BIG DATA BIG DATA 8/1/12. Cool Informa+cs Tools and Services for Biomedical Research. David Ruau, PhD. August 1 st, 2012

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1 Cool Informa+cs Tools and Services for Biomedical Research David Ruau, PhD. August 1 st, Sponsored by the Office of Postdoctoral Affairs and the Lane Medical Library BIG DATA BIG DATA 1

2 Big Data in Biomedicine hip:// data- to- hit- milestone We live in a Big Course Data outline world 1. Analyzing genomic data 1. TradiOonal bioinformaocs tools 2. Microarrays/gene lists without any code 3. Microarrays/gene lists with code 4. NGS and mrna- seq 2. Beyond genomic 1. Protein- protein interacoon network 3. General data handling tools 1. Storing your data 2. Data are dirty 4. Sta+s+cs made easy 5. Graphics rules! 6. Demys+fying the work! (the code) 7. Conclusion + Q&A We live in TradiOonal a Big Data bioinformaocs world tools Bioinforma+cs sorware to solve everyday problems. The EMBOSS tool suite hip://emboss.sourceforge.net/ One web portal is: hip://mobyle.pasteur.fr/cgi- bin/portal.py - DNA / AA Pairwise global and local alignment - Sequence feature analysis (CpG island, gene scan, restricoon enzyme site, 2D/ 3D structure...) - Protein structure and domains - Similarity search (Blast, phi- blast, psi- blast, delta- blast...) - PhylogeneOcs (trees from mulople alignments)

3 We live in TradiOonal a Big Data bioinformaocs world tools Bioinforma+cs sorware to solve everyday problems. The EMBOSS tool suite hip://emboss.sourceforge.net/ One web portal is: hip://mobyle.pasteur.fr/cgi- bin/portal.py - DNA / AA Pairwise global and local alignment - Sequence feature analysis (CpG island, gene scan, restricoon enzyme site, 2D/ 3D structure...) - Protein structure and domains - Similarity search (Blast, phi- blast, psi- blast, delta- blast...) - PhylogeneOcs (trees from mulople alignments) -... UPGMA joining method We live in TradiOonal a Big Data bioinformaocs world tools Bioinforma+cs sorware to solve everyday problems. Some tools are provided through databases interface such as NCBI Entrez. - The UCSC genome browser. - The Encode project results - For example: visualize GC content and restricoon enzyme site in your gene of interest. StaOng the obvious This is not because you have a GUI that the analysis is brain dead simple. 3

4 We live in a Analyzing Big Data genomic world data Analyzing microarray gene expression microarray without any code. Gene PaIern: hip://genepaiern.broadinsotute.org/gp/ We live in a Analyzing Big Data genomic world data Upload your expression data as a text file. Gene PaIern takes RES and GCT files. Conversion tools are provided To transform CEL files to GCT. RES We live in a Analyzing Big Data genomic world data StarBiogene hip://web.mit.edu/star/biogene/index.html (java web app) - Part of GenePaIern but provide pipeline style process online SeqExpress hip:// (Windows only) - AlternaOve independent applicaoon (less acovity than GenePaIern) Expander hip://acgt.cs.tau.ac.il/expander/ - AlternaOve independent applicaoon (less acovity than GenePaIern) RMAExpress hip://rmaexpress.bmbolstad.com/ - InteresOng to perform a quality control of your microarrays. Cluster hip://bonsai.hgc.jp/~mdehoon/socware/cluster/ - This is the original program to analyze microarray results. No pre- processing funcoonality. You need to pre- process separately (using RMAExpress for example) SAM hip://www- stat.stanford.edu/~obs/sam/ (significance Analysis of Microarrays) - To extract the DE genes. This is a Excel plugin. Again, you need to pre- process separately 4

5 We live in a Analyzing Big Data genomic world data Commercial solu+on Genespring GX (first 20 days are free) Access through Stanford with CMGM hip://cmgm3.stanford.edu We live in a InterpreOng Big Data your world results InterpreOng a gene list rely on external knowledge. Several resources / tools are available to help. KEGG: hip:// pathway database REACTOME: hip:// pathway 2.0 database Gene Ontology: hip:// the ulomate resource for gene funcoon, processes, localizaoon BioMart: hip:// Portal providing access to mulople database GSEA: hip:// part of GenePa[ern but also R David: hip://david.abcc.ncifcrf.gov/ to perform an over- representaoon analysis Bingo: hip:// over- representaoon analysis but produce graphical result (cytoscape) BioGPS: hip://biogps.org/ To know where your gene is expressed in the body or which cell line We live in a InterpreOng Big Data your world results Reactome Made to be used programmaocally Cytoscape (a network tool) has a plugin for Reactome. Just give a gene list or a list of gene + the number of sample where the gene is mutated (for Cox survival analysis) - Retrieve a network from a gene list - Do network analysis - Perform Gene Ontology analysis - Survival analysis hip:// 5

6 We live in a InterpreOng Big Data your world results DAVID database Perform fast over- representaoon analysis again different databases - KEGG; Reactome; OMIM (diseases), Generif (literature), protein domain etc... Protein domains We live in a InterpreOng Big Data your world results biogps. Exploring expression across Ossues and cell lines Look at other library of Ossues We live Analyzing a Big public Data gene world expression data Analyzing public microarray with code (kind of...) 6

7 We live Analyzing a Big public Data gene world expression data Then clic on TOP 250 buion We live Analyzing a Big public Data gene world expression data Top 250 genes R code We live in Next a Big GeneraOon Data world Sequencing Next Genera+on Sequencing The main NGS plarorm are: Roche /454 (Genome Sequencer; GS) Illumina/Solexa (Genome Analyzer socware) SOLiD (Applied Bioscience) Upcoming challengers: Ion Torrent (Illumina) Oxford Nanopore What you should request Sequencer sequence of contigs (FASTA format) SAM/BAM alignment files Done by the core facility 7

8 We live in a Big Analyzing Data mrna- seq world Analyzing mrna- seq data: 4 steps. [with GUI and commercial] 1- Alignment and trimming of reads: Genome Studio from Illumina [no GUI] Genomequest [looks preiy awesome.] Tophat (assembly and splice juncoon mapper) Cufflinks (assembly and RPKM esomates) GALAXY provide access to Tophat, Cufflinks. 2- Calling variants and indels: GATK (hip:// VarScan (hip://varscan.sourceforge.net/) SHRIMP2; VARiD; Atlas- SNP2; SomaOcSniper... InterpretaOon of variants: SIFT (galaxy) 3- Finding differenoally expressed genes Cuffdiff (galaxy) DEXseq (R) 4- VisualizaOon: SAVANT (hip://genomesavant.com/savant/) IGV (hip:// We live in a Big How Data to use world Galaxy? Analyzing mrnaseq data: Introducing GALAXY hip://galaxy.psu.edu/ We live in a Working Big Data in the world cloud Dudley JT, and BuIe AJ In silico research in the era of cloud compuong. Nat Biotechnol 28:

9 We live in a Big Summary Data mrna- seq world GALAXY This is a compendium of socware. You even have UNIX tools and EMBOSS in it. Take home message: FASTQ files > Tophat > Cuffdiff > IGV (for differenoal expression) FASTQ files > Tophat > GATK > IGV (for variant detecoon) Where to find help: hip://seqanswers.com Analyzing RNAseq using R DEXSeq is a R / BioConductor package. R is a staosocal programming socware widely used in bioinformaocs We live in a Big Summary Data mrna- seq world Addi+onal tools for genomic - - Genomespace: h[p:// CollecOon of tools: GenePaIern, Galaxy, cytoscape, genomica etc... (free apparently). Data are stored in the cloud on Amazon VM. If you do not want to do it yourself: - - Science exchange: hips:// Science job for hire! This is where top core facilioes compete to provide the best service. - - Assay Depot: hips:// like home depot but for science - - taskrabbit: hip:// If science take too much of your 5me! We live Beyond in a genomics: Big Data results world interpretaoon Interpre+ng your gene list with protein- protein interac+on network. ihop: hip:// net.org/unipub/ihop/ Ingenuity Pathway Analysis (commercial) access through stanford 9

10 We live Beyond in a genomics: Big Data results world interpretaoon Looking into PPI databases: IntAct: hip:// BioGrid: hip://thebiogrid.org/ (soon mulogene search) HPRD: hip:// What about open- source soluoons for searching the interacoon between the genes in your gene list? Cytoscape hip://cytoscape.org BioNetBuilder hip://chiano.ucsd.edu/cyto_web/plugins/... R for programmaoc access to databases hip://brainchronicle.blogspot.com The plus of using R is that results are reproducible and you can share your method more easily than with point and clic interface. We live Data in management a Big Data and world manipulaoon REDCap: hip://project- redcap.org/ Web app for building and managing online survey and databases To find parocipants: hips:// MySQL for a professional relaoonal database. Requires some programming skills in SQL and database design. ApplicaOon to query and build databases (goodbye command line): [OS X]: SequelPro [Windows]: sqlyog; Toad for MySQL... We live in a Big Data Data are world dirty... How to clean your data more efficiently than doing everything by hand? 12:10: POCT Comment GLUCOSE BY METER 21:24:00 51 O2 SaturaOon, ISTAT (Ven) ISTAT EG7, VENOUS 5:39:00 91 Glu GLUCOSE BY METER 10:58: Comments BLOOD CULTURE (2 AEROBIC BOTTLES) 9:36: Report Status BLOOD CULTURE (2 AEROBIC BOTTLES) 16:25:00 25 CO2, Ser/Plas METABOLIC PANEL, COMPREHENSIVE 8:12: Glucose, Ser/Plas METABOLIC PANEL, BASIC 8:06: MONO, % CBC WITH DIFF 8:01: Glucose METABOLIC PANEL, BASIC 13:22: CO2 (a) BLOOD GASES, ARTERIAL 4:45: MONO CBC WITH DIFF Stanford hip://vimeo.com/ Google- down the road. A bit less intuiove than Wrangler. For more complex data transformaoon: reshape2 package in R 10

11 8/1/12 made easy... We live in a StaOsOcs Big Data world Excel... Obviously. But what else when you want something more powerful? Switch to a staosocal socware like R. R graphical interface: Deducer (hip:// hip:// The case of star+ng using R 1. Powerful staosocs procedures R has become the lingua franca for staosocal programming 2. Packages for everything from Flow cytometry DNA microarrays RNA- seq Google graph API... See hip://goo.gl/rwer7 3. Graphics, graphics, graphics... R graphical manual: hip://goo.gl/qshmq in R We live in a BigGraphics Data world cience VisualizaOon: We livedata in asbig Data world Circos CIRCOS: hip://circos.ca/ To visualize genome scale interacoon and funcoonal informaoon CIRCOS is a Perl program. Some light programming is needed. But it is worth it! 11

12 We live in Data a Big Science Data VisualizaOon world Tableau: hip:// Great for geo- localized data We live in Data a Big Science Data VisualizaOon world Google VisualizaOon: hips://developers.google.com/chart/interacove/docs/gallery Require data in JSON format. Fortunately a bridge with R is possible. Earthquake in Japan We live in Data a Big Science Data VisualizaOon world Google VisualizaOon: hips://developers.google.com/chart/interacove/docs/gallery MoOon chart hip:// 7TCIe08 R commands:! > M1 <- gvismotionchart(fruits, idvar="fruit", timevar="year )! > plot(m1)! 12

13 We live in a DemysOfying Big Data world the work Its all about reproducible research Sharing your analyocal process (aka. what you did) is as important as the final manuscript. How do you share what you did with a graphical interface? The soluoon is to use a programming language, like R if suitable, and share your code. Several tools can make your life easier. Rstudio or Deducer Come to the workshop in 2 weeks! We live in a Big The Data kitchen world TextMate and NotePad++ for coding Use version control systems like GitHub or Bitbucket To make research reproducible when data are not available: DataThief: hip:// To follow the last buzz in science: Some R books. Most of those book are available online for free through the Stanford Library. We live in a Big Data Q&A world This Class was sponsored by the Office of Postdoctoral Affairs and the Lane Library Offline quesoons to druau@stanford.edu Thanks! 13