Large Scale Analysis of Transcription Factors Recognition Sites May 19, 2009

In an amazing paper published in Science Express, Bulyk, Hughes and colleagues haved used protein-binding microarrays to determine the DNA-binding specificities of 104 mouse transcription factors.

The most interesting finding of the study is that nearly half of all TF seem to be able to bind two distinct types of sequences. For example, Hnf4a appears to bind to both GGTCA and GGTCCA (and this was confirmed by EMSA). This is also the case for BCL6, which I actively study. They also found several clear cases of dependencies between motif positions (not only adjacent positions).

This study provides a very rich dataset that will be extremely useful for those of us who work on transcriptional regulation.

http://www.sciencemag.org/cgi/content/abstract/1162327v1

Mining the Deep Web February 23, 2009

There’s a pretty interesting article in the NY Times about the Deep Web, that is, the data that is stored in databases and available through web interfaces. The article mentions some of the strategies that scientists (and web search companies) use to mine the Deep Web. Essentially, these strategies involve making a first few queries in order to guess the type and structure of the data contained in a given database, then either building a model of the data or making many more targeted queries in order to essentially map out the content of the database. This type of research is particularly interesting for us biologists, since we use many such databases (pubmed, genome browsers, database of gene expression, etc), and these databases are not at all connected with each other. Clearly, tools that automatically query and integrate data from the Deep Web would be very useful for us.

http://www.nytimes.com/2009/02/23/technology/internet/23search.html

Moving to Weill Medical College of Cornell University July 25, 2008

On September 1st, I will be starting my own group at the Weill Medical College of Cornell University in Manhattan. My lab will be located at the Institute for Computational Biomedicine (ICB).

I have many exciting projects in mind, some of them will build on FIRE, FIRE-pro, PAGE and FastCompare, others are completely new. The overall goal will be to understand and predict cellular activity and regulation based on sequence (DNA, RNA and protein). The ultimate goal will be to develop translational research towards understanding diseases such as cancer, neurodegenerative disorders (and I am particularly interested in investigating brain gene expression in order to study the latter) and malaria. Importantly, I will seek close collaborations with experimentalists, and in fact I already have started collaborating with some experimental groups there at Weill.

I also have funds to hire several postdoctoral scientists, so if you are interested in working with me, please send me an email at ole2001@med.cornell.edu

Regulatory elements in the coding region of odorant genes March 13, 2008

Odorant receptors (ORs) constitute one of the largest gene families in mammalian genomes. Odorant receptor genes are expressed in olfactory sensory neurons (OSN), with each OSN stably expressing a single OR protein (from a single allele). How exactly this is achieved is largely unresolved (and fascinating). How do cells chose which OR is going to be expressed ? How do they make sure that only this OR is expressed (which may means that other ORs have to be repressed). In a recent Cell paper (pointed out to me by Yoav), Ryba, Belluscio and colleagues present new exciting data about the underlying mechanisms. In mouse, transgenic odorant receptor genes cannot be expressed in olfactory neurons, when directly under the control of OR promoters. The authors hypothesize that this is due to interactions between regulatory sequences in OR coding regions, and regulatory sequences in OR promoters. So they tried to eliminate this interaction by separating OR promoters from coding sequences. They placed an OR coding sequence under the control of an artificial promoter (TetO), activable by TTA. The TTA-coding gene is itself placed under the control of an OR promoter. Using this system, they managed to express the OR in a good fraction of neurons. In turn, this allowed them to observe that the expressed OR suppresses expression of endogenous ORs. Likewise, TetO-OR expression appears suppressed in neurons where an endogenous OR is expressed. The authors conclude that regulatory sequences in OR coding regions are mediating the inhibition. Identifying these sequences and what binds to them is therefore the next step … it is unclear whether sequence conservation can help, as it appears that the ~1kb OR coding sequences are highly variable.

Princeton molbio profile August 21, 2007

The molbio department just posted a description of my research on its web site.

Stb3 binds to RRPE July 19, 2007

That probably will sound boring to a lot of people, but for people who have done motif analyses of yeast microarray data, that’s a significant finding: the yeast factor that binds to RRPE has finally been identified. The RRPE motif (AAA[AT]TTTT) was found some time ago upstream of many genes involved in ribosomal biogenesis. It is particularly easy to identify computationally because the genes that have it are tightly co-expressed in gene expression profiling experiments (microarrays). Because ribosomal genes (and other growth -involved genes) are down-regulated in response to stress, and many perturbation experiments trigger a stress response, the RRPE motif repeatedly comes up when looking for motifs in clusters of co-expressed genes in microarray experiments. But up to now, nobody had succeeded in identifying the binding factor. Now, using a library of GST-tagged ORFs, and a gel-shift assay, Heideman and colleagues have identified Stb3 as the factor that binds RRPE (and did some pretty convincing follow-up experiments to confirm it). It is by the way interesting that Stb3 does not have a known DNA-binding domain …

http://www.jbc.org/cgi/content/abstract/M704762200v1

Sequencing news June 1, 2007

I have been saying for a little while now that cheap ultra-high-throughput sequencing is going to revolutionize biology. Both Solexa and 454 technologies have already been used to reveal huge numbers of small RNAs, including novel classes (Aravin et al, 2007;Ruby et al, 2006;Rajagopalan et al, 2006) and the presence of miRNAs in unicellular organisms (Zhao et al, 2007).

Now Solexa sequencing has been used to map protein-DNA interactions using chromatin-immunoprecipitation (ChIP), with what looks like impressive accuracy (Johnson et al, 2007).

In parallel, 454 technology (pyrosequencing) was used to sequence James Watson’s genome, in two months and for less than $1M (NY Times article).

FIRE/Fastcompare predicted regulatory elements browser May 30, 2007

This browser combines and shows, for all genes in C. elegans, the predicted regulatory elements obtained from
1) running FIRE on 30+ C. elegans gene expression datasets (FIRE is described in Elemento*, Slonim* and Tavazoie, submitted)
2) comparing the upstream regions and 3′UTRs of C. elegans and C. briggsae (Elemento and Tavazoie, 2005; Chan*, Elemento* and Tavazoie, 2005).

Here is an example for pha-4.

New paper in PLoS Biology May 30, 2007

Our latest paper, “Unmasking Activation of the Zygotic Genome Using Chromosomal Deletions in the Drosophila Embryo” was published in the May edition of PLoS Biology. My first paper with a Nobel laureate as co-author

A supplementary database accompanies the paper.

Meeting report on CSHL Systems Biology 2007 meeting May 1, 2007

Genome Biology just published my report on the meeting.