Models and algorithms for genomic sequences, proteins, and networks of
Speaker: Serafim Batzoglou , Stanford
Date: May 1 2006
Time: 11:30AM to 1:00PM
Location: TOC Lab 32-G575
Host: P Clote- BC & B Berger- MIT
Contact: Kathleen Dickey, 617 253 3037, kvdickey@mit.edu
Relevant URL: http://www-math.mit.edu/compbiosem/This talk has two parts: the first part is on new ways to model and analyze
biological sequences, which are the most abundant kinds of genomic data; the
second part describes methods for constructing and comparing interaction
networks, which are emerging as canonical data sets of the post-genomic era.
Algorithms for biological sequence analysis. One of the most fruitful
developments in bioinformatics in the past decade was the wide adoption of
Hidden Markov Models (HMMs) and related graphical models to an array of
applications such as gene finding, sequence alignment, and non-coding RNA
folding. Conditional Random Fields (CRFs) are a recent alternative to HMMs,
and provide two main advantages: (1) they enable more elaborate modeling of
biosequences by allowing us to conveniently describe and select rich feature
sets. For example, when comparing two residues during protein alignment,
using a CRF allows leveraging in a principled manner the chemical properties
of the neighborhood of those residues. (2) CRFs allow training of parameters
in a way that is more effective for making predictions on new input
sequences. I will describe three practical CRF-based tools that improve upon
state-of-the-art methods in terms of accuracy: CONTRAlign, a protein
aligner; CONTRAST, a gene finder; and CONTRAfold, a method for predicting
the secondary structure of non-coding RNAs. Our tools are available at
http://contra.stanford.edu.
Networks of protein interactions. Graphs that summarize pairwise
interactions between all proteins of an organism have emerged as canonical
data sets that can be constructed using multiple sources of functional
genomic data. We construct protein interaction networks for all sequenced
microbes by rigorously integrating information extracted from genomic
sequences as well as microarrays and other predictors of pairwise
interactions. We then align these networks in multiple species using
Graemlin, a tool that we developed for that purpose, and search for modules
(subgraphs) of proteins that exhibit homology as well as conservation of
pairwise interactions among many organisms. Graemlin provides substantial
speed and sensitivity gains compared to previous network alignment methods;
it can be used to compare microbial networks at
http://graemlin.stanford.edu.
MIT
Department of Mathematics
& The Theory of
Computation Group
at CSAIL
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