Itsik Pe'er -
Teaching
|
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 '12].
Previous: [Spring '11]
[Spring '10].
[Spring '09].
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:
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:
[Spring '09].
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. Topics for Fall ’11 focus on diverse
applications: RNA-seq, ChIP-seq,
FAIRE-seq, cancer sequencing, and personal genomics [list
of papers]
Time: Mon 2:10-4
Place: 253 Engineering Terrace
[Fall ’09
Papers]
COMS1005
has long been an introductory course aimed at providing non-CS majors with
basic programming skills, an asset in many fields requiring automated
quantitative analysis. Material includes programming with the MATLAB language
and software development environment, fundamentals of computer science, and
examples from physics, evolution, genomics, imaging, and other applications. [Fall '10][Fall
'08 - emphasis on applications in the life sciences].
This
class is intended for non-CS engineering majors seeking to strengthen their
theoretical understanding of computing and practical programming skills.
Further information on the Spring 2008 class is available through CourseWorks .
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]
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]