and a custom gene-to-drug key. StarAMR uses BLAST to compare input nucleotide sequence data against these databases and reports on detected AMR determinants, predicted drug resistance, and quality assessment of the genomics data. We have evaluated StarAMR across 1,321 genomic isolates and found 99% concordance between antimicrobial susceptibility testing and the predicted drug resistances derived from sequencing data by StarAMR.
StarAMR is distributed as a stand-alone command-line application, but is also packaged as a Galaxy tool for easy integration into Galaxy-based workflows. The input for StarAMR is either a single genome or collections of genomes in FASTA format and it produces as output tabular-based or Excel reports of the detected AMR-associated genomic features and predicted drug resistances. We have integrated StarAMR into a Galaxy-based workflow for the detection of AMR in microbial whole-genome sequencing data, alongside the Resistance Gene Identifier (RGI), which makes use of the CARD database. This Galaxy workflow forms the basis of the AMR detection component of the IRIDA platform—software that uses pre-packaged Galaxy workflows for the analysis of microbial genomes for infectious disease investigations and genomic surveillance.
StarAMR is available on GitHub (https://github.com/phac-nml/staramr) and within the Galaxy toolshed. The Galaxy workflow for AMR detection and other information used by the IRIDA platform is available on GitHub (https://github.com/phac-nml/irida-plugin-amr-detection). StarAMR is used for the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) and to complement other surveillance programs and outbreak investigations. We believe that this will continue to be a useful tool to aid in detection of AMR both within Galaxy and as a stand-alone application.
