Lopresti_101907 - Introduction to Bioinformatics Dan Lopresti Associate Professor Office PL 404B [email protected] Introduction to Bioinformatics

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Introduction to Bioinformatics Lopresti BioS 95 October 2007 Slide 1 Dan Lopresti Associate Professor Office PL 404B [email protected] Introduction to Bioinformatics
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Introduction to Bioinformatics Lopresti BioS 95 October 2007 Slide 2 Motivation “Biology easily has 500 years of exciting problems to work on.” By developing techniques for analyzing sequence data and related structures, we can attempt to understand molecular basis of life. http://cmgm.stanford.edu/biochem218/
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Introduction to Bioinformatics Lopresti BioS 95 October 2007 Slide 3 What is bioinformatics? Application of techniques from computer science to problems from biology. Bioinformatics Why is it interesting? Important problems. Massive quantities of data. Desperate need for efficient solutions. Success is rewarded. Bioinformatics Computer Science Biology
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Introduction to Bioinformatics Lopresti BioS 95 October 2007 Slide 4 Data Explosion To first approximation, DNA is a language over a four character alphabet, { A , C , G , T }. http://www.ncbi.nlm.nih.gov/Genbank/genbankstats.html Opportunity (1) Adenine , (2) Cytosine , (3) Guanine , (4) Thymine . Our genetic identity is encoded in long molecules made up of four basic units, the nucleic acids:
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Introduction to Bioinformatics Lopresti BioS 95 October 2007 Slide 5 Genomes Complete set of chromosomes that determines an organism is known as its genome . http://www.cbs.dtu.dk/databases/DOGS/ http://www.nsrl.ttu.edu/tmot1/mus_musc.htm http://www.oardc.ohio-state.edu/seedid/single.asp?strID=324 Us ??? ??? Mus musculus Conclusion: size does not matter!
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Introduction to Bioinformatics Lopresti BioS 95 October 2007 Slide 6 Comparative Genomics http://www.ornl.gov/sci/techresources/Human_Genome/graphics/slides/ttmousehuman.shtml How did we decipher these relationships?
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Introduction to Bioinformatics Lopresti BioS 95 October 2007 Slide 7 Algorithms are Central Conduct experimental evaluations (perhaps iterate above steps). An algorithm is a precisely-specified series of steps to solve a particular problem of interest. Develop model(s) for task at hand. Study inherent computational complexity: Can task be phrased as an optimization problem? If so, can it be solved efficiently? Speed, memory, etc. If we can't find a good algorithm, can we prove task is “hard”? If known to be hard, is there approximation algorithm (one that works at least some of the time or comes close to optimal)?
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Introduction to Bioinformatics Lopresti BioS 95 October 2007 Slide 8 Sequence Nature of Biology Macromolecules are chains of simpler molecules. In DNA and RNA, they are
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This note was uploaded on 03/09/2008 for the course BIOS 95 taught by Professor Varies during the Spring '08 term at Lehigh University .

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Lopresti_101907 - Introduction to Bioinformatics Dan Lopresti Associate Professor Office PL 404B [email protected] Introduction to Bioinformatics

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