Proteomicscbs.umn.edu/sites/cbs.umn.edu/files/public/downloads/... · 2019. 3. 29. · repositories...

Center for Mass Spectrometry and Proteomics | Phone | (612)625-2280 | (612)625-2279

Proteomics Data Analysis using Galaxy-P

Center for Mass Spectrometry and Proteomics

November 23rd 2015 Pratik Jagtap

http://www.cbs.umn.edu/msp

Proteomics Data Analysis using Galaxy-P •  Proteomics Workflow •  Search Databases •  Galaxy Platform •  Generating a Database within GalaxyP •  Peaklist Conversion •  Search algorithms •  Using search algorithms within GalaxyP •  Protein Inference •  Using PeptideShaker within GalaxyP

Documentation: http://z.umn.edu/augworkshopgalaxyp

Eng et al 2011 Mol Cell Proteomics. 10(11): R111.009522.

PROTEOMICS WORKFLOW

SEARCH DATABASES

Mass spectrum Reference Protein Database

from genomic annotation Peptide Spectral Match

Swiss-Prot is the manually annotated and reviewed section of the UniProt Knowledgebase (UniProtKB).

It is a high quality annotated and non-redundant protein sequence database,

which brings together experimental results, computed features and scientific

conclusions. http://en.wikipedia.org/wiki/Swiss-Prot

TrEMBL contains high-quality computationally analyzed records, which are enriched with automatic annotation.

The translations of annotated coding sequences in the EMBL-Bank/GenBank/DDBJ nucleotide sequence database are automatically processed and entered in

TrEMBL. http://en.wikipedia.org/wiki/TrEMBL

PROTEOMIC DATABASES

CUSTOMIZED PROTEOMIC DATABASES

Customized database

repositories (CPTAC / UniMesh)

Genomic DNA

sequences.

Expressed sequence

tags / cDNA sequences.

Six-frame translation

Three-frame translation

Metagenomic databases.

Translation

RNASeq data.

Translation and database reduction

workflows

Proteomic databases.

LOOKING BEYOND THE KNOWN PROTEOME

from genomic annotation

Cancer / Disease related Databases such as COSMIC, IARC p53, OMIM…

Deep genome sequencing data from ICGC, TCGA and CPTAC

RNASeq data (Customized OR

Combined)

6-frame DNA sequences. 3-frame cDNA sequences. Identification of

peptides corresponding

to novel proteoforms.

GALAXY PLATFORM

Benefits of Galaxy •  A web-based bioinformatics data analysis platform. •  Software accessibility and usability. •  Share-ability of tools, workflows and histories. •  Reproducibility and ability to test and compare results after using multiple

parameters. •  Software tools can be used in a sequential manner to generate analytical workflows

that can be reused, shared and creatively modified for multiple studies.

Goecks J et al Genome Biol. 2010;11(8):R86.

TOOLS & WORKFLOWS •  Software tools can be used in a sequential manner to generate analytical

workflows that can be reused, shared and creatively modified for multiple studies.

For example, Protein Database Downloader downloads UniProt protein FASTA

databases of various organisms.

Galaxy-P: https://galaxyp.msi.umn.edu/

INPUTS : Mass spectral data and search database.

The dataset will be searched against FASTA database with human proteins, contaminant proteins, spiked in proteins and a subset of 3-frame translated cDNA database from EnSEMBL.

INPUTS: a) MGF formatter MGF files. (dataset collection) b) ABRF-Spike4: FASTA sequences of 4 spiked in proteins. c) FASTA File from EnSEMBL Searches: Subset of 3-frame translated cDNA database from EnSEMBL (our template for identifying novel proteoforms). d) Human UniProt FASTA file + contaminant proteins.

HeLa cell lysate

4 proteins spiked in (10 fmols each)

Digested O/N with trypsin

Liquid chromatography fractionation (10 fractions)

Thermofinnigan Orbitrap Velos (Orbi MS, MS/MS HCD)

RAW Files

mzml files

msconvert

MGF files

Log in using your MSI login and password. Click on http://z.umn.edu/history1 Import history and click on ‘start using this history’ Click on http://z.umn.edu/workflow1 Choose import to copy the workflow into your user workflows. On the confirmation screen, select start using this workflow to navigate to your user. In the workflows menu select Run Workflow 1 from the drop down menu. Appropriately assign each input database from History 1 to the corresponding input or the workflow and ‘Run’ the workflow.

GENERATING A DATABASE

WORFLOW 1

Tools used in the workflow

Select History 1

Import history

Start using this history

Select Workflow 1

Import workflow

Start using this workflow

Run Workflow 1

WORKFLOW

http://z.umn.edu/history2

PROTEOMICS WORKFLOW

INPUTS : Mass spectral data and search database.

The dataset will be searched against FASTA database with human proteins, contaminant proteins, spiked in proteins and a subset of 3-frame translated cDNA database from EnSEMBL.

INPUTS: a) MGF formatter MGF files. (dataset collection) b) ABRF-Spike4: FASTA sequences of 4 spiked in proteins. c) FASTA File from EnSEMBL Searches: Subset of 3-frame translated cDNA database from EnSEMBL (our template for identifying novel proteoforms). d) Human UniProt FASTA file + contaminant proteins.

HeLa cell lysate

4 proteins spiked in (10 fmols each)

Digested O/N with trypsin

Liquid chromatography fractionation (10 fractions)

Thermofinnigan Orbitrap Velos (Orbi MS, MS/MS HCD)

RAW Files

mzml files

msconvert

MASS SPECTRAL DATA

RAW DATA CONVERSION TOOL

msconvert ProteoWizard

http://z.umn.edu/msconvert

MGF Formatter

http://z.umn.edu/mgfformatter

Click on http://z.umn.edu/history2b Import history and click on ‘start using this history’

A face in the crowd: recognizing peptides through database search. Eng et al 2011 Mol Cell Proteomics. 10(11)

PROTEOMICS WORKFLOW

from genomic annotation Peptide Spectral Match

DATABASE SEARCH

Nesvizhskii et al Nature Methods - 4, 787 - 797 (2007)

DATABASE SEARCH

Nesvizhskii et al Nature Methods - 4, 787 - 797 (2007)

DATABASE SEARCH

SEARCHGUI

Vaudel M. et al Proteomics (2011) 11(5) https://code.google.com/p/searchgui/ 31

Myrimatch

MS Amanda

MULTIPLE SEARCH ALGORITHMS Tabb et al, J. Proteome Res., 2007, 6 (2)

Eng et al, Proteomics. 2013, 13(1)

Kim and Pevzner PA. Nat Commun., 2014, 5(1)

Geer et al, J Proteome Res., 2004,3(5).

Craig and Beavis. Bioinformatics., 2004, Jun 20(9)

Dorfer et al, J Proteome Res., 2014, 13(8).

MULTIPLE SEARCH ALGORITHMS

Identification Algorithms: OMSSA, MS-GF+ and Comet Database Search Parameters 1: Precursor Accuracy Unit: ppm 2: Precursor Ion m/z Tolerance: 10.0 3: Fragment Ion m/z Tolerance: 0.01 4: Enzyme: Trypsin 5: Number of Missed Cleavages: Not implemented 6: Database: input_database.fasta 7: Forward Ion: b 8: Rewind Ion: y 9: Fixed Modifications: mmts on c 10: Variable Modifications: oxidation of m

SEARCHGUI PARAMETERS

Identification Algorithms: OMSSA, MS-GF+ and Comet Database Search Parameters 1: Precursor Accuracy Unit: ppm 2: Precursor Ion m/z Tolerance: 10.0 3: Fragment Ion m/z Tolerance: 0.01 4: Enzyme: Trypsin 5: Number of Missed Cleavages: Not implemented 6: Database: input_database.fasta 7: Forward Ion: b 8: Rewind Ion: y 9: Fixed Modifications: mmts on c 10: Variable Modifications: oxidation of m

PEPTIDESHAKER

Vaudel et al Nature Biotechnology, 33, (2015)

http://galaxyproteomics.github.io/peptideshaker/

Slide from Alexey Nesvizshkii talk at http://www.scivee.tv/node/12671

PEPTIDESHAKER : PROTEIN INFERENCE

4.3 Peptide Shaker in GalaxyP

PEPTIDESHAKER : TARGET-DECOY SEARCH

4.3 Peptide Shaker in GalaxyP

PEPTIDESHAKER: OUTPUTS

http://z.umn.edu/augworkshopgalaxyp

Complex Workflows Galaxy-P provides an integrated platform for every step of proteogenomic analysis. •  Build target database – download and

translate EST databases or perform gene prediction with Augustus.

•  Numerous tools for identification and text manipulation.

•  Workflow utilizing BLAST to identify novel peptides.

•  Tool to assess peptide-spectrum matches and visualize spectra.

•  Visualize identified peptides on the genome. •  140 steps: Seamless, integrated

proteogenomic workflow.

Flexible and accessible workflows for improved proteogenomic analysis using Galaxy framework. J. Proteome Res., DOI: 10.1021/pr500812t Link: z.umn.edu/pgfirstlook

Links to workflows, webcast, pages, documentation and publications.

Workflows Proteogenomic studies: http://z.umn.edu/pg140 Metaproteomic studies: http://z.umn.edu/metaproteomics1

Webcast Using ProteinPilot within Galaxy-P: z.umn.edu/ppingp

Pages Proteogenomics page: z.umn.edu/proteinpilotpage Metaproteomics page: z.umn.edu/metaproteomicspage

Workshop / Tutorial on proteogenomics: Mass Spectrometry-based Proteomics Data Analysis using Galaxy-P: z.umn.edu/gcc2015gp

Manuscripts

•  Metaproteomic analysis using the Galaxy framework. Proteomics. (2015) doi: 10.1002/pmic.201500074. PMID: 26058579.

•  Multi-omic data analysis using Galaxy. Nat Biotechnol. (2015) 33(2):137-9. doi: 10.1038/nbt.3134. PMID: 25658277

•  Flexible and accessible workflows for improved proteogenomic analysis using the Galaxy framework. J Proteome Res. (2014)13(12):5898-908. doi: 10.1021/pr500812t. PMID:25301683

•  Proteomic profiles in acute respiratory distress syndrome differentiates survivors from non-survivors. PLoS One. (2014) 7;9(10):e109713. doi: 10.1371/journal.pone.0109713. PMID: 25290099

•  Using Galaxy-P to leverage RNA-Seq for the discovery of novel protein variations. BMC Genomics. (2014) 15:703. doi: 10.1186/1471-2164-15-703. PubMed PMID: 25149441

Proteogenomics page: z.umn.edu/proteinpilotpage Metaproteomics page: z.umn.edu/metaproteomicspage

5: Sheynkman GM, Johnson JE, Jagtap PD, Shortreed MR, Onsongo G, Frey BL, Griffin TJ, Smith LM.

QUESTIONS?

Follow us on twitter.com/usegalaxyp

Visit http://usegalaxyp.org

or http://galaxyp.msi.umn.edu

Proteomicscbs.umn.edu/sites/cbs.umn.edu/files/public/downloads/... · 2019. 3. 29. · repositories...

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