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Bioinformatics 2

This lecture provides an introduction to the field of Bioinformatics, including an overview of the course, career options, and core topics. It covers the central dogma of molecular biology and introduces students to the application of statistical methods in bioinformatics. The lecture also discusses available resources and the course design, which includes guest lectures and hands-on lab experience.

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Bioinformatics 2

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  1. Bioinformatics 2 Introduction Bio2 lecture 1

  2. Lecture 1 • Course Overview & Assessment • Introduction to Bioinformatics Research • Careers and PhD options • Core topics in Bioinformatics • the central dogma of molecular biology Bio2 lecture 1

  3. About us... • Degree in Physics • PhD in Mathematics (mathematical physics) • Got interested in mathematical modelling • Worked in machine learning & bioinformatics & systems biology • Office in forum, IF 1.44 • Shahzad Asif – 3rd year PhD student – lab (week 5) Bio2 lecture 1

  4. What do I think you know? • Variety of backgrounds and experience: • Biological Sciences • Computing Sciences • Mathematics, Statistics and Physics Bio2 lecture 1

  5. Course Outcomes • An appreciation of statistical methods in bioinformatics, with special focus on networks and probabilistic models • Experience in using and/or implementing simple solutions • Appreciate the current ‘state of the art’ • Be familiar with some available resources Bio2 lecture 1

  6. Course Design • Lectures cover (selective) background • Guest lectures present current research/ applications • Self-study and assignments designed to cover practical implementation • Lab to give hands on experience support Bio2 lecture 1

  7. Guest lectures and topics Donald Dunbar (Centre for Inflammation Research) Microarray technologies, week 3 (26/01) Chris Larminie (GlaxoSmithKline) Bioinformatics in the Pharmaceutical Sector, week 7 (23/02) Ian Simpson (School of Informatics) ChIP-on-chip technologies, week 8 (02/03) Dirk Husmeier (BioSS) Inference of Gene Regulatory Networks, week 9 (09/03) Bio2 lecture 1

  8. Assessment (Bio2) • Written assignment (released on 18/02, due 11/03) • Data analysis mini project • Plagiarism will be refereed externally • Cite all sources!!! • Late submissions get 0 marks! Bio2 lecture 1

  9. Bioinformatics? • Introduce yourselves to each other. • What is Bioinformatics? • What does Bioinformatics do for CS? • What does Bioinformatics do for Biology? • What guest Bioinformatics lecture would you like? • Discuss in groups for 10 min. Bio2 lecture 1

  10. Bio2 lecture 1

  11. What is BioInformatics? • Sequence analysis and genome building • Molecular Structure prediction • Evolution, phylogeny and linkage • Automated data collection and analysis • Simulations and modelling • Biological databases and resources Bio2 lecture 1

  12. BioInf and CS • Provides CS with new challenges with clear bio-medical significance. • Complex and large datasets sometimes very noisy with hidden structures. • Can biological solutions be used to inspire new computational tools and methods? Bio2 lecture 1

  13. BioInf and Biology • High-throughput biology: • around 1989, the sequence of a 1.8kb gene would be a PhD project • by 1993, the same project was an undergraduate project • in 2000 we generated 40kb sequence per week in a non-genomics lab • Illumina/Solexa systems Gigabases per expt. Bio2 lecture 1

  14. BioInf and Biology • High-throughput biology • Data management and mining • Modeling of Biological theories • Analysis of complex systems • Design and re-engineering of new biological entities Bio2 lecture 1

  15. Bioinformatics • http://www.bbsrc.ac.uk/science/grants/index.html • Awarded grants database • http://bioinformatics.oxfordjournals.org • www.biomedcentral.org/bmcbioinformatics • www.nature.com/msb • http://bib.oxfordjournals.org/ Bio2 lecture 1

  16. Bioinformatics@ed • Database integration • Data provenance • Evolutionary and genetic computation • Gene expression databases • High performance data structures for semi-structured data (Vectorised XML) 1/2 Bio2 lecture 1

  17. Bioinformatics@ed • Machine learning • Microarray data analysis • Natural language and bio-text mining • Neural computation, visualisation and simulation • Protein complex modeling • Systems Biology • Synthetic Biology 2/2 Bio2 lecture 1

  18. Career Options • Academic Routes • Get Ph.D, do Postdoctoral Research - lectureship and independent group • M.Sc. RA - becomes semi independent usually linked to one or more academic groups. Career structure is less defined but improving. RAs can do Ph.D. part-time. Bio2 lecture 1

  19. Career Options • Commercial Sector • Big Pharma - Accept PhD and MSc entry. Normally assigned to projects and work within defined teams. Defined career structure (group leaders, project managers etc) • Spin-out/Small biotech - Accept PhD and MSc entry. More freedom and variety. A degree of ‘maintenance’ work is to be expected. Bio2 lecture 1

  20. Career Options • Hybrid Approaches • Commercial and Academic research groups are becoming much closer linked. • University academics encouraged to exploit their IPR (intellectual property rights). • Companies can get government support to collaborate with academic research groups. Bio2 lecture 1

  21. Ph.D. • Assuming a start date of September 2010 • ‘prize’ studentships advertised on jobs.ac.uk, Nature, Science etc starting NOW! • Many linked to nationality/residency (Check details carefully). • UK ‘quota’ studentships vary with department but contact/apply early. Bio2 lecture 1

  22. Ph.D. • US studentships take longer but are better paid and have extra training/coursework • require an entry exam • deadlines around summer for ‘11 Bio2 lecture 1

  23. Bioinformatics 2 Basic biology and roadmap Bio2 lecture 1

  24. How do you characterise life? Bio2 lecture 1

  25. The central “dogma” of molecular biology • Static genetic information is stored in DNA • Genes are portions of DNA which are “transcribed” into mRNA • mRNA is “translated” by ribosomes into proteins • Proteins carry out the essential cellular functions: enzymatic, regulatory, structural Bio2 lecture 1

  26. Bio-Map Slide from http://www.nd.edu/~networks/ GENOME protein-gene interactions PROTEOME protein-protein interactions Cell structure Metabolism Bio2 lecture 1

  27. Multiple control points • DNA replication is regulated • mRNA transcription is regulated by transcription factor proteins • mRNA degradation is regulated by RNA binding proteins/ small RNAs • mRNA translation is regulated by tRNA • Enzymes “regulate” metabolism Bio2 lecture 1

  28. Multiple data types • Sequencing (deep) measures genomic data • Microarrays (lecture 3), PCR, RNA-seq measure mRNA • Flow cytometry/ fluorescence measures single cell protein abundance • Mass spectrometry measures proteins/ metabolites Bio2 lecture 1

  29. Multiple data types • Chromatin immunoprecipitation measures protein binding (lecture 8) • Yeast 2 hybrid measures protein interactions • Nuclear magnetic resonance measures metabolites Bio2 lecture 1

  30. Roadmap • Lectures 2, 4, 5, 6 aim at covering basic statistical/ ML tools to handle diverse data types • Guest lectures 3, 7, 8 illustrate particular experimental tools and industrial apps • Guest lecture 9 describes advanced ML for bioinformatics Bio2 lecture 1

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