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Future Challenges in Bioinformatics Evolution

Explore the past evolution of bioinformatics, its pivotal role in life sciences, and upcoming challenges in this field. Discover the categories of bioinformatics tools, the need for supercomputers, software development issues, and tools for biotech projects.

adamsmichael
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Future Challenges in Bioinformatics Evolution

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  1. Future Challenges in Bioinformatics

  2. Introduction • Introduction: How RRX got involved … • Life sciences context: How bioinformatics came to be important… • The past half century: How bioinformatics has “evolved”…

  3. Introduction • Categories of Bioinformatics Tools • Why We Need Supercomputers • Software Development Issues • Future Challenges • Tools for Biotech Projects • Summary

  4. How RRX got involved … • Submitted a Canadian Foundation for Innovation (CFI) proposal for Advanced Bioinformatics Collaborative Computing (ABioCC)

  5. How RRX got involved … • Developed an SVG based visualization front end • Paper will be presented at SVG Open 2003 in Vancouver on July 17th

  6. How bioinformatics came to be important… • After the structure of DNA was reverse engineered with X-Ray diffraction in 1953 focus shifted to nucleic acid sequence analysis • DNA/RNA/protein sequence data accumulated using computer programs for storage and analysis

  7. How bioinformatics came to be important… • Bioinformatics algorithms in development for the last half century came into wide spread use by researchers • The ability to compare sequences created a homology context for unknown sequences of interest leading to advances…

  8. How bioinformatics came to be important… • Improved sequencing technology enabled the complete deciphering of the human genome >>> 1999 • About 3.18 billion base pairs • Celera used 300 PE Biosystems ABI Prism 3700 DNA Analysers

  9. How bioinformatics has “evolved”… • Central dogma of molecular biology – • DNA sequences are transcribed into mRNA sequences, mRNA sequences are translated into protein sequences, which fold 3D creating structures with functions statistically survival selected >>>affecting the prevalence of the underlying DNA sequences in a population

  10. How bioinformatics has “evolved”… • This created a supporting information flow • Organization and control of genes in the DNA sequence • Identification of transcriptional units in the DNA sequence • Prediction of protein structure from sequence • Analysis of molecular function

  11. How bioinformatics has “evolved”… • Another covariant information flow was created based on the scientific method • Create hypothesis wrt biological activity • Design experiments to test the hypothesis • Evaluate resulting data for compatibility with the hypothesis • Extend/modify hypothesis in response

  12. How bioinformatics has “evolved”… • IT used to handle explosion of data from high throughput techniques, too complex for manual analysis • X-ray diffraction

  13. How bioinformatics has “evolved”… • Automated DNA sequencing • Amersham Biosciences • Applied Biosystems • Beckman Coulter • LI-COR • SpectruMedix Corp. • Visible Genetics Corp.

  14. How bioinformatics has “evolved”… • Microarray expression analysis

  15. How bioinformatics has “evolved”… • Rapid emergence of 3D macromolecular structure databases • New sub discipline: structural bioinformatics • Atomic and sub cellular spatial scales • Representation/physics • Storage/retrieval/source data correlation/interpretation • Analysis/simulation • Display/visualization

  16. How bioinformatics has “evolved”…

  17. Categories of Bioinformatics Tools… • Databases >>> search/compare • Sequence Analysis - Clusters • Genomics • Phylogenics • Structure Prediction • Molecular Modelling • Microarrays • Packages, Misc Apps, Graphics, Scripts

  18. aceperl BLAST Blastall Blastpgp BLAT Blimps Entrez FASTA fastacmd formatdb getz HMMER IMPALA InterProScan PHI-BLAST ProSearch PSI-BLAST PSI-BLASTN Seguin Swat tace xace Categories of Bioinformatics Tools… • Database >>> search/compare

  19. Artemis Bl2seq BLAST Clustal W, X consed/autofinish Cross_match Dotter EMBOSS FASTA Glimmer HMMER InterProScan MEME View Paracel Transcript Assem Phrap Phred Primers ProSearch Readseq2 Rnabob RRTree SAPS seals Seqsblast STADEN Swat T-Coffee Sequence Analysis

  20. Calc_primers Cross_match FPC GENSCAN Glimmer Image Mzef Phrap Phred STADEN Swat tace tace_celegans tRNAscan-SE xace xace_celegans Genomics

  21. Clustal W Clustal X MOLPHY MrBayes PHYLIP RRTree T-Coffee TREE-PUZZLE TreeViewX Phylogenics

  22. Structure Prediction • EMBOSS • MEME • Modeller • Mzef • PHI-BLAST

  23. Molecular Modelling • Modeller • homology modeling an alignment of a sequence to be modeled with known related structures • Rasmol • a molecular graphics program intended for 3D visualisation of proteins and nucleic acids • Raster3D (publishing images) • X3DNA • analyzing and rebuilding 3D structures

  24. Microarrays • Dapple • a program for quantitating spots on a two-colour DNA microarray image.. • OligoArray • a program that computes gene specific oligonucleotides that are free of secondary structure for genome-scale oligonucleotide microarray construction.

  25. BioPERL BioJava boxshade mvscf seg Split_fasta Packages, Useful Scripts/Source Code, Graphics, PERL • povRay • Raster3D • MOLPHY

  26. Why We Need Supercomputers… • Some commercial packages run on “supercomputers” • Accelrys: modeling and simulation • Materials Studio • Cerius2 (SGI Unix only) • Homology modeling to catalyst design • Insight II (SGI Unix only) • 3D graphical environment for physics based molecular modeling • Catalyst (high end Unix servers) • database management valuable in drug discovery research • QUANTA (high end Unix servers) • crystallographic 2D/3D protein structure solution • Discovery Studio

  27. Why We Need Supercomputers… • Supercomputer advantages • Multiple processors • Large shared memory • Handle very large files • Large/fast RAID arrays • Terabyte tape backup systems • Power backup systems • High performance networks

  28. Why We Need Supercomputers… • Common bioinformatics requirements • Computationally intensive tasks • Large memory models • Intensive/complex database searches • Large experimental database sets • Large derived database sets • Large persistent intermediate data structures • Teamwork data sharing and visualization

  29. Why We Need Supercomputers… • Network requirements • Driving gigE/10gigE NICs • Moving large files/data sets rapidly • Visualization streams/Access GRID • Coordinating Cluster/GRID computing • Dynamic provisioning of light paths

  30. Why We Need Supercomputers…

  31. Why We Need Supercomputers… xxxxxxxxxxxxxxxxxxxxxxx

  32. Software Development Issues… • Collaboration contexts/barriers • Team work … collaboration spaces • Standards development … DTDs • Integration issues… • experimental data to homology to 3D model • platform issues… • network issues – 9k MTU - jumbo frames • Licensing issues – public vs. private

  33. Future Challenges… • Creating developer infrastructure for building up structural models from component parts … • components from macromolecule libraries ported to object models • Understanding the design principles of systems of macromolecules and harnessing them to create new functions … • specialized molecular machines

  34. Future Challenges… • Learning to design drugs efficiently and cost effectively based on knowledge of the target … • target generation automation • validation automation • Development of enhanced simulation models that give insight into context based function from knowledge of structure … • possible use of artificial intelligence to limit scope of search

  35. How Tools might be used for Industry Biotech Projects

  36. Summary • Bioinformatics • well positioned to assist with application development • exploring novel bioinformatics software development • proceeding with supporting access GRID and optical switching technology

  37. Questions/Comments… ? ;-)

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