Free software and bioinformatics
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an overview of the free software philosophy as it has been applied on the bioinformatics field
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Free software and bioinformatics
- 1. Free software and biomedical research Alberto Labarga alberto.labarga@scientifik.info
- 2. When Craig Venter was asked, “What makes you think you can do a better job with life and genetics than God?”, he answered…
- 6. biology is a data intensive science
- 8. Scientific information available in 2010 will double every 72 hours
- 10. data mining
- 11. my data is mine!
- 12. and your data is mine, too!
- 14. open science
- 15. 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
- 17. In 1981 the EMBL Nucleotide Sequence Data Library is created. Version 2 was composed of 811 secuences, around 1 million bases introduced by hand.
- 25. 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 quot;hackathonquot; events (Bio*)
- 31. Generic Model Organism Database project http://gmod.org
- 32. DAS Concept Annotation server A Annotation server B Annotation server C Client Reference server http://www.biodas.org
- 33. 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:
- 34. Das viewer
- 36. Illumina / Solexa Genetic Analyzer Applied Biosystems Roche / 454 Applied Biosystems Genome Sequencer SOLiD ABI 3730XL 3000 Mb/run 100 Mb/run 1 Mb/day
- 37. 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)
- 39. Genomes Nucleotides Proteins Structures Other molecules Interactions Experiments Literature Ontologies http://www.ebi.ac.uk/Databases/
- 41. 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
- 42. 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/
- 43. Web Services http://www.ebi.ac.uk/Tools/
- 44. 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
- 46. submission curation ws ws ws ws ws dataflow workflow
- 47. RESTful web services GET, POST HTML,XML,PNG REST: REpresentational State Transfer http://www.ebi.ac.uk/Tools/webservices/rest/dbfetch/uniprot/slpi_human
- 49. 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 •
- 50. Biomart query <Query virtualSchemaName=quot;central_server_1quot;> <Dataset name=quot;hsapiens_gene_ensemblquot; > <Attribute name=quot;ensembl_gene_idquot;/> <Attribute name=quot;ensembl_transcript_idquot;/> <Filter name=quot;chromosome_namequot; value=quot;1quot;/> <Filter name=quot;band_endquot; value=”p36.33quot;/> <Filter name=quot;band_startquot; value=”q44quot;/> </Dataset> <Dataset name=quot;msdquot;> <Attribute name=quot;pdb_idquot;/> <Attribute name=”experiment_typequot;/> <Filter name=quot;experiment_typequot; value=”NMRquot;/> </Dataset> </Query>
- 51. SOAP services SOAP: Simple Object Access Protocol fetchData(uniprot,wap_rat,default,xml)
- 52. wsdbfetch fetchData (db, id, format, style) entry
- 53. 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 quot;$inquot;; }
- 54. EBI web services (analysis tools) run(params, data) jobid checkStatus (jobid) status getResults (jobid) results available poll (jobid, type) result file
- 55. 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=>quot;sequencequot;, content=>quot;MRCSISLVLGLLALEVALARNLQEHVFNSVQSMCSDDSFSEDTECIquot;}; # $data={type=>quot;sequencequot;, # content=>“uniprot:slpi_humanquot;}; my $jobid = $fasta_client >runFasta( SOAP::Data->name('params')->type(map=>%params), SOAP::Data->name( content => [$data])); print $fasta_client->poll($jobid);
- 56. 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 quot;RUNNINGquot;) { sleep 10; $status = $fasta_client->checkStatus($jobid); } # when job is done, poll for the results my $result = $fasta_client ->poll($jobid) if ($status eq quot;DONEquot;) ; print $result;
- 61. data management
- 63. Why must support standards? • Unambiguous representation, description and communication – Final results and metadata • Interoperability – Data management and analysis • Integration of OMICS system biology
- 64. 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
- 65. MIBBI: Standard Content Promoting Coherent Minimum Reporting Requirements for Biological and Biomedical Investigations: The MIBBI Project, Taylor et Al, Nature Biotechnology
- 66. data analysis
- 67. 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
- 68. 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
- 69. 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/
- 70. 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
- 71. Bioconductor for Microarray Analysis Quickly becoming the accepted approach • Open source • Flexible • (fairly) simple to use ‐ intuitive • Wide applications – many packages •
- 72. affy package Pre-processing oligonucleotide chip data: • diagnostic plots, • background correction, • probe-level normalization, • computation of expression measures. plotAffyRNADeg barplot.ProbeSet image plotDensity
- 73. mva package heatmap
- 75. proteomics
- 77. 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
- 78. Bioclipse Editor View View Console Properties http://www.bioclipse.net/
- 82. cytoscape http://www.cytoscape.org
- 83. pyMol http://www.pymol.org
- 84. image processing
- 85. 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
- 86. OMERO
- 88. OMERO
- 89. beyond software
- 90. 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/
- 92. where is the stuff
- 97. the future
- 98. [Phil Bourne] Growth of open access scientists digital natives, always online, hybrids catalysts for change
- 100. • 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.