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Bioinformatics lectures at Rice University

Bioinformatics lectures at Rice University. Li Zhang Lecture 1 Department of Bioinformatics and Computational Biology MD Anderson Cancer Center March-April , 2012. Contact information. Li Zhang Phone: 713-563-4298 (office) 713-962-6661 (cell) Email: lzhangli@mdanderson.org

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Bioinformatics lectures at Rice University

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  1. Bioinformatics lectures at Rice University Li Zhang Lecture 1 Department of Bioinformatics and Computational Biology MD Anderson Cancer Center March-April, 2012

  2. Contact information • Li Zhang • Phone: 713-563-4298 (office) 713-962-6661 (cell) • Email: lzhangli@mdanderson.org • URL: http://odin.mdacc.tmc.edu/~llzhang/RiceCourse/ • Office location: FCT4.5034. Pickens Tower, 4th floor, MD Anderson Cancer Center.

  3. Homework • There will be 2-3 assignments posted online. • All students are required to complete the assignments. Homework will be submitted at the beginning of class on the due date. • If circumstances beyond the student’s control arise and an assignment cannot be submitted on the due date, an instructor should be contacted prior to the due date. With an instructor’s permission, late homework may be accepted within one week of the due date. • All decisions will be made on an individual student basis and the final decision rests with the instructor assigning the homework. A penalty of 10 percentage points will be applied to late homework.

  4. What is bioinformatics? • Bioinformatics is the application of computer science and information technology to the field of biology and medicine. Bioinformatics deals with algorithms, databases and information systems, web technologies, artificial intelligence and soft computing, information and computation theory, software engineering, data mining, image processing, modeling and simulation, signal processing, discrete mathematics, control and system theory, circuit theory, and statistics, for generating new knowledge of biology and medicine, and improving & discovering new models of computation (e.g. DNA computing, neural computing, evolutionary computing, immuno-computing, swarm-computing, cellular-computing). • Commonly used software tools and technologies in this field include Java, XML, Perl, C, C++, Python, R, MySQL, noSQL, CUDA, MATLAB, and Microsoft Excel.

  5. Focus area of this course • Reference book by in Pierre Baldi’s: “Bioinformatics: A machine learning approach” and a few key papers. • Introducing high throughput technologies that provide the data. • Machine learning algorithms and models to visualize and explore large datasets identify patterns & relationships. • Computing language: R/Perl. • Database: Non-relational database NoSQL. • Not focused web applications, no structural biology.

  6. Why should we study bioinformatics? Why it is important to study bioinformatics?

  7. Let us see a few growth charts …

  8. Growth of PDB (Protein Structures) The Protein Data Bank (PDB) is a repository for the 3-D structural data of large biological molecules, such as proteins and nucleic acids. Most structures are determined by X-ray diffraction, but about 15% of structures are determined by NMR. Large scale organized efforts by Structural Genomics Initiative and International Structural Genomics Consortium have greatly accelerated the pace of growth.

  9. Growth Chart Of GEO (RNA etc) Gene Expression Omnibus (GEO) database holdsover 10 000 experiments comprising 300 000 samples, 16 billionindividual abundance measurements, for over 500 organisms, submittedby 5000 laboratories from around the world. The database typicallyreceives over 60 000 query hits and 10 000 bulk FTP downloadsper day, and has been cited in over 5000 manuscripts.

  10. GenBank growth chart (DNA sequences) There are 126 billion bases in 135 million sequence records in the traditional GenBank divisions and 191 billion bases in 62 million sequence records in the WGS division as of April 2011.

  11. A brief history of the big bang of the digital universe

  12. The age of big data “The story is similar in fields as varied as science and sports, advertising and public health — a drift toward data-driven discovery and decision-making. It’s a revolution. We’re really just getting under way. But the march of quantification, made possible by enormous new sources of data, will sweep through academia, business and government. There is no area that is going to be untouched.” -------- By Steve Lohr, “The Age of Big data”, The New York Times, 2012.

  13. What is big data? • 3Vs of big data: • High volume, • High-velocity, • High-variety • --- A definition of big data, The Gartner Inc. Simply put, it is big and complex.

  14. The big value of big data The value of big data is that analysis of the big data can lead to enhanced decision making, insight discovery and process optimization. In business, big data can help to identify unknown needs, customize advertisement, monitor and evaluate operation, which leads to big profit and big saving. In science, big data is a huge resource for a lot of scientific discoveries.

  15. A brief introduction of molecular biology

  16. James Watson and Francis Crick DNA

  17. Next generation sequencing

  18. The cost of sequencing has reduced 100 thousand fold in the past 12 years

  19. The little USB drive could do it Oxford Nanopore, long the sleeper project to watch in the field of mapping DNA, just announced two products that could dramatically change the field of DNA sequencing: a new DNA sequencer that may be able to handle a human genome in 15 minutes, and a USB thumb drive DNA sequencer that can read DNA directly from blood with no prep work. “‘Game changer’ is an understatement,” says George Church of Harvard University. (Church was one of the inventors on one of the patents licensed to Oxford Nanopore that led to the device.” He ticks off the devices specs: Tiny instruments for $900. Able to read DNA in 10,000-letter stretches — compared to a couple hundred for current technologies. Able to sequence a human genome in fifteen minutes (although you’d need 20 of the server-size devices coming in 2013, not just the USB stick.)

  20. Nanopore sensing

  21. Data explosion in the era of genomics There have been a large series of breakthroughs in micro-electronics and nano-electronics that have produced instruments that quantify and/or characterize large number of biological molecules in parallel using very small mount of biomaterial. Such technical advances have made possible to comprehensively characterize and quantify the building blocks (DNA, RNA, protein) in a biological system.

  22. Think google …

  23. Or, think Netflix.

  24. Bioinformatics is the key in genomics

  25. Genome, genomics and post genomic era List of sequenced genomes of mammals:

  26. Large Projects • TCGA: The cancer genome Atlas • 1000 Genome Project • 1001 Genome Project • ICGC: International cancer genome consortium • The International HapMap Project • …

  27. Data  Information  Knowledge/power Bioinformatics provides tools to catalyze the transformations

  28. Ion semiconductor sensing Ion Torent

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