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

UCSC Genome Browser

http://genome.ucsc.edu/

Ensembl

www.ensembl.org

dbGaP

http://www.ncbi.nlm.nih.gov/gap

www.1000genomes.org

1000 Genomes Project

http://evs.gs.washington.edu/EVS

Exome Variant Server

ExAC Server

http://exac.broadinstitute.org

Sequence Variant Analysis

http://genetics.bwh.harvard.edu/pph/

usegalaxy.org

Galaxy

Outline

• Repositories and Browsers • Gene Annotation and Pathway Analysis • Gene lists

GeneCards

www.genecards.org

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)

DAVID

http://david.abcc.ncifcrf.gov

WebGestalt

http://bioinfo.vanderbilt.edu/webgestalt

WebGestalt

http://bioinfo.vanderbilt.edu/webgestalt

http://cbl-gorilla.cs.technion.ac.il/

GOrilla

GSEA

www.broadinstitute.org/gsea

GSEA

www.broadinstitute.org/gsea

Ingenuity

www.ingenuity.com/

Ingenuity

www.ingenuity.com/

Ingenuity

www.ingenuity.com/

Gene Annotation Portal: Enrichr

http://amp.pharm.mssm.edu/Enrichr

BioGPS

http://biogps.org

Literature Mining

www.chilibot.net

www.brain-map.org

Allen Brain Atlas

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

COXPRESdb

http://coxpresdb.jp/

GeneNetwork

www.genenetwork.org/

STRING

http://string-db.org

DAPPLE

www.broadinstitute.org/mpg/dapple

https://tfenrichment.semel.ucla.edu/

Transcription Factor Enrichment

CoNTExT

https://context.semel.ucla.edu/

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: dhg@ucla.eduhttp://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

https://www.hdinhd.org/

HDinHD

GeneSet analysis

anRicher

-OMICs Studies - Life Cycle

Thank you

gcoppola@ucla.edu

Doxa Chatzopoulou Sandeep Deverasetty

Yeongshnn Ong Yining Zhao