Itsik Pe'er - Teaching

Current:

This course is an advanced graduate-level seminar, motivated by current technologies dubbed "Next Generation Sequencing", that revolutionize genomics by offering data acquisition at throughput and cost-effectiveness three orders of magnitude better than previously available. The class is intended to introduce already-interdisciplinary students of both computational and bio-medical skill sets to state of the art applications and computational techniques in high throughput sequencing, preparing them for research and method development in the field. A prerequisite is an introductory class in computational genomics (4761 or equivalent - please contact instructor for specific approval) and the intention is to maintain a small class that would facilitate participation in discussions. Computational topics covered include string matching, the Burrows-Wheeler transform, hidden Markov models and focused at practical issues in analysis of terabytes of data. Biological applications focus at individual genomic sequencing of humans, but include also cancer sequencing, transcriptome sequencing, and metagenomics. Tentative list of papers to be covered is available [Here]
Time: Currently set to Mon 2:10-4, starting Sep 14th, to be rescheduled
Place: 252 Engineering Terrace

Research Projects COMS 3998, 4901, 6901, 6902, 9911, 9800, 9910 and BINF 6001

I offer several research-oriented projects, each with potential to be pursued to different depths, to fit different levels of credit requirements. Our lab has had positive experiences with students as early in their career as their freshman/sophomore summer for particular projects, as long as the student has the skills to be an independent programmer. We also offer graduate-level projects (Computer Science MSc or Computational Biology rotation), although we strongly recommend students with no prior research experience not to take on a research project during their first semester in Columbia. Specific projects currently available:

1. Development of a GUI for other tools in the group
2. Simulation and evaluation for recently developed methods
3. Compilation of a database of a genealogy for the human species.

The success of any project typically hinges on the level of commitment that a student can dedicate to research. Students are encouraged to apply, but advised to consider the burden of a research project on their time, commensurate with a non-project course with the same academic credit, and weigh that in when considering taking on other courses while they are doing research. Please send your CV if you are interested.

Previous:
Computational Genomics CBMF 4761

Technology for obtaining DNA sequences have been consistently improving faster than Moore's law. This has opened a wealth of computational challenges in weaving the heaps of straw of DNA sequence data into gold of biological insight. The class serves as an introduction to computational genomics, explaining the basic challenges and teaching the general computer-science tools to tackle them. [Spring '09]. Introduction to Computer Science and Programming in MATLAB, emphasis on applications in the life sciences COMS W1005.002

Data Structures and Algorithms in C COMS W3133:

Seminar in Computational Genomics COMS W4995-2

This course is intended to introduce students of both computational and bio-medical skill sets to current quantitative understanding of mammalian genomics and prepare them to computational research in the field. The course is interdisciplinary in nature, aiming at a broad scope of Topics include: Sequencing of the human genome, vertebrate genomes, highlighting parts of the genome, sequence conservation, sequence variation, structural mutations, primate evolution. The computational toolbox discussed includes parameter inference, likelihood analysis, hidden Markov and other graphical models, approximate string matching and other algorithms. [Spring '07]

Computational Human Genetics COMS W4995/E6998

This course is intended to introduce students of both computational and bio-medical skill sets to current quantitative understanding of human genetics and prepare them to computational research in the field. Topics include: genetics of a single site, coalescence with recombination, history of humans, mapping rare mutations through linkage, mapping common variants through association, isolated and admixed populations, natural selection, copy number changes, model organisms, and genotyping technologies. The computational toolbox discussed includes parameter inference, likelihood analysis, hidden Markov and other graphical models, eigenvalue decompositions, and classification problems. [Fall'07] [Fall'06]