Genomics and Computation - Lecture Notes | BIOL 8803, Study Guides, Projects, Research of Biology

Download Genomics and Computation - Lecture Notes | BIOL 8803 and more Study Guides, Projects, Research Biology in PDF only on Docsity! 1 Computational Genomics BIOL 8803 A Spring 2008 I. King Jordan & Jittima Piriyapongsa Office hours by appointment: king.jordan@biology.gatech.edu 404-385-2224 Cherry Emerson 215 jittima@gatech.edu 404-385-1264 Cherry Emerson 217 2 Genomics involves the characterization & study of complete genomes Genomics = experimentation + computation Computers needed to handle large data sets (obvious, perhaps trivial) Computers needed to convert information into knowledge Genome sequencing efforts (along with functional genomics efforts) yield information alone Computational tools must be applied to bring light to that information Genomics & Computation 5 In this class, you the students will complete all of the computational phases of a complete (microbial) genome project Starting with unassembled genome sequence data Neisseria meningitidis provided by the Centers for Disease Control (CDC) Finishing with a publicly available genome sequence browser This course is unlike any course you have had before This course is entirely practical This course is centered on work and results This course is real – you will be solving an actual problem with real data Reality-based course 6 Why run a course like this? This course meets a specific need for more practical training that has been articulated by Bioinformatics students and faculty Real world training on the most up-to-date technological platforms – e.g. we will analyze 454 sequence data and use the latest in analytical (computational) tools There is no way to ‘spoon-feed’ this kind of knowledge and experience to students (‘sage on the stage’ will not work here) The only way to relate these skills is to have you do them yourselves – this is the ultimate ‘active learning’ course The burden of making this course successful will be placed squarely on the students 7 The corporate model In order to facilitate this novel pedagogical model, we will be adopting a corporate model for the course Chief Executive Officer (CEO) – King Jordan Chief Operating Officer (COO) – Jittima Piriyapongsa Chief Information Officer (CIO) – Troy Hilley Share holders – Leonard Mayer and the Meningitis lab at the CDC Management & Employees – you the students Consultants – expert guest lecturers 10 Guest lecturers (consultants) [ii] Mark Borodovsky - Georgia Tech BME & CSE - February 4th - Gene Prediction Dhwani Govil - CDC Bioinformatics Core Facility - February 18th - CDC Functional Annotation Konstantinos Mavrommatis - Joint Genome Institute (JGI) - February 20th - Functional annotation & Comparative Genomics with the Integrated Microbial Genomes (IMG) Resource Scott Cain - Cold Spring Harbor Laboratory - March 26th - Generic Model Organism Database Software Platform 11 Employee (student) responsibilities Technology acquisition – learn relevant approaches and tools including the underlying theory Choice of appropriate technology – evaluate the performance of different tools, choose the best tool(s) for the job Explanation of technology acquisition and choice – clearly relate to your peers why you made the choices you did, relative assessment of performance should be used here, demo showing preliminary results, if complementary approach needed then explain Perform analysis – do the actual analysis your group is charged with, report the results to the class in a lecture and on the Wiki, get the results into the genome browser, iterate as needed 12 Benchmarks for success 1. Actively engage in classroom discussions and lab work 2. Demonstrate that your group understands the theory and the state-of-the art for your specific analytical phase (Group Presentation I) 3. Clearly justify your choice of the tool(s) to be used for your analytical phase, demonstrate comparative performance (Group Presentation II) 4. Do analysis, produce & document results, present results and integrate into genome browser 5. Work closely as needed to help other groups succeed in their phases (see ‘Virtual’ Distributed Computing) 15 Group member questionnaire Programming Unix/Linux Biology Database Andrey Kislyuk . . . Matthew Hagen . . . Manjula Kasoji . . . Charlotte Wiest . . . Andrew Conley . . . Jianrong Wang . . . Minmin Pan . . . 16 Group seed members (please arrange to see me) 1. Genome Assembly – Andrey Kislyuk 2. Gene Prediction – Jianrong Wang 3. Functional Annotation – Manjula Kasoji & Eddie Loh 4. Comparative Genomics – Brendan Hunt & Charlotte Wiest 5. Genome Browser – Andrew Conley 17 No Freeloaders Active participation by all group members is required Delegation of workload within groups will be entirely determined by the groups Group members should invest substantial time and effort upfront to ensure optimal analytical design strategy and workflow If problems arise in terms of effort distribution – i.e. if individual members are not contributing sufficiently – then there are 3 successive levels of control to address this: 1. Work to resolve issue within group (use peer pressure) 2. Consult with COO Jing as to how best resolve issue 3. If steps 1 and then 2 fail, consult with me and I will address the issue 20 Group evaluation & grading (see syllabus for details) 1. All class members will be evaluated on their overall class participation – 12.5% of final grade 2. Group presentation I – 12.5% of final grade 3. Group presentation II – 12.5% of final grade 4. Group presentation III – 12.5% of final grade 5. Final Results and Documentation – 50% of final grade 21 Contingency plans The coursework is inherently sequential & progressive The successful completion of each phase of the analysis hinges upon the previous step We will implement a series of contingency plans in the event that any given step in the analytical pipeline breaks down E.g. if the assembly doesn’t work then we can provide an assembled genome, stripped of annotation, to the gene prediction group Hopefully we will not have to resort to this 22 Computational Resources The School of Biology has provided a dedicated Linux server for this course In addition, all lab computers are dual boot Linux and Windows We have installed a number of bioinformatics software packages on the server and on the lab computers – we can install more as needed Troy Hilley will describe this resource and the other lab facilities shortly