Archive by category | Data integration

[Research highlight] modENCODE releases extensive functional investigation of fly and worm genomes

Recently, a series of publications by members of the modENCODE consortium were released online at Science, Nature, and Genome Research. These works collectively describe a massive effort to functionally characterize and annotate the Drosophila melanogaster and Caenorhabditis elegans genomes, including in-depth analyses of genes and transcripts, epigenetic marks, transcription factor binding, and replication timing, across a range of developmental and tissue sources.  Read more

Keystone Symposium – Omics Meets Cell Biology (II)

Keystone Symposium – Omics Meets Cell Biology (II)

Before I carry on with a summary of the second part of the Keystone Symposium ‘Omics Meets Cell Biology’, I should clarify that this post and the previous one dedicated to this conference are not intended to provide an comprehensive account of all the talks but rather to communicate some general (and subjective) impressions of the meeting. To keep these posts reasonably short (and sometimes due to a lack of memory…), I had to omit several of the excellent presentations given at this meeting. The full program and complete list of speakers is available at the Keystone Symposium website.  Read more

Keystone Symposium – Omics Meets Cell Biology (I)

Keystone Symposium – Omics Meets Cell Biology (I)

At the Keystone Symposium ‘OMICS Meets Cell Biology’, held this week in Breckenridge, Colorado, attendees had initially to face two major challenges: the first was to survive the cocktail mixing jet lag and altitude sickness and the second one—oh, it hurts!— was to resist the temptation to just forget all about science and focus exclusively on the concepts revolving around snow, slopes and fun sports…  … Read more

SciFoo: scientific fireworks

SciFoo: scientific fireworks

In his list of eight ‘generative’ values (Better Than Free), Kevin Kelly includes ’embodiment’–the actual physical realization of an item or event which could be otherwise freely distributed over the web. While we are all ‘hyperlinked’ on the Internet, the value of those unique qualities that cannot be generated or “copied” on the web is dramatically increased. The type of intense emulation and shared excitement sparked at the recent Science Foo Camp (SciFoo 2008), organized by Nature, Google and O’Reilly, gave a wonderful example of the unique value of direct human exchange during an exclusive event bringing together roughly 200 top scientists, ‘geeks’ and other technologists at the Googleplex in Mountain View, California.  Read more

Top-down mapping of gene regulatory pathways

Top-down mapping of gene regulatory pathways

In a very recent lecture (see full video from NIH VideoCasting) given for the NIH Systems Biology Special Interest Group, Trey Ideker presents a great overview of the various strategies his group has been developing in the recent years in order to integrate multiple types of large scale datasets. While one of the most pervasive ‘meme’ about high-throughput measurement is that they are “notoriously unreliable” (see Hakes et al, 2008, for a recent example), Trey beautifully illustrates how predictive computational models and novel biological insights can be generated by sophisticated data integration strategies. Three types of applications are presented in his talk:  … Read more

Transcription paused and poised for regulation

Transcription paused and poised for regulation

For eukaryotes, it is widely thought that transcription is primarily regulated through recruitment of the essential machinery to transcription start-sites. Previous hints challenging this paradigm have been confirmed by recent analyses showing that transcription regulation of a large number of genes actually occurs after recruitment. Mechanistically, such studies have gone furthest in Drosophila melanogaster (Muse et al, 2007; Zeitlinger et al, 2007). Here, conservative estimates indicate that more than 10% of genes are regulated through promoter-proximal pausing. On such genes, RNA polymerase II is recruited and initiates transcription, but then pauses around 50 bp downstream of the transcription start-site where it awaits further signals to resume elongation and complete transcription proper.  Read more

Consumer Health Information Technology

Consumer Health Information Technology

I highly recommend to visit the NIH VideoCasting page, which hosts many interesting video/podcasts. Even if I realize that this is a bit old according to the blogosphere time scale, I would like to point to this one: “The Future: Consumer Health Information Technology”, featuring talks given at a NCI-sponsored meeting on Dec 10, 2007 by Adam Bosworth (formerly “Google Health architect”, now starting his own company Keas), Bern Shen (Intel) and Bill Crounse (Microsoft).  Read more

Semantic zooming of networks

One can only agree with Euan Adie, that “the way we present genomic and proteomic data on the web sucks” (read post on Nascent). And this holds for biological networks: depiction of protein-protein interactions as colorful hairballs results in impressive figures but is not obligatorily very useful. While the network representation is a powerful abstract representation of biological processes, it is trivial to say that a graph (with its jungle of nodes and edges) is far from resembling even remotely to an actual living cell as you see it under the microscope… In the crude visualization of biological process as simple graphs, space, time, multi-scale structure and biological context are missing.  Read more

Analyzing time-series expression data

Analyzing time-series expression data

Ziv Bar-Joseph and colleague describe their new method Dynamic Regulatory Events Miner (DREM) to analyze time-series gene expression data and combine them with static ChIP-chip experiments. The expression profiles are modeled using an extension of Hidden Markov Model that enforces a tree structure onto the expression profiles. The technique allows to deduce the condition-specific or time-dependent activity of transcription factors that explain the observed expression profiles.  Read more