This document provides an overview of bioinformatics tools and services for analyzing big data in biomedical research. It discusses traditional bioinformatics tools, analyzing genomic data from microarrays and next-generation sequencing without and with code, interpreting results using protein interaction networks and pathways, tools for data storage, cleaning and visualization, and making research reproducible. Galaxy, R, and programming are presented as useful for automated, reproducible analysis of large genomic datasets.

1. Cool Informatics Tools and Services
for Biomedical Research
David Ruau, PhD.
August 1st, 2012 @druau
Sponsored by the Office of Postdoctoral Affairs and
the Lane Medical Library

2. BIG DATA

3. BIG DATA

4. Big Data in Biomedicine
http://www.nature.com/news/gene-data-to-hit-milestone-1.11019

5. We live in a Big Dataoutline
Course world
1. Analyzing genomic data
1. Traditional bioinformatics 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 interaction network
3. General data handling tools
1. Storing your data
2. Data are dirty
4. Statistics made easy
5. Graphics rules!
6. Demystifying “the work”! (the code)
7. Conclusion + Q&A

6. We live in a Big Data world tools
Traditional bioinformatics
Bioinformatics software to solve everyday problems.
The EMBOSS tool suite http://emboss.sourceforge.net/
One web portal is: http://mobyle.pasteur.fr/cgi-bin/portal.py
- DNA / AA Pairwise global and local alignment
- Sequence feature analysis (CpG island, gene scan, restriction enzyme site,
2D/3D structure...)
- Protein structure and domains
- Similarity search (Blast, phi-blast, psi-blast, delta-blast...)
- Phylogenetics (trees from multiple alignments)
- ...

7. We live in a Big Data world tools
Traditional bioinformatics
Bioinformatics software to solve everyday problems.
The EMBOSS tool suite http://emboss.sourceforge.net/
One web portal is: http://mobyle.pasteur.fr/cgi-bin/portal.py
- DNA / AA Pairwise global and local alignment
- Sequence feature analysis (CpG island, gene scan, restriction enzyme site,
2D/3D structure...)
- Protein structure and domains
- Similarity search (Blast, phi-blast, psi-blast, delta-blast...)
- Phylogenetics (trees from multiple alignments)
- ...
UPGMA joining method

8. We live in a Big Data world tools
Traditional bioinformatics
Bioinformatics software 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 restriction enzyme site in your
gene of interest.

9. Stating the obvious
This is not because you have
a GUI that the analysis is
brain dead simple.

10. We live in a Big Data worlddata
Analyzing genomic
Analyzing microarray gene expression microarray without any code.
Gene Pattern: http://genepattern.broadinstitute.org/gp/

11. We live in a Big Data worlddata
Analyzing genomic
Upload your expression data as a text file.
Gene Pattern takes RES and GCT files. Conversion tools are provided
To transform CEL files to GCT. RES

12. We live in a Big Data worlddata
Analyzing genomic
StarBiogene http://web.mit.edu/star/biogene/index.html (java web app)
- Part of GenePattern but provide pipeline style process online
SeqExpress http://www.seqexpress.com/ (Windows only)
- Alternative independent application (less activity than GenePattern)
Expander http://acgt.cs.tau.ac.il/expander/
- Alternative independent application (less activity than GenePattern)
RMAExpress http://rmaexpress.bmbolstad.com/
- Interesting to perform a quality control of your microarrays.
Cluster http://bonsai.hgc.jp/~mdehoon/software/cluster/
- This is the original program to analyze microarray results. No pre-processing
functionality. You need to pre-process separately (using RMAExpress for example)
SAM http://www-stat.stanford.edu/~tibs/SAM/ (significance Analysis of Microarrays)
- To extract the DE genes. This is a Excel plugin. Again, you need to pre-process
separately

13. We live in a Big Data worlddata
Analyzing genomic
Commercial solution
Genespring GX (first 20 days are free)
Access through subscription @ Stanford with CMGM http://cmgm3.stanford.edu

14. We live in a Big Datayour results
Interpreting world
Interpreting a gene list rely on external knowledge.
Several resources / tools are available to help.
KEGG: http://www.genome.jp/kegg/
pathway database
REACTOME: http://www.reactome.org
pathway 2.0 database
Gene Ontology: http://www.geneontology.org/
the ultimate resource for gene function, processes, localization
BioMart: http://www.biomart.org/
Portal providing access to multiple database
GSEA: http://www.broadinstitute.org/gsea/index.jsp
part of GenePattern but also R
David: http://david.abcc.ncifcrf.gov/
to perform an over-representation analysis
Bingo: http://www.psb.ugent.be/cbd/papers/BiNGO/Home.html
over-representation analysis but produce graphical result
(cytoscape)
BioGPS: http://biogps.org/
To know where your gene is expressed in the body or which cell line

15. We live in a Big Datayour results
Interpreting world
Reactome
• Made to be used programmatically
• 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
http://www.reactome.org/userguide/Usersguide.html#FI_Network_Tool

16. We live in a Big Datayour results
Interpreting world
DAVID database
Perform fast over-representation analysis again different databases
- KEGG; Reactome; OMIM (diseases), Generif (literature), protein domain etc...
Protein domains

17. We live in a Big Datayour results
Interpreting world
bioGPS. Exploring expression across tissues and cell lines
Look at other library of
tissues

18. We live in a Big Datayour results
Interpreting world
RMAexpress and quality control of microarrays
Several test exist to test if the microarray performed correctly.
Hall of fame of failed microarrays:
http://plmimagegallery.bmbolstad.com/

19. Analyzing public gene expression data
We live in a Big Data world
Analyzing public microarray with code (kind of...)

20. Analyzing public gene expression data
We live in a Big Data world
Then clic on “TOP 250” button

21. Analyzing public gene expression data
We live in a Big Data world
Top 250 genes
R code

22. Next Generation Sequencing
We live in a Big Data world
Next Generation Sequencing
The main NGS platform are:
• Roche /454 (Genome Sequencer; GS)
• Illumina/Solexa (Genome Analyzer software)
• SOLiD (Applied Bioscience)
Upcoming challengers:
• Ion Torrent (Illumina)
• Oxford Nanopore
What you should request
Done by the
core facility

23. Analyzing mRNA-seq
We live in a Big Data world
Analyzing mRNA-seq data: 4 steps.
1- Alignment and trimming of reads:
[no GUI] [with GUI and commercial]
Tophat (assembly and splice junction mapper) Genome Studio from Illumina
Cufflinks (assembly and RPKM estimates) Genomequest [looks pretty awesome.]
GALAXY provide access to Tophat, Cufflinks.
2- Calling variants and indels:
GATK (http://www.broadinstitute.org/gsa/wiki/index.php/Home_Page)
VarScan (http://varscan.sourceforge.net/)
SHRIMP2; VARiD; Atlas-SNP2; SomaticSniper...
Interpretation of variants: SIFT (galaxy)
3- Finding differentially expressed genes
Cuffdiff (galaxy)
DEXseq (R)
4- Visualization:
SAVANT (http://genomesavant.com/savant/)
IGV (http://www.broadinstitute.org/software/igv)

24. How to use Galaxy?
We live in a Big Data world
Analyzing mRNAseq data: Introducing GALAXY
http://galaxy.psu.edu/

25. We live in a Big Data the cloud
Working in world
Dudley JT, and Butte AJ. 2010. In silico research in the era of cloud computing. Nat
Biotechnol 28: 1181–1185.

26. Summary mRNA-seq
We live in a Big Data world
GALAXY
This is a compendium of software. You even have UNIX tools and EMBOSS in it.
Take home message:
FASTQ files > Tophat > Cuffdiff > IGV (for differential expression)
FASTQ files > Tophat > GATK > IGV (for variant detection)
Where to find help: http://seqanswers.com
Analyzing RNAseq using R
DEXSeq is a R / BioConductor package.
R is a statistical programming software
widely used in bioinformatics

27. Summary mRNA-seq
We live in a Big Data world
Additional tools for genomic
-- Genomespace: http://www.genomespace.org
Collection of tools: GenePattern, 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: https://www.scienceexchange.com/
Science job for hire! This is where top core facilities compete to provide the
best service.
-- Assay Depot: https://www.assaydepot.com/
like home depot but for science
-- taskrabbit: http://www.taskrabbit.com/
If science take too much of your time!

28. Beyond genomics: results interpretation
We live in a Big Data world
Interpreting your gene list with protein-protein interaction network.
iHOP: http://www.ihop-net.org/UniPub/iHOP/
Ingenuity Pathway Analysis
(commercial) access through
CMGM @ stanford

29. Beyond genomics: results interpretation
We live in a Big Data world
Looking into PPI databases:
IntAct: http://www.ebi.ac.uk/intact/
BioGrid: http://thebiogrid.org/ (soon multigene search)
HPRD: http://www.hprd.org/index_html
What about open-source solutions for searching the interaction between the genes in
your gene list?
• Cytoscape http://cytoscape.org
• BioNetBuilder http://chianti.ucsd.edu/cyto_web/plugins/
• ...
• R for programmatic access to databases
• http://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.

30. We live in management and manipulation
Data a Big Data world
REDCap: http://project-redcap.org/
Web app for building and managing online survey and databases
To find participants: https://www.researchmatch.org
MySQL for a professional relational database.
Requires some programming skills in SQL and database design.
Application to query and build databases (goodbye command line):
[OS X]: SequelPro
[Windows]: sqlyog; Toad for MySQL...

31. Data are dirty...
We live in a Big Data world
How to clean your data more efficiently than doing everything by hand?
12:10:00 9999999 POCT Comment GLUCOSE BY METER
21:24:00 51 O2 Saturation, ISTAT (Ven) ISTAT EG7, VENOUS
5:39:00 91 Glu GLUCOSE BY METER
10:58:00 9999999 Comments BLOOD CULTURE (2 AEROBIC BOTTLES)
9:36:00 9999999 Report Status BLOOD CULTURE (2 AEROBIC BOTTLES)
16:25:00 25 CO2, Ser/Plas METABOLIC PANEL, COMPREHENSIVE
8:12:00 132 Glucose, Ser/Plas METABOLIC PANEL, BASIC
8:06:00 5.7 MONO, % CBC WITH DIFF
8:01:00 9.6 Glucose METABOLIC PANEL, BASIC
13:22:00 16.2 CO2 (a) BLOOD GASES, ARTERIAL
4:45:00 2.7 MONO CBC WITH DIFF
DataWrangler @ Stanford
http://vimeo.com/19185801
Google-refine @ down the road.
A bit less intuitive than Wrangler.
For more complex data transformation: reshape2 package in R

32. We live in a Statistics made easy...
Big Data world
Excel... Obviously. But what else when you want something more powerful?
• Switch to a statistical software like R.
• R graphical interface: Deducer (http://www.deducer.org/)
• http://www.youtube.com/watch?v=T6kOvlMaFCA
The case of starting using R
1. Powerful statistics procedures
• R has become the lingua franca for statistical programming
2. Packages for everything from
• Flow cytometry
• DNA microarrays
• RNA-seq
• Google graph API
• ... See http://goo.gl/RwER7
3. Graphics, graphics, graphics...
• R graphical manual: http://goo.gl/qSHMQ

33. We live in a BigGraphics in R
Data world

34. We liveData Science Visualization: Circos
in a Big Data world
CIRCOS: http://circos.ca/
To visualize genome scale interaction and
functional information
CIRCOS is a Perl program. Some light
programming is needed. But it is worth it!

35. We live in Data Science Visualization
a Big Data world
Tableau: http://www.tableausoftware.com/ Great for geo-localized data

36. We live in Data Science Visualization
a Big Data world
Google Visualization: https://developers.google.com/chart/interactive/docs/gallery
Require data in JSON format. Fortunately a bridge with R is possible.
Earthquake in Japan

37. We live in Data Science Visualization
a Big Data world
Google Visualization: https://developers.google.com/chart/interactive/docs/gallery
Motion chart
http://www.youtube.com/watch?v=rnF-7TCIe08
R commands:
> M1 <- gvisMotionChart(Fruits, idvar="Fruit", timevar="Year”)
> plot(M1)

38. We live in aDemystifying the work
Big Data world
Its all about “reproducible research”
Sharing your analytical process (aka. what you did) is as important as the final manuscript.
How do you share what you did with a graphical interface?
The solution 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!

39. We live in a Big The kitchen
Data world
TextMate and NotePad++ for coding
Use version control systems like GitHub or Bitbucket
To make research reproducible when data are not available:
DataThief: http://www.datathief.org/
To follow the last buzz in science: Twitter @druau
Some R books. Most of those book are available online
for free through the Stanford Library.

40. Q&A
We live in a Big Data world
This Class was sponsored by the Office of Postdoctoral Affairs and
the Lane Library
Offline questions to druau@stanford.edu
Thanks!