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M Reich - GenomeSpace
The document discusses how integrative studies can provide insights through combining candidate genomic regions, mitochondrial proteomic data, and cancer expression compendiums to discover genes involved in diseases like Leigh Syndrome and cancers. It also highlights several other studies that have integrated data like DNA sequences, copy numbers, methylation, expression profiles, and pathways to characterize disease subtypes and improve risk stratification for conditions such as glioblastoma multiforme and medulloblastoma. The document presents an example of a translational research study that integrated multiple genomic data types and computational tools in 12 steps to analyze alterations in gene expression and identify potential transcription factor binding sites.
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M Reich - GenomeSpace
- 1. Michael Reich Broad Instituteof Harvard and MIT July 14, 2012
- 2. Need: Insights throughintegrative studies Gene mutation causing Leigh Syndrome Discovery of 3 new genes involved in French Canadian Type (LFSC) and 8 Glioblastoma Multiforme (NF1, ERRB2, other mitochondrial diseases PIK3R1); Confirmation of TP53, PTEN, Integrate: candidate genomic region, EGFR, RB1, PIK3CA mitochondrial proteomic data, and cancer Integrate: DNA sequence, copy number, expression compendium methylation aberrations and expression Authors: Mootha et al. 2003, Calvo et al. 2006 profiles in 206 glioblastomas Authors: TCGA Research Network 2008 Subtle repression of oxphos genes in diabetic muscle: role for mitochondrial ~3000 novel, large non-coding RNAs with dysregulation in diabetes pathology; new functions in development, the immune computational approach Gene Set response and cancer Enrichment Analysis Integrate: Genome sequences from 21 Integrate: Gene sets/pathways & processes mammals, epigenomic maps, and with expression profiles expression profiles Authors: Mootha et al. 2003, Subramanian & Tamayo Authors: Guttman, Rinn et al. 2009 et al. 2005 Characterization of disease subtypes and IKBKE as a new breast cancer oncogene improved risk stratification for Integrate: RNAi screens, transformation medulloblastoma patients of activated kinases, and copy number Integrate: Copy number, expression, from SNP arrays of cell lines clinical data for 96 medulloblastoma Authors: Boehm et al. 2007 patients Authors: Tamayo et al., Cho et al. 2011
- 3. Translational Research Example GenePattern Cytoscape IGV/UCSC Genomica CMAP iii 1 Compendium Differentially Expressed Genes GCT GXP Load compendium Arrests SIF Show module ODF GMT G2/M 3 map i Network 2 Show GSEA test network GXC GRP enrichment 5 Extract HTML 4 Show module Expand +1 Chromosome NA gene list Idea ii (include Alterations neighbors) 6 Add Transcription Learn p53 atcgcgtttattcgataagg site/score on atcgcgttttttcgataagg Factor track Added to GenePattern from UCSC promoter Pathway iv activation v 8 Expression 7 Test for Looks closeGFF similarity of GXA Conclusion to p53 site p53 and gene location vi
- 4. 12 steps, 6tools, 7 transitions 1 i GenePattern Cytoscape IGV Genomica 2 3 ii iii CMAP UCSC Browser Analysis step 4 5 iv v Analysis conclusion Within tool Across tools 6 8
- 5. The Need • Alightweight “connection layer” for a wide variety of integrative genomic analyses o Support for all types of resource: Web-based, desktop, etc. o Automatic conversion of data formats between tools o Easy access to data from any location o Any tool that joins is automatically connected to the community of tools o Ease of entry into the environment
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- 7. Online community toshare diverse computational tools 6 Seed Tools 3 Driving Biological Projects Cytoscape Galaxy lincRNAs GenePattern Genomica Cancer stem cells IGV Patient Stratification UCSC Browser New Biological New tools www.genomespace.org Projects
- 8. GenomeSpace Principles • Aimedtowards non-programming users • Support interoperability through automatic cross-tool data transfer • Requires minimal changes to tools www.genomespace.org
- 9. GenomeSpace Components GenePattern Galaxy Cytoscape Integrative Genomics Viewer GS Enabled Tools 1 Authentication and Authorization 2 3 Analysis and Data Manager Tool Manager Genome Space GenomeSpace Server Project Data geWorkbench External Data Sources & Tools
- 10. Seed Tools Cytoscape Galaxy GenePattern Genomica IGV UCSC Genome Browser
- 11. New Tools Recently added InSilicoDB Cistrome (University of Brussels) (Dana-Farber Cancer Institute) In development geWorkbench Reactome ArrayExpress (Columbia University) (Ontario Institute of Cancer Research) (EBI)
- 12. Using GenomeSpace Tools and Data Sources Actions Cloud- based filesystem
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- 15. GenomeSpace Tool Enablement:GenePattern
- 16. GenomeSpace Tool Enablement:GenePattern
- 17. GenomeSpace Data SourceIntegration: InSilico DB
- 18. Other collaborating projects •Taverna/MyExperiment (University of Manchester) • National Center for Biomedical Ontology (Stanford University)
- 19. DBP3: Studying theregulatory control of human hematopoiesis
- 20. DBP3: Studying theregulatory control of human hematopoiesis – Overview Part 1: Data pre-processing Part 3: Studying the Part 4: cis-regulatory and quality control transcriptional program site analysis Genomica From From part 3 3 2 2 4 part 2 GenePattern 3 4 1 2 IGV To part 2 2 2 1 1 1 From part 2 1 Cytoscape Galaxy 1 1 1 1 1 2 2 Manual step 3 2 To part 5 1 2 Analysis section 1 Part 5: Finding new Analysis step (# steps) 4 transcription factor 1 1 regulators Analysis conclusion From part 4 Optional choices Part 2: Basic analysis 1 Currently integrated From part 1 To part 3 3 1 2 3 2 2 Not yet integrated 2 1 2 2 1 3+ 2 1 2 1 To part 4
- 21. Part 5: Findingnew transcription factor regulators From part 4 Step 1 2 3 Step 4 Step 2 2 Step 3 2
- 22. Step 1: Createtranscription factor dataset in Genomica and save to GenomeSpace
- 23. Step 2: Sendtranscription factor datasets into GenePattern
- 24. Step 2: Performdifferential expression analysis in GenePattern
- 25. Step 3: Senddifferentially expressed genes to Genomica
- 26. Step 3: performmodule network analysis in Genomica
- 27. Step 4: Visualizeregulators with known SNPs and linkage regions
- 28. Step 4: Visualizeregulators with known SNPs and linkage regions
- 29. Deployment Architecture andAPIs Deployment Architecture GS Clients REST Amazon GS UI Identity Service (OpenID) REST IGV Analysis REST CDK Task Genomica Manager (ATM) CDK Gene- Provenance Pattern Simple DB REST Galaxy Data REST Manager CDK Cytoscape (DM) External Data REST Sources UCSC (e.g., Arrary Express) S3 File transfers 30
- 30. Join the GenomeSpacecommunity • Researchers with biological projects • Developers – Add your tools – Contribute format converters – Build new infrastructure • Data portals and repositories – Link your resources
- 31. Acknowledgements GenomeSpace Collaborators Broad Institute Cytoscape: Trey Ideker Lab, UCSD Ted Liefeld Galaxy: Anton Nekrutenko Lab, Penn State University Helga Thorvaldsdottir Genomica: Eran Segal Lab, Weizmann Institute Jim Robinson UCSC Browser Team Marco Ocana GenePattern Team Eliot Polk IGV Team Jill Mesirov, PI Driving Biological Projects Howard Chang Lab – Stanford University Aviv Regev Lab – Broad Institute Funding gs-help@broadinstitute.org www.genomespace.org
Editor's Notes
- #5 The key observation here is that if you can focus on making the transitions between tools easier, you can greatly accelerate the way you can use those tools in concert
- #6 The key challenge is the growing gap between the need to use a variety of different analysis and visualization tools, and the difficulty of getting tools from different sources to work together. 7-10K bioinformatics toolsBroad alone lists ~60 tools on its external website.Doesn’t include internal use tools, lims, data processing/mgmt pipelines,5K public data bases
- #8 Data storage in the cloud is being used in a huge number of applications for the cost, scalability and accessibilityAnd, Web-based APIs have been used to connect disparate Web resources in a very easy wayThese were the principles we used when we developed GenomeSpace
- #9 0. GenomeSpace project to build an online community to share, find, and interoperate diverse computational tools. Tools retain their identity and use as stand- alone software and GenomeSpace maintains their native look and feel. Our goal is to bring the ever-changing wealth of genomic analysis methods and whatever data is required to the fingertips of any biologistSeeded with 6 popular genomics tools representing diverse architectures(cytoscape, galaxy, genepattern, genomica, igv, UCSC browser)Support interoperability through frictionless data transfer with Reproducibility, analytic work flows, comprehensive documentationDevelopment driven by 3 driving biological projects in (cancer, lincRNAs,, stem cell circuits, and patient stratification)Live in the CloudNext phase starting to Engage new toolsEngage new biomedical projectsCurrent participating institutions
- #12 Seed tools span a wide variety of genomic analysis areas: Network analysis and visualization Sequence analysis Functional genomics analysis Module map analysis Integrated genomics and sequence visualization
- #13 Seed tools span a wide variety of genomic analysis areas: Network analysis and visualization Sequence analysis Functional genomics analysis Module map analysis Integrated genomics and sequence visualization
- #18 View GenomeSpace files from within GenePattern, use a file from GenomeSpace in a GenePattern analysisGo to the GenomeSpace UI
- #19 This is a way to get GEO datasets into any tool that will accept gene expression files. You also don’t need to convert from MAGE-TAB.We are also working with the developers of ArrayExpress to GenomeSpace-enable their repository.
- #21 Let’s show you how it works. One of the scientific collaborators, Aviv Regev, recently published work on the regulatory control of human hematopoiesis. For this GenomeSpace demonstration, we will show a simplified version of part of this work.
- #22 This is an overview of the analytic workflow of the work done by Aviv and colleagues. This is months of research condensed to one slide. The analysiscanbedivided in 5 major parts. The first part comprises data preprocessing and qualitycontrol, the second part is a basicanalysis, the third part comprisesstudying the transcriptional program, part four is a cis-regulatoryanalysis, and part fiveaimsforfindingnewtranscription factor regulators. Eachcirclerepresentsworkdone in one of the seed tools, whichyoucanseehere, coloredby the identity of the tool. Circlescolored in greyrepresentmanual steps. They are manualbecause the functionalitydoesn'texist in any of the tools. Duringthis demo, we willmentionwhen we have to do such a manual step. The numberinsideeachcirclerepresents the number of steps that are performedwithin the tool. And the arrowsrepresent the different pathsthatcanbefollowed. At certainpoints, indicatedby the blue boxes, there are alternativeoptionalpathsthatcanbefollowed. We willnow zoom in on part 5 in this demo, which is onfindingnewtranscription factor regulators.
- #23 Whatwe’reactuallydoing in this demo is the following. We start from a very broad set of transcription factors. We then find those transcription factorsthat are significantly differentially expressed in a particular lineage as compared to the other lineages. These we’ll call our lineage-dependent transcription factors. We then infer regulatory programs using Module Networks from Genomica. Regulatory programs consist of modules of coexpressed genes that themselves are coregulated. These regulators we then visualize and validate using previously published GWAS SNPs and linkage regions. We’renowgoing to walk youthrough the steps in this demo. We first start in Genomica and there we load the full expression data containing more than 200 samples and about 8000 genestogetherwith a gene set thatcontains the GO transcription factors. The blue squares next to the genesindicatewhichgenes are GO transcription factors. In Genomica we thencan save the expression data fromonly the GO transcription factors into a text file. In GenePattern we want to refine the GO transcription factors to a set of lineage-specifictranscription factors. We do thisusing the ComparativeMarkerSelection and ExtractComparativeMarkerResults modules in GenePattern. So we basicallyuse a t-test to assessdifferentialexpression of transcription factors in eachlineage versus the rest of the lineages, and we canuse criteria such as an FDR < 0.05 to actually select significantlydifferentiallyexpressedtranscription factors. As anexample, we continue with the transcription factors that are specific to the hematopoietic stem cell (HSC) lineage. Back in Genomica, we run Module Networks on the full expression data, whilealsoloading the lineage-specifictranscription factors we justgenerated in GenePattern. As running Module Networks usuallytakes a while, we nowimmediatelyloadourpreviouslysavedresults. In this case, we have 80 modules of coexpressedgenesforwhich we cangenerate a list of the potential regulators. This list allowsus to browseveryeasilythrough the results. Hereyousee a module that has as top regulator SMAD4. SMAD4 divides the sample spaceinto 2 partitions, whileother regulators furthersubdivide the samples. As a last step, we want to visualize and validateour list of potential regulators. To do that, we follow 2 approaches in parallel. Both approachesrequireus to manuallycreate a .bed annotation track thatcontains the coordinates of the regulators. To validateour regulators, we want to findoverlapsbetweenour regulators and previouslypublished GWAS SNPs and linkageregions. We download these SNPs and linkageregions and we manuallycreate .bed annotation tracks forboth of them. The firstapproach is to visualizeour regulators in IGV. Thisrequiresus to upload the 3 .bed annotation tracks in IGV. In parallel, we submit the same 3 .bed annotation tracks to Galaxy, where we can do overlap analysisbetween regulators, SNPs and linkageregions in several steps. Fromthisanalysis, we getanexcel file that displays the overlaps in tableformat. Interestingalso is thatthistablecontains the pubmedIDs of the papers in which a particular GWAS SNP has been published in relation to a particulardisease.
- #24 To launch desktop-based tools, we first get the prompt that we’re downloading the application in jnlp format
- #26 NOTE: The file sent to GenePattern is still a Genomica-formatted file. We specify in the URL what format we want to convert the file to
- #27 We send this file directly from the GenePattern interface to Genomica, now converting it back to a Genomica formatted file
- #28 We now see Genomica open with the data file, and we perform a module map analysis on this data to determine regulators of module networks, and since we’ll need some other data for the next step, we save the results to GenomeSpace
- #29 From the GenomeSpace Ui, we’re going to send these files now to IGV
- #30 We now see IGV open with our tracks.What’s under the covers…
- #32 The beta release is available for use!A few brave men and women (explorers)