ProteoGenomics in Galaxy: Identifying novel constellations of proteoforms using transcriptomic and proteomic data.
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1 ProteoGenomics in Galaxy: Identifying novel constellations of proteoforms using transcriptomic and proteomic data. Galaxy Community Conference (GCC 2016) Bloomington, IN 28 th June 2016 PRATIK JAGTAP Center for Mass Spectrometry and Proteomics, St. Paul, MN Department of Biochemistry, Molecular Biology and Biophysics, Minneapolis, MN
2 AcknowledgementS Biochemistry, Molecular Biology & Biophysics TIM GRIFFIN Candace Guerrero Kevin Murray Biologists / collaborators Ka@e Vermillion Kyle Anderson Ma= Andrews BreB Noel, PhD Tzu-Yi Yang, PhD Laurie Parker Minnesota Supercompu0ng Ins0tute Ben Lynch Trevor Wennblom Tom McGowan Ge2ria Onsongo James Johnson Center for Mass Spectrometry & Proteomics LeeAnn Higgins, Todd Markowski Harald Barsnes & Marc Vaudel University of Bergen, Bergen, Norway Lennart Martens VIB, Ugent, Belgium John Chilton Galaxy Team Penn State University Mo Heydarian Karen Reddy Bjoern Gruening University of Freiburg, Freiburg, Germany Funding Ira Cooke La Trobe University, Melbourne, Australia 2
3 ProteoGenomics in Galaxy PROTEOMICS / PROTEOFORMS / PROTEOGENOMICS GALAXY WORKFLOWS AS A SOLUTION EXAMPLE PROTEOGENOMICS PROJECTS WHAT S NEXT?
4 Proteomics, ProteoForms and ProteoGenomics PROTEOFORM : all of the different molecular forms in which the protein product of a single gene can be found, including changes due to genetic variations, alternatively spliced RNA transcripts and posttranslational modifications. (Smith and Kelleher Nat Methods. doi: /nmeth.2369) RNASeq Data
5 Galaxy Workflows Our complex workflow (approximately 140 steps) can be easily shared using builtin Galaxy functions, enabling their use and customization by others. Our results provide a blueprint for the establishment of the Galaxy framework as an ideal solution for the emerging field of proteogenomics. J Proteome Res. (2014) 13(12): doi: /pr500812t
6 Galaxy workflows Database generation RNASeq à Protein database generation (Novel junctions, Single amino acid mutations, Reduced databases) Sheynkman et al 2014; doi: / Data Processing BLAST-P Workflow doi: /s z PSM Evaluation mzsqlite PSM Evaluation Tool doi: /pr500812t Raw Data conversion msconvert and MGF Formatter to convert RAW (Thermo) files. Sciex Converter and msconvert to convert wiff (Sciex) files. Jagtap et al 2014;. doi: /pr500812t Genome Visualization Generate BED files so that they can be visualized using Integrated Genomics Viewer Vermillion et al 2015; doi: /acs.jproteome. 5b00575 Anderson et al 2016; doi: /acs.jproteome. 5b01138 Database Search ProteinPilot; SearchGUI / PeptideShaker; Morpheus. Search strategies (two-step method, multi-step method, etc.) Boekel et al 2015; doi: /nbt.3134 Shortreed et al 2015; doi: /acs.jproteome.5b00599 z.umn.edu/galaxypreferences
7 FILTERING TOOLS FOR Quality Control.
8 MultiOmics Visualization Platform
9 Proteogenomics Projects Salivary ProteoGenomics
10 Proteogenomics Projects Hibernation (Heart and Skeletal)
11 Proteogenomics Projects Hibernation (Heart and Skeletal) HEART MUSCLE: Novel peptide sequence analysis. (A) Venn diagram depicting the number of BLAST-P identified novel peptide sequences from each set of proteomic samples, including peptides that were found in more than one data set. There were 162 novel peptide sequences identified in all three replicates. Vermillion et al J. Proteome Res. 2015, 14, DOI: /acs.jproteome.5b00575 SKELETAL MUSCLE: The transition out of hibernation to an active state (April) exhibits most notable changes in the skeletal muscle proteome. - Relative to hibernation phase, proteins related to glucose metabolism increase sharply, while fatty acid metabolism proteins decline at the active state phase. - Relative to hibernation phase, there is an increase in fast-twitch associated isoform myosin (which uses a more glycolytic mechanism) and a reduction in slow-twitch isoforms. Anderson et al J. Proteome Res. 2016, 15, DOI: /acs.jproteome.5b01138
12 Ongoing Projects B-Cell Development (Reddy Lab) itraq-labeled dataset with pre-pro-b cells and pro-b cells were analyzed by searching against RNASeq data derived protein databases. Heydarian et al 2014; doi: /jpb a) 3-frame translated novel lncrna db b) 3-frame translated annotated lncrna db. c) 3-frame translated antisense lncrna db. d) 3-frame translated repetitive lncrna db. e) Novel junction database f) 3-frame translated EnSEMBL cdna db.
13 Ongoing Projects MultiDrug Resistance (Parker Lab) Increasing concentration of kinase inhibitor Human CML cell line (inhibitor sensitive) K Nilotinib (N) ~ 3 months I K562-IR TKI resistant lines: Grow in [drug] at levels that kill the sensitive line K562 K562-NR N D Cultured alongside with no treatment K562-DR K562 TKI sensitive line: untreated, maintained in culture for same amount of time
14 What is Next? Database generation RNASeq à Protein database generation (Novel junctions, Single amino acid mutations, Reduced databases) RNASeq Quantitation Output RNASeq or EnSEMBL database generation for genome visualization. Replace HiSAT with TopHAT Data Processing BLAST-P Workflow PSM Evaluation mzsqlite PSM Evaluation Tool Raw Data conversion msconvert and MGF Formatter to convert RAW (Thermo) files. Sciex Converter and msconvert to convert wiff (Sciex) files.. Genome Visualization Generate BED files so that they can be visualized using Integrated Genomics Viewer Can be launched on Trackster? Visualization of novel proteoforms ALONG with known proteoforms (their stoichiometry Database Search ProteinPilot; SearchGUI / PeptideShaker; Morpheus. Search strategies (two-step method, multi-step method, etc.) Quantitation Data from Search Outputs? Genome-Proteome Quant Integration Integration at quantification, localization, pathway analysis levels. Ability to parse out interesting peptide sequences for MRM analysis and submit to Skyline? Visualization of genome-proteome data. Ability to parse out interesting RNA sequences for RT-PCR analysis (Quant?)
15 AcknowledgementS Biochemistry, Molecular Biology & Biophysics TIM GRIFFIN Candace Guerrero Kevin Murray Biologists / collaborators Ka@e Vermillion Kyle Anderson Ma= Andrews BreB Noel, PhD Tzu-Yi Yang, PhD Laurie Parker Center for Mass Spectrometry & Proteomics Harald Barsnes & Marc Vaudel University of Bergen, Bergen, Norway Lennart Martens VIB, Ugent, Belgium John Chilton Galaxy Team Penn State University Mo Heydarian Karen Reddy Bjoern Gruening University of Freiburg, Freiburg, Germany z.umn.edu/galaxypreferences Minnesota Supercompu0ng Ins0tute Ben Lynch Trevor Wennblom Tom McGowan Ge2ria Onsongo James Johnson INTERESTED IN GALAXY- P TEAM? POST-DOC POSITION AVAILABLE Funding Ira Cooke La Trobe University, Melbourne, Australia 15
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