Where we are and where we are going From biology to data and back again Chris Evelo Department of Bioinformatics - BiGCaT Maastricht University.

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Presentation transcript:

Where we are and where we are going From biology to data and back again Chris Evelo Department of Bioinformatics - BiGCaT Maastricht University

Faculty of Health, Medicine and Life Sciences Existing knowledge Genomic Results Genetic Results

Faculty of Health, Medicine and Life Sciences Existing Knowledge Carefully Hidden in:

Faculty of Health, Medicine and Life Sciences Computers aren’t good at: Reading Listening

Faculty of Health, Medicine and Life Sciences There is a lot of knowledge to structure

Cardiomyopathy: Downregulated genes

Fatty Acid Degradation? Other pathways / processes?

Faculty of Health, Medicine and Life Sciences What do we really need? Well…

Find the pathways: Biological processes in duodenal mucosa affected by glutamine administration number of genes PathwayChangedUpDownMeasuredTotalZ Score Hs_Mitochondrial_fatty_acid_betaoxidation Hs_Electron_Transport_Chain Hs_Fatty_Acid_Synthesis Hs_Fatty_Acid_Beta-Oxidation Hs_mRNA_processing_Reactome Hs_Unsaturated_Fatty_Acid_Beta_Oxidation Hs_HSP70_and_Apoptosis Hs_Oxidative_Stress Hs_Fatty_Acid_Omega_Oxidation Hs_Proteasome_Degradation Hs_RNA_transcription_Reactome Hs_Irinotecan_pathway_PharmGKB Hs_Synthesis_and_Degradation_of_Ketone_Bodie s_KEGG

Faculty of Health, Medicine and Life Sciences Understand genomics Example WikiPathway Pathway Pathway on glycolysis. Using modern systems iology annotation. And genes and metabolites connected to major databases.

PathVisio Visualize data on biological pathways It can use gene expression, proteomics and metabolomics data Identify significantly changed processes Martijn P van Iersel, Thomas Kelder, Alexander R Pico, Kristina Hanspers, Susan Coort, Bruce R Conklin, Chris Evelo (2008) Presenting and exploring biological pathways with PathVisio. BMC Bioinformatics 9: 399

Faculty of Health, Medicine and Life Sciences adding data = adding colour Example PathVisio result Showing proteomics and transcriptomics results on the glycolysis pathway in mice liver after starvation. [Data from Kaatje Lenaerts and Milka Sokolovic, analysis by Martijn van Iersel]

Faculty of Health, Medicine and Life Sciences Process the data…

Faculty of Health, Medicine and Life Sciences GSCF Templates Groups Protocols Subjects Samples Events Query module Structured querying Full-text querying Profile-based analysis Study comparison Pathways, GO, metabolite profiles Simple Assay module Body weight, BMI, etc. Epigenetics module Transcriptomics module Raw data Nimblegen Illumina Resulting Genome Feature data Clean CPG island data Raw data cell files Result data p-values z-values Clean data gene expression Web user interface dbNP Architecture Assays

custom programs custom programs custom dbs custom dbs GSCF Templates Groups Protocols Subjects Samples Events Assays NCBO Ontologies Data import xls, cvs, text Molgenis EBI repository custom dbs Output xls ISAtab API custom programs web interface Generic Study Capture Framework Data input / output

Epigenetics DNA Methylation Pipeline R QC, processing R QC, processing R QC, processing Sequence QC, processing Statistical analysis Raw data Nimblegen Raw sequencing data MeDIP, BIS-Seq Raw data Illumina Clean DNA methylation data (Genome Feature Format) Result data with p-values (GFF) RA 6 RA1 2

Now we just need the Pathways

WikiPathways Public resource for biological pathways Anyone can contribute and curate More up-to-date representation of biological knowledge WikiPathways: Pathway Editing for the People. Alexander R. Pico, Thomas Kelder, Martijn P. van Iersel, Kristina Hanspers, Bruce R. Conklin, Chris Evelo. PLoS Biology 2008: 6: 7. e184 Commentaries: Big data: Wikiomics. Mitch Waldrop. Nature 2008: 455, We the curators. Allison Doerr. Nature Methods 2008: 5, 754–755 No rest for the bio-wikis. Ewen Callaway. Nature 2010: 468,

Search: “One carbon”

Click

Editing Login needed Registration by address All edits logged

Draw the proteins and interactions

How to ever do data visualization?

Connect to Genome Databases

Double click to annotate the proteins

Click Add reference to literature

Download

PPS1 Liver All pathways Pathways with high z-score grouped together. Explains why there are relatively few significant genes, but many pathways with high z-score. Cytoscape visualization used to group

Faculty of Health, Medicine and Life Sciences Existing Knowledge Carefully Hidden in:

Faculty of Health, Medicine and Life Sciences Backpages link to databases

Assisted content generation Suggestions from: Compound sources (HMDB) Gene resources (UniProt, IntAct) Pathways & processes (KEGG, GO, local) Text mining Semantic web data stores

Faculty of Health, Medicine and Life Sciences Existing knowledge Genomic Results Genetic Results

Faculty of Health, Medicine and Life Sciences Existing knowledge Genomic Results Genetic Results

Problem: Identifier Mapping ? Affymetrix probeset _at Entrez Gene 3643

BridgeDB: Abstraction Layer interface IDMapper class IDMapperRdb relational database class IDMapperFile tab-delimited text class IDMapperBiomart web service

Faculty of Health, Medicine and Life Sciences Can we show SNPs? Using dbSNP links in ENSEMBL as part of BridgeDB libs

Faculty of Health, Medicine and Life Sciences But it will look like this….

Gene/Protein Z Metabolite X Metabolite Y mi999 TF RS00001 RS00003 RS00002 RS00004 Gene/Protein Y RS00005

Gene/Protein Z Metabolite X Metabolite Y mi999 TF RS00001 RS00003 RS00002 RS00004 Functionalize SNPs Unkown function (attribute to gene) In miRNA binding site In TF binding site Changing protein functionality (coding) Gene/Protein Y RS00005 Changing protein interactions (coding)

Gene/Protein Z Gene/Protein Y RS00011 RS00013 RS00012 RS00014 RS00015 Many more SNPs in one interaction (which is one reason Hapmap based approaches don’t work well) RS00001 RS00003 RS00002 RS00004 RS00005

Gene/Protein Z Gene/Protein Y RS00011 RS00013 RS00012 RS00014 RS00015 Give them (predicted) direction Which helps in evaluating epidemiology studies RS00001 RS00003 RS00002 RS00004 RS00005

Gene/Protein Z Gene/Protein Y RS00011 RS00013 RS00012 RS00014 RS00015 Give them quantities (from Biochemistry and Epidemiology) Which makes them usable in SBML models But then also the interactions in the model need to have directions and quantities. RS00001 RS00003 RS00002 RS00004 RS00005

So we just have to color the jellies, ehhrm SNPs

Thanks!

Faculty of Health, Medicine and Life Sciences Where does this connect to you? What can you use? What do you need? Where can you contribute? What courses/training should be organized? And… where do you disagree? Also please fill the evaluation