Core facilities at EMBL – an overview - Molmedrex Project

Core facilities at EMBL –an overviewVladimír Benešgenecore@embl.de

Research & Core Facilities @ EMBL

Research & Core Facilities at EMBLPrinciples of the Core Facilities’ operation• Identified team for specific service work• The Head of CF is responsible for user satisfaction• No own scientific projects but close interaction with theresearch community and among individual CF• Work on service basis (no co-authorship required ordemanded)• Fast & efficient services at the best possible price• All CFs have their user committees representing the Units• Access for academic collaborators/visitors, EMBO YIPs• Charging to recover the cost of consumables, maintenance

Introduction of new methodsReasons:• To improve quality and efficiency of the services• To reduce the cost of the services• To test and further develop new technologies or modifyexisting ones (collaboration with industry)• To react to users’ demand

Core Facilities at EMBL• Advanced Light Microscopy Core Facility• Electron Microscopy Core Facility• Flow Cytometry Core Facility• Protein Expression & Purification Core Facility• Proteomics Core Facility• Genomics Core Facility• Chemical Biology Core Facility• Monoclonal Antibody Core Facility

GeneCore: Mission Statement• In-house genomics service centre equipped with thestate-of-the art equipment required for projects infunctional genomics• Highly competent, dedicated staff (“intelligent service”)• Consistent performance (proficiency testing)• Stringent adherence to quality• New applications offered if a sufficient user-base exists• “Missing” applications complemented with other CFnearby (in DKFZ, for example)

Unlocking the genomeCelniker et al., Nature 2009

GeneCore ServicesMicroarrays (commercial, in-house spotted):• Expression analysis (mostly Affymetrix, some Agilent, in-house1400MycoArray)1200• Occupancy profiling with tiling arrays (Affymetrix)1000• microRNA analysis800(in-house LNA-based miChip)600• Molecular bar-coding, aCGH400Support for microarray 200 data analysis: GeneSpring, Genomatix,Partek, GeneGo02006 2007 2008 2009expressionAgilent scanner upgraded to 2 µm resolution => suitable for veryhighdensity arrays (1-2 mil. features, Agilent, Nimblegen)tiling

GeneCore ServicesLiquid handling robotics, automation supportProjects based on 96- and 384-well format• Plasmid DNA purification• PCR set up & purification• Replication & consolidation (cherry picking) oflibraries• Preparation of spotting plates (re-arraying)• Development of new applications & automationof protocols compatible with devices

“Samples”:GeneCore Services• Sequencing (plasmids, PCR fragments, BACs),winding down=> outsourced to GATC Konstanz• Fragment analysis (genotyping)• qPCR miRNA assays• Bioanalyzer

GeneCore ServicesComplete support & access to instruments:• qPCR• NanoDrop, PCR cyclers, microarray scanners,hybridization ovens, high capacity SpeedVac,HydroShear, Covaris etc.

Other activitiesCooperation with ALMF to prepare arrays forautomated high-content microscopy (cellbasedsiRNA assays in LabTek chambers)Training courses on functional genomicsmethods & their data analysis for EMBL andoutsideProficiency testing of the GeneCore staffcDNA libraries (drosophila, mouse, medaka,nematode)…

Regulation of ERα responsive genesHOCH3 OHE2HMTsHATs,HDACsMediatorTRANSCRIPTION:GX microarrays,qPCR GX analysis,miRNA profiling,SAGELOCATION ANALYSIS:ChIP-on-chip, ChIP-qPCRData analysisNuclearreceptorNucleosomesEPIGENETICS:“Bisulfite” sequencingDNMTsTarget genesBy courtesy of G. Reid& S. Kangaspeska

New-gen sequencing on SolexaState-of-the-matter:Hardware – 3 GAIIx with paired-end modulesSoftware, IT infrastructure – data processing pipeline inplace but continuously updated from the Illumina side- data storage management with IT ServicesPreparation of seq libraries – under reasonable control,almost all sample types tested, Covaris, HydroShearData analysis – in-house SW solutions, Genomatix GenomeAnalyzer & Mapper, DNAStar Ngen, collaboration withEBI

EMBL Solexas• ~180 million reads per a flow-cell (8 lanes)• >7 Gbp per single read flow-cell (36 bases)• >14 Gbp per PE flow-cell (36 bases)• >2 Gbp/day (capillary sequencer max. 320 kb/day)• 2,5 day single read run, 5 day PE run on Solexa (36 bases)• Supported reading length: 75 bases, 100 coming• Sequencing libraries 200-500 bp, 3-5 kb “insert” length(mate-pair), multiplexing• Full sample processing (library preparation, clustering,sequencing): ~950 €/sample/lane (single-read, 36 bases)

Illumina-Solexa workflow1) Library preparationSingle readPaired-end read36-100 basesBarcoding2) Cluster generation ona flow cell3) Sequencing & imaging4) Data processing & analysis

Cluster generationPrepare DNAfragmentsLigate adaptersAttach single molecules to surfaceAmplify to form clusters100 µmRandom array of clusters~1,000 molecules per ~ 1 µm 2 cluster~180,000 clusters per tile~22 million clusters/lane=>~8 Gb/flow cell

MPS spaceKahvejian, Nat Biotech 2008

Applications at EMBL• Whole-genome analyses of transcriptome,miRNome & occupancy profiling• Identification of DNA and RNA protein binding sites(ChIP-Seq, CLIP-Seq)• Identification of methylation patterns (Methyl-Seq)• Identification of small ncRNA (miRNA) sequences• Digital gene expression profiling (RNA-Seq)• Whole genome sequencing• De novo sequencing and re-sequencing (pairedendedreads)• Structural variation of genomes – CNV, SNP, in/del

Data analysis –sequence census approach3 types of information:• Sequence polymorphism in the template• Quantitative measure (abundance of reads)• Covered regions reveal the internalstructure of the template (exon/introns, NAproteinbinding sites)Morozova & Marra, Genomics (2008)

Challenges• ChIP-Seq and RNA-Seq still requires relatively high startingamount of IP or total RNA• Output is not yet quantitative enough~10 million reads to quantify ~ 80 %of transcripts, ~200 million reads to quantifysplicing levels for ~80 % of transcriptsBlencowe, Genes & Development (2009)• Tools for quantification of sequencing library to determinenumber of fragments that can generate clusters suboptimal• Reduction of base composition bias in produced data• Data analysis

Experimental designMorereadsis importantLargeinsertSmallinsertPaired endMassively parallelsequencingLongerreadsMoresamplesArray-basedenrichmentHighercoverageSolution-basedenrichmentBy courtesy of J. Castle

AcknowledgementRichard CarmoucheJürgen ZimmermannJens StolteJos de GraafSabine SchmidtTomi Bähr-IvacevicDavid IbbersonTobias RauschJonathon Blake

http://www.genecore.embl.de