Coppola network analysis_ucla-07032015
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Data repositories and web tools for data mining Giovanni Coppola July 3 2015 2015 Network Analysis Short Course UCLA
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Transcript of Coppola network analysis_ucla-07032015
Data repositories and web tools for data mining
Giovanni Coppola July 3 2015
2015 Network Analysis Short Course UCLA
-OMICs Studies - Life Cycle
Outline
• Repositories and Browsers • Gene Annotation and Pathway Analysis • Gene lists
Outline
• Repositories and Browsers • Gene Annotation and Pathway Analysis • Gene lists
GEO
www.ncbi.nlm.nih.gov/geo
Array Express
www.ebi.ac.uk/arrayexpress
dbGaP
http://www.ncbi.nlm.nih.gov/gap
http://evs.gs.washington.edu/EVS
Exome Variant Server
Outline
• Repositories and Browsers • Gene Annotation and Pathway Analysis • Gene lists
Gene Ontology
geneontology.org
Gene Ontology
http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003343#s2
Cellular Component (CC)
Molecular Function (MF)
Biological Process (BP)
GSEA
www.broadinstitute.org/gsea
GSEA
www.broadinstitute.org/gsea
Ingenuity
www.ingenuity.com/
Ingenuity
www.ingenuity.com/
Ingenuity
www.ingenuity.com/
Brain RNA-Seq
http://web.stanford.edu/group/barres_lab/brain_rnaseq.html
IlluminaAgilent
Affymetrix
Rosenberg K J et al. PNAS 2008
Microarrays vs. RNA-seq
Sequencing
Brain RNA-Seq
http://web.stanford.edu/group/barres_lab/brain_rnaseq.html
Brain RNA-Seq
http://web.stanford.edu/group/barres_lab/brain_rnaseq.html
https://tfenrichment.semel.ucla.edu/
Transcription Factor Enrichment
Integrative Functional GenomicAnalyses Implicate Specific MolecularPathways and Circuits in AutismNeelroop N. Parikshak,1,2 Rui Luo,3,4 Alice Zhang,2 Hyejung Won,1 Jennifer K. Lowe,1,4 Vijayendran Chandran,5
Steve Horvath,3,6 and Daniel H. Geschwind1,2,3,4,5,*1Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles,CA 90095, USA2Interdepartmental Program in Neuroscience, University of California, Los Angeles, Los Angeles, CA 90095, USA3Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA4Center for Autism Treatment and Research, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles,Los Angeles, CA 90095, USA5Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles,CA 90095, USA6Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA*Correspondence: [email protected]://dx.doi.org/10.1016/j.cell.2013.10.031
SUMMARY
Genetic studies have identified dozens of autismspectrumdisorder (ASD) susceptibility genes, raisingtwo critical questions: (1) do these genetic lociconverge on specific biological processes, and (2)where does the phenotypic specificity of ASD arise,given its genetic overlap with intellectual disability(ID)? To address this, we mapped ASD and ID riskgenes onto coexpression networks representingdevelopmental trajectories and transcriptional pro-files representing fetal and adult cortical laminae.ASD genes tightly coalesce in modules that implicatedistinct biological functions during human corticaldevelopment, including early transcriptional regula-tion and synaptic development. Bioinformatic ana-lyses suggest that translational regulation by FMRPand transcriptional coregulation by common tran-scription factors connect these processes. At a cir-cuit level, ASD genes are enriched in superficialcortical layers and glutamatergic projection neurons.Furthermore, we show that the patterns of ASD andID risk genes are distinct, providing a biologicalframework for further investigating the pathophysi-ology of ASD.
INTRODUCTION
Autism spectrum disorder (ASD) is a heterogeneous neurodeve-lopmental disorder in which hundreds of genes have been impli-cated (Berg and Geschwind, 2012; Geschwind and Levitt, 2007).Analysis of copy number variation (CNV) and exome sequencinghave identified rare variants that alter dozens of protein-coding
genes in ASD, none of which account for more than 1% ofASD cases (Devlin and Scherer, 2012). This and the fact that asignificant fraction (40%–60%) of ASD is explained by commonvariation (Klei et al., 2012) point to a heterogeneous geneticarchitecture.These findings raise several issues. Based on the background
human mutation rate (MacArthur et al., 2012), most genesaffected by only one observed rare variant to date are likely falsepositives that do not increase risk for ASD (Gratten et al., 2013). Itis therefore essential to develop approaches that prioritizesingleton variants, especially missense mutations. Furthermore,given the heterogeneity of ASD, it would be valuable to identifycommon pathways, cell types, or circuits disrupted within ASDitself. Recent studies combining gene expression, protein-protein interactions (PPIs), and other systematic gene annotationresources suggest some molecular convergence in subsets ofASD risk genes (Ben-David and Shifman, 2013; Gilman et al.,2011; Sakai et al., 2011; Voineagu et al., 2011). Yet, it remainsunclear how the large number of genes implicated throughdifferent methods may converge to affect human brain develop-ment, which is critical to a mechanistic understanding of ASD(Berg andGeschwind, 2012). Additionally, ASD has considerableoverlapwith ID at the genetic level, so identifyingmolecular path-ways and circuits that confer the phenotypic specificity of ASDwould be of considerable utility (Geschwind, 2011; Matson andShoemaker, 2009).Here, we took a stepwise approach to determine whether
genes implicated in ASD affect convergent pathways duringin vivo human neural development and whether they are en-riched in specific cells or circuits (Figure 1A). First, we con-structed transcriptional networks representing genome-widefunctional relationships during fetal and early postnatal braindevelopment and mapped genes from multiple ASD and IDresources to these networks. We then assessed shared neurobi-ological function among these genes, including coregulatoryrelationships and enrichment in layer-specific patterns from
1008 Cell 155, 1008–1021, November 21, 2013 ª2013 Elsevier Inc.
http://geschwindlab.neurology.ucla.edu/sites/all/files/networkplot/ParikshakDevelopmentalCortexNetwork.html
Network Browser
http://goo.gl/vwp2iG
Outline
• Repositories and Browsers • Gene Annotation and Pathway Analysis • Gene lists
https://coppolalab.ucla.edu/account
REPAIR
username: HDinHDdemo password: test123
REPAIR study list
REPAIR query results
REPAIR study comparisons
GeneSet analysis
anRicher
-OMICs Studies - Life Cycle
