In 2007 I finished my M.Sc studies at Tel Aviv University, under the supervision of Prof. David Horn (physics) and Prof. Eytan Ruppin (computer science & medicine). My M.Sc research was in the field of bioinformatics and machine learning. In my research I dealt with the extraction of regulatory motifs from promoter regions in the genome and understanding aspects of the mechanism by which gene regulation occurs.

In 2005 I graduated the Tel Aviv University Interdisciplinary Program for Fostering Excellence. My undergraduate studies as a student of the program included an individually designed interdisciplinary curriculum. I aimed to acquire knowledge in various aspects of computational biology and machine learning; I have mostly studied mathematics, computer science and biology and got to taste a bit of economics, psychology, history & arts.

Liat Segal , Michal Lapidot , Zach Solan , Eytan Ruppin , Yitzhak Pilpel, David Horn
Nucleotide variation of regulatory motifs may lead to distinct expression patterns
Bioinformatics Vol. 23, pages i440-i449.

Motivation: Current methodologies for the selection of putative transcription factor binding sites (TFBS) rely on various assumptions such as over-representation of motifs occurring on gene promoters, and the use of motif descriptions such as consensus or position-specific scoring matrices (PSSMs). In order to avoid bias introduced by such assumptions, we apply an unsupervised motif extraction (MEX) algorithm to sequences of promoters. The extracted motifs are assessed for their likely cis-regulatory function by calculating the expression coherence (EC) of the corresponding genes, across a set of biological conditions.
Results: Applying MEX to all Saccharomyces cerevisiae promoters, followed by EC analysis across 40 biological conditions, we obtained a high percentage of putative cis-regulatory motifs. We clustered motifs that obtained highly significant EC scores, based on both their sequence similarity and similarity in the biological conditions these motifs appear to regulate. We describe 20 clusters, some of which regroup known TFBS. The clusters display different mRNA expression profiles, correlated with typical changes in the nucleotide composition of their relevant motifs. In several cases, a variation of a single nucleotide is shown to lead to distinct differences in expression patterns. These results are confronted with additional information, such as binding of transcription factors to groups of genes. Detailed analysis is presented for clusters related to MCB/SCB, STRE and PAC. In the first two cases, we provide evidence for different binding mechanisms of different clusters of motifs. For PAC-related motifs we uncover a new cluster that has so far been overshadowed by the stronger effects of known PAC motifs.