Free software and
biomedical research
Alberto Labarga
alberto.labarga@scientifik.info

When Craig Venter
was asked, “What
makes you think you
can do a better job
with life and genetics
than God?”, he
answered…

we have computers

and software too

¡free software!

biology is a data intensive science

Scientific
information
available in 2010
will double every
72 hours

data mining

my data is mine!

and your data is mine,
too!

open source
open data
open access

open science

Sequence (DNA/RNA)
Comparative genomics & phylogeny
Protein sequence analysis &
Regulation of gene evolution
expression; transcription
factors & micro RNAs
Protein structure & function:
computational crystallography
Protein families,
motifs and domains
Chemical biology
Protein interactions & complexes: modelling and prediction
Pathway analysis
Data integration & literature mining
Image analysis Systems
modelling

The first Atlas of Protein Sequence and Structure, presented information
about 65 proteins.

In 1981 the EMBL Nucleotide Sequence Data Library is created.
Version 2 was composed of 811 secuences, around 1 million
bases introduced by hand.

Smith TF, Waterman MS (1981). \"Identification of common
molecular subsequences.\". J Mol Biol. 147 (1): 195‐7.

S.F. Altschul, et al. (1990), \"Basic Local Alignment Search Tool,\" J.
Molec. Biol., 215(3): 403‐10, 1990. 15,306 citations

J. Thompson, T. Gibson, D. Higgins (1994), CLUSTAL W: improving
the sensitivity of progressive multiple sequence alignment. Nuc.
Acids. Res. 22, 4673 ‐ 4680

In 1995 the European bioinformatics institute is created.

http://emboss.sourceforge.net/
EMBOSS (The European Molecular Biology Open Software Suite)
is a free Open Source software analysis package that provides a
comprehensive set of sequence analysis package specially
developed for the needs of the molecular biology user
community.
First requirements based on a list of long‐standing problems in
existing commercial software (GCG), and the need for public
source code
Within EMBOSS you will find around 200 programs (applications).
Current version is 6.0.1

Main Programs in EMBOSS
Retrieve sequences from database
Sequence alignment
Nucleic gene finding and translation
Protein secondary structure prediction
Rapid database searching with sequence patterns
Protein motif identification, including domain analysis
Nucleotide sequence pattern analysis, for example to
identify CpG islands or repeats.
Codon usage analysis for small genomes
Rapid identification of sequence patterns in large scale
sequence sets
Presentation tools for publication

2 open‐bio.org
• The Open Bioinformatics Foundation is a non profit,
volunteer run organization focused on supporting
open source programming in bioinformatics.
• Its main activities are:
– Underwriting and supporting the BOSC conferences
– Organizing and supporting developer‐centric \"hackathon\"
events (Bio*)

O’Reilly Books and Conferences

http://www.ensembl.org

30
http://www.uniprot.org

Generic Model Organism Database project
http://gmod.org

DAS Concept
Annotation server A Annotation server B Annotation server C
Client Reference server
http://www.biodas.org

DAS Server
• DAS request to retrieve features on a segment:
• http://das.ensembl.org/das/ens_36_omim_genes/features?s
egment=1:1,1000000
• Result:

Das viewer

http://www.ebi.ac.uk/dasty/

Illumina / Solexa
Genetic Analyzer
Applied Biosystems Roche / 454 Applied Biosystems
Genome Sequencer SOLiD
ABI 3730XL
3000 Mb/run
100 Mb/run
1 Mb/day

Sequencing Fragment assembly problem The Shortest
Superstring Problem Velvet (Zerbino, 2008)
Sequence comparison pairwise and multiple sequence
alignments dynamic algorithm, heuristic methods PSI‐ BLAST
(Altschul et. al., 1997) (SSAHA, 2001) (MUMmerGPU, 2008)
Gene finding Hidden Markov Models, pattern recognition
methods GenScan (Burge & Karlin, 1997)

Genomes
Nucleotides
Proteins
Structures
Other molecules
Interactions
Experiments
Literature
Ontologies
http://www.ebi.ac.uk/Databases/

Curso práctico de base de datos e
integración de información biológica

Challenges of Data Integration
• Different types of data (sequence, function, literature etc.)
• Different data formats (FASTA, EMBL, Genbank, tab
delimited etc.)
• Different storage formats (ASCII flatfile, XML, RDBMS)
• No standard formats for common fields (citations,
descriptions, dates etc.)
• Volume and size of data

BioMart is a simple and robust data integration system for large
scale data querying, providing researchers with fast and flexible
access to biological databases
http://www.biomart.org/

Web Services
http://www.ebi.ac.uk/Tools/

Challenges when using tools in unison
• Manually transfer data from one application to
another
• Understand disparate data formats
• Convert file formats where appropriate
• Manage and understand disparate application
environments e.g. web browser, desktop
application

submission
curation
ws ws ws ws ws
dataflow workflow

RESTful web services
GET, POST
HTML,XML,PNG
REST: REpresentational State Transfer
http://www.ebi.ac.uk/Tools/webservices/rest/dbfetch/uniprot/slpi_human

Any web page is a web service
http://www.ebi.ac.uk/cgi-bin/dbfetch?db=uniprot&id=alk1_human&style=html&format=default

Friendly URL and XML documents
http://www.ebi.ac.uk/Tools/webservices/rest/dbfetch/uniprot/slpi_human
•
http://www.ebi.ac.uk/Tools/webservices/rest/dbfecth/uniprot/slpi_human/xml
•
http://www.ebi.ac.uk/Tools/webservices/rest/dbfetch/uniprot/slpi_human/fasta
•

Biomart query
<Query virtualSchemaName=\"central_server_1\">
<Dataset name=\"hsapiens_gene_ensembl\" >
<Attribute name=\"ensembl_gene_id\"/>
<Attribute name=\"ensembl_transcript_id\"/>
<Filter name=\"chromosome_name\" value=\"1\"/>
<Filter name=\"band_end\" value=”p36.33\"/>
<Filter name=\"band_start\" value=”q44\"/>
</Dataset>
<Dataset name=\"msd\">
<Attribute name=\"pdb_id\"/>
<Attribute name=”experiment_type\"/>
<Filter name=\"experiment_type\" value=”NMR\"/>
</Dataset>
</Query>

SOAP services
SOAP: Simple Object Access Protocol
fetchData(uniprot,wap_rat,default,xml)

wsdbfetch
fetchData (db, id, format, style)
entry

Perl client
use SOAP::Lite;
my $WSDL='http://www.ebi.ac.uk/Tools/webservices/wsdl/WSDbfetch.wsdl';
my $soap = SOAP::Lite->service($WSDL);
# fetchData dbName:id <format> <style>
my $result = $soap->fetchData(‘uniprot’, ‘default’, ‘raw’);
die $soap->call->faultstring if $soap->call->fault;
foreach my $i (@$result) { print \"$i\\n\"; }

EBI web services (analysis tools)
run(params, data)
jobid
checkStatus (jobid)
status
getResults (jobid)
results available
poll (jobid, type)
result file

Perl client
use SOAP::Lite;
my $WSDL = 'http://www.ebi.ac.uk/Tools/webservices/wsdl/WSFasta.wsdl';
my $fasta_client = SOAP::Lite->service($WSDL);
my %params=();
$params{'program'}='fasta3';
$params{'database'}='uniprot';
$params{'email'}='your@email.com';
$params{‘async'}= 1;
$data={type=>\"sequence\",
content=>\"MRCSISLVLGLLALEVALARNLQEHVFNSVQSMCSDDSFSEDTECI\"};
# $data={type=>\"sequence\",
# content=>“uniprot:slpi_human\"};
my $jobid = $fasta_client >runFasta(
SOAP::Data->name('params')->type(map=>\\%params),
SOAP::Data->name( content => [$data]));
print $fasta_client->poll($jobid);

Perl client (cont.)
# set a loop for checking job submission status
# RUNNING, NOT_FOUND, ERROR, DONE
my $status = $fasta_client ->checkStatus($jobid);
while (status eq \"RUNNING\") {
sleep 10; $status = $fasta_client->checkStatus($jobid);
}
# when job is done, poll for the results
my $result = $fasta_client ->poll($jobid) if ($status eq \"DONE\") ;
print $result;

http://taverna.sourceforge.net/

http://www.myexperiment.org/users/471

high throughput genomics

data management

http://base.thep.lu.se/
https://carmaweb.genome.tugraz.at/

Why must support standards?
• Unambiguous representation, description
and communication
– Final results and metadata
• Interoperability
– Data management and analysis
• Integration of OMICS system biology

What to standarize?
• CONTENT: Minimal/Core Information to be
reported ‐> MIBBI (http://www.mibbi.org)
• SEMANTIC: Terminology Used ‐>
Ontologies, OBI (http://obi‐ontology.org)
• SYNTAX: Data Model, Data Exchange ‐
>Fuge (http://fuge.sourceforge.net/) ISA‐
TAB, MAGE‐TAB, PRIDE

MIBBI: Standard Content
Promoting Coherent Minimum Reporting Requirements for Biological and
Biomedical Investigations: The MIBBI Project, Taylor et Al, Nature Biotechnology

data analysis

Biological question
Microarray
Experimental design
Microarray experiment
Image analysis
Expression quantification Pre‐processing
A
Normalization n
RT‐PCR a
l
Testing
Estimation Prediction
Clustering
y
s
Biological verification i
and interpretation s

r‐project.org
R is an open source implementation of the S Language
•
Many statistical and machine learning algorithms
•
Good visualization capabilities
•
Possible to write scripts that can be reused
•
Sophisticated package creation and distribution system
•
Supports many data technologies: XML, DBI, SOAP
•
Interacts with other languages: C; Perl; Python; Java
•
R is largely platform independent: Unix; Windows; OSX
•
R has an active user community
•
cran.r‐project.org

BioConductor
• Access wide range of powerful statistical and graphical tools
• Facilitate the integration of biological metadata in the analysis of
experimental data
• Allow the rapid development of extensible, scalable, and
interoperable software;
• Promote high‐quality documentation and reproducible research.
• Provide training in computational and statistical methods for the
analysis of genomic data.
http://www.bioconductor.org/

Bioconductor Packages/libraries
Two releases each year that follow the biannual releases of R
294 software packages
490 Metadata packages
>700 citations
No. software packages
Release 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 ‐> 294 packages

Bioconductor for Microarray Analysis
Quickly becoming the accepted approach
•
Open source
•
Flexible
•
(fairly) simple to use ‐ intuitive
•
Wide applications – many packages
•

affy package
Pre-processing oligonucleotide chip data:
• diagnostic plots,
• background correction,
• probe-level normalization,
• computation of expression measures.
plotAffyRNADeg
barplot.ProbeSet
image
plotDensity

mva package
heatmap

proteomics

http://www.agml.org/

Trans‐Proteomic Pipeline (TPP) is a collection of integrated tools for MS/MS proteomics
http://tools.proteomecenter.org
http://proteowizard.sourceforge.net
http://www.thegpm.org/TANDEM

Bioclipse
Editor
View
View
Console
Properties
http://www.bioclipse.net/

Work with spectra: Spectrum plugin

Work with sequences: BioJava plugin

CMLRSS plugin: Chemistry on the web

cytoscape
http://www.cytoscape.org

pyMol
http://www.pymol.org

image processing

Open Microscopy Environment
• OME is a multi‐site collaborative effort among academic
laboratories and a number of commercial entities that
produces open tools to support data management for
biological light microscopy.
• The original OME server is an application written in Perl
running under Apache. It is accessed using a Web User
Interface, via a Java API, or using a plugin for ImageJ.
• The server can support images in a wide range of file
formats. This model is also extendable allowing custom
data to be stored in the server.
• It supports multiple users and provides appropriate
security for private research and collaboration.
http://openmicroscopy.org

OMERO

OMERO

beyond software

At $150,000, the Polonator is the cheapest
instrument on the market, says Harvard
University's George Church, whose lab
developed the technology in conjunction
with Dover Systems, Plus, the tool uses five‐
fold less reagents than other platforms, and
is the smallest instrument available.
http://www.polonator.org/

http://www.igem.org
http://www.bioparts.org/

where is the stuff

http://bioinformatics.oxfordjournals.org

http://nar.oxfordjournals.org

http://www.biomedcentral.com/bmcbioinformatics/

http://genomebiology.com/software/

the future

[Phil Bourne]
Growth of open access scientists
digital natives, always online, hybrids
catalysts for change

• Making scientific research “re‐useful” — We help
people and organizations open and mark their research
and data for reuse.
• Enabling “one‐click” access to research materials —
We help streamline the materials‐transfer process so
researchers can easily replicate, verify and extend
research.
• Integrating fragmented information sources — We
help researchers find, analyze and use data from
disparate sources by marking and integrating the
information with a common, computer‐readable
language.