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PhD student, Computer Science Department, Technion - IIT
Phylogenetic reconstruction is the task of determining the structure (topology) of the evolutionary tree over a given set of species. This is typically done using an alignment of genetic sequences extracted from the species in the set. Reconstruction of evolutionary trees is a central task in comparative genomics which is used as a basic step in many applications. One of the main challenges in this field is to design reconstruction algorithms which provide a good tradeoff between the length of input sequences and the extent of accurate reconstruction.
In the past decade much attention has been focused on studying fast converging reconstruction algorithms, which guarantee correct reconstruction of any tree from sequence of (asymptotically) minimal length. However, when the tree in question contains even a single short edge, the sequence length required by these methods might be too long for any practical purpose. Short edges are prevalent in phylogenies and are notoriously hard to reconstruct. They result from two subsequent speciation events which are separated by little evolutionary change. A famous example is the human-chimp-gorilla separation.
In this talk we present a novel fast converging algorithm which is the first one to guarantee correct reconstruction of every edge in the 'true' tree whose weight exceeds a certain weight threshold. In a sense, giving up on edges which are too short to reliably reconstruct from the given input allows us to reliably reconstruct sufficiently long edges, and longer alignments allow reconstruction of shorter edges. This property (which we propose to term adaptive fast convergence), together with the low running time of our algorithm (quadratic in the number of species), makes it appealing for practical purposes.
This is a joint work with Sagi Snir and Shlomo Moran.
Host: Shlomo Moran
IBM Research, Tel-Aviv
Gene expression is directly regulated by protein transcription factors that bind at particular DNA or RNA sites in a sequence specific manner. A comprehensive characterization of these functional non-coding elements, or motifs, remains a formidable challenge, especially for higher eukaryotes.
I will present a rigorous computational methodology for ab-initio motif discovery from expression data, that utilizes the concept of mutual information, and have the following characteristics:
(i) directly applicable to _any_ type of expression data
I will present results for a variety of data types, measured for different organisms, including yeast, worm, fly, human, and the Plasmodium parasite responsible for malaria. I will further discuss in detail surprising observations regarding gene expression regulation that were overlook by previous studies and naturally arise of our analysis.
Based on joint work with Olivier Elemento and Saeed Tavazoie.
Host: Dan Geiger
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