We have just released Version 1.0 of ChIPseeqer, a comprehensive framework for analysis of ChIP-seq data. ChIPseeqer contains a very fast and accurate ChIP-seq peak detection program, as well as several others tools to facilitate regulatory element discovery using FIRE, pathway analysis using iPAGE and conservation analyses.
http://physiology.med.cornell.edu/faculty/elemento/lab/chipseq.shtml
Following a request from somebody at Virginia Tech, we have added Rice to the list of organisms supported by FIRE. We used the following gene identifiers:
LOC_Os11g37320
LOC_Os01g12410
LOC_Os01g25110
LOC_Os02g07360
LOC_Os11g45360
etc.
To analyze Rice expression data, you’ll need to download FIRE, then download the rice_data.zip file. The download site is http://tavazoielab.princeton.edu/FIRE/
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.
This is the title of our latest paper in Blood, which is also my first one at Weill Cornell and my first one as (joint) corresponding author.
http://bloodjournal.hematologylibrary.org/cgi/content/abstract/blood-2008-12-193037v1
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
My lab doesn’t work on plants, but following a request from somebody in Malcom Campbell’s lab at U of Toronto, I’ve just added the Poplar to the list of organisms supported by FIRE.
The genome data comes from http://genome.jgi-psf.org/Poptr1_1/
To analyze Poplar expression data, you’ll need to download FIRE, then download the poplar_data.zip file from http://tavazoielab.princeton.edu/FIRE/
I tested it on a clustered dataset of Poplar tissue expression profiles (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6422), and I got very impressive results. The test expression profile is included in the EXPFILES directory in poplar_data.zip file.
Update Feb 18th. Here is more information about how the stimulus money is going to be spent, from Nature :
“National Institutes of Health: $7.4 billion divided among the agency’s scientific institutes and centres will fund grants from a backlog of 14,000 investigator-initiated ‘R01′ grants already reviewed and categorized as “highly meritorious”. It will also fund new R01 applications for projects that could reasonably make progress in two years. The agency will add supplemental funding to existing grants and fund new “challenge grants” aimed at thorny problems.
…
Kington notes: “We are being very careful to focus on funding that only covers the two years of the stimulus package. There will be relatively little, if any, money that entails a four-year commitment.” “
Source: http://www.nature.com/news/2009/090218/full/457942a.html
The very first issue of this exciting new journal is out:
http://genomemedicine.com/
I am co-organizing the new Seminars in Computational and Systems Medicine. David Botstein was our inaugural speaker and both talking to him and hearing his seminar about the coordination of growth rate, cell cycle, stress response and metabolic activity in yeast has been a fantastic experience (his seminar was much more thought-provoking than it sounds).
We are very pleased and honored to have the following speakers coming next :
February 6 – Isidore Rigoutsos
March 6 – Jason Mezey
April 3 – Olga Troyanskaya
May 15 – Dana Pe’er
June 26 – Alexandre Morozov
The seminars will be held in LC-504 (1300 York Avenue, 5th floor). All seminars will be on Fridays at 3pm. You can email me at ole2001@med.cornell.edu for more information.
Chinnaiyan and colleagues have combined 454 and Solexa sequencing to identify transcripts resulting from gene fusion, both in cancer cell lines and primary tumors. The approach relies on deep sequencing the cancer transcriptome and looking for reads that show partial alignment to exon boundaries from different genes. When applied to prostate cancer cell lines and tumors, it detects the well-known TMPRSS-ERG fusion but also discover several novel fusions. It is likely that this approach will lead to the discovery of many novel oncogenes in the near future.
http://www.nature.com/nature/journal/vaop/ncurrent/full/nature07638.html
