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Bioinformatics 2

Bioinformatics 2. “My main problem these days is that I don’t understand how we go from an experiment in the lab to a number on the screen…” Prof. XXX, CBE, FRS, FRSE. Lecture 1. Course Overview & Assessment Introduction to Bioinformatics Research Careers and PhD options

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Bioinformatics 2

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  1. Bioinformatics 2 “My main problem these days is that I don’t understand how we go from an experiment in the lab to a number on the screen…” Prof. XXX, CBE, FRS, FRSE 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 – finishing PhD student – lab (week 5) Bio2 lecture 1

  4. 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

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

  6. Guest lectures and topics Donald Dunbar (Centre for Inflammation Research) Transcriptomic technologies, week 4 (08/02) Ian Simpson (School of Informatics) ChIP technologies, week 8 (07/03) Chris Larminie (GlaxoSmithKline) Bioinformatics in the Pharmaceutical Sector, week 9 (14/03) Bio2 lecture 1

  7. Assessment (Bio2) • Written assignment (released on 24/02, due 10/03) • Data analysis mini project • Plagiarism will be refereed externally • Cite all sources!!! • Late submissions get 0 marks! Bio2 lecture 1

  8. 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

  9. Answers Bio2 lecture 1

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. Bioinformatics 2 Basic biology and roadmap Bio2 lecture 1

  19. How do you characterise life? Bio2 lecture 1

  20. 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

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

  22. 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

  23. 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

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

  25. Roadmap • Lectures 1, 2, 3, 6 aim at covering basic statistical/ ML tools to handle diverse data types • Guest lectures 4, 7, 8 illustrate particular experimental tools and industrial apps • Tutorials help clarifying concepts and applying to problems Bio2 lecture 1

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