Archive by category | Bioinformatics

[Research highlight] Cis-regulatory evolution, not so mysterious after all?

Animal genomes are littered with conserved non-coding elements (CNEs)—most of which represent evolutionarily constrained cis-regulatory sequences—however, it is often not clear why these sequences are so exceptionally conserved, since anecdotal examples have shown that orthologous CNEs can have divergent functions in vivo (Strähle and Rastegar 2008; Elgar and Vavouri 2008). In an article recently published in Molecular Biology & Evolution, Ritter et al. compare the functional activities of 41 pairs of orthologous conserved non-coding elements (CNEs) from humans and zebrafish (2010). Interestingly, sequence similarity was found to be a poor predictor of which CNEs had conserved function. In contrast, the authors found that measuring transcription factor binding site change, instead of simple sequence divergence, improves their ability to predict functional conservation.  Read more

The role of neutral mutations in the evolution of phenotypes

The role of neutral mutations in the evolution of phenotypes

In a recent opinion piece, Andreas Wagner tries to reconcile the tension between proponents of neutral evolution and selectionism (Wagner 2008). He argues that “neutral mutations prepare the ground for later evolutionary innovation”. Wagner illustrates this point using a network model of genotype-phenotype relationships (Wagner 2005). In a so-called ‘neutral network’, nodes correspond to distinct genotypes associated with the same phenotype and are connected by an edge if the respective genotypes differ only by a single mutation event (eg point mutation). Examples of neutral networks include different genotypes coding for RNA or protein structures. In this representation, highly connected networks correspond to robust phenotypes that are not very sensitive to changes in genotype. Wagner notes the zinc finger fold as an impressive example of a highly connected neutral network as its structure remains essentially the same even after mutating all but seven of its 26 residues to alanine.  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

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

What would you do with a petaflop?

The Blue Brain Project of the Ecole Polytechnique Fédérale de Lausanne (EPFL) currently uses an IBM Blue Gene/L supercomputer, reaching peaks of 22.4 teraflops (flops=Floating Point Operations Per Second), to simulate a model of a mammalian neocortical column composed of 10’000 neurons (Markram, 2006). IBM recently announced the release of the new Blue Gene/P, which, in its largest configuration, would be more than 100 times more powerful than the EPFL Blue Gene/L, reaching peaks of 3000 teraflops (3 petaflops, 3×1015 flops, 100’000 times more powerful than a home PC).  Read more

E. coli counts in base 117

E. coli counts in base 117

Finding general laws on the organization principle of living organisms is a particularly difficult task in biology but certainly a central one in systems biology. Part of the difficulty in this endeavor is probably linked to the fact that “by its very nature, life is both contingent and particular, each organism the product of eons of tinkering, of building on what had accumulated over the course of a particular evolutionary trajectory” (Keller, 2007, see also our post). Such laws are thus particularly significant when they emerge from evolutionary constraints alone. In a recent paper published in PNAS, Matthew Wright and colleagues may well provide such an example by looking at the “”http://dx.doi.org/10.1073/pnas.0610776104″>chromosomal periodicity of evolutionarily conserved gene pairs” (Wright et al, 2007).  Read more

Connecting disease state to genetic modules

Connecting disease state to genetic modules

Diseases such as cancer are often related to collaborative effects involving interactions of multiple genes within complex pathways, or to combinations of multiple SNPs. To understand the structure of such mechanisms, it is helpful to analyze genes in terms of the purely cooperative, as opposed to independent, nature of their contributions towards a phenotype (Anastassiou, 2007).  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