An illuminated view of molecular biology

A report on the 18th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) and the 7th Special Interest Group meeting on Alternative Splicing, Boston, USA, 9-13 July 2010

The Casimir zero-point radiation pressure

We analyze some consequences of the Casimir-type zero-point radiation pressure. These include macroscopic "vacuum" forces on a metallic layer in-between a dielectric medium and an inert ($\epsilon (\omega) = 1$) one. Ways to control the sign of these forces, based on dielectric properties of the media, are thus suggested. Finally, the large positive Casimir pressure, due to surface plasmons on thin metallic layers, is evaluated and discussed.Comment: 4 2-column pages, LATE

AVISPA: a web tool for the prediction and analysis of alternative splicing

Transcriptome complexity and its relation to numerous diseases underpins the need to predict in silico splice variants and the regulatory elements that affect them. Building upon our recently described splicing code, we developed AVISPA, a Galaxy-based web tool for splicing prediction and analysis. Given an exon and its proximal sequence, the tool predicts whether the exon is alternatively spliced, displays tissue-dependent splicing patterns, and whether it has associated regulatory elements. We assess AVISPA's accuracy on an independent dataset of tissue-dependent exons, and illustrate how the tool can be applied to analyze a gene of interest. AVISPA is available at http://avispa.biociphers.org

Model-based detection of alternative splicing signals

Motivation: Transcripts from ∼95% of human multi-exon genes are subject to alternative splicing (AS). The growing interest in AS is propelled by its prominent contribution to transcriptome and proteome complexity and the role of aberrant AS in numerous diseases. Recent technological advances enable thousands of exons to be simultaneously profiled across diverse cell types and cellular conditions, but require accurate identification of condition-specific splicing changes. It is necessary to accurately identify such splicing changes to elucidate the underlying regulatory programs or link the splicing changes to specific diseases

Genomic profiling of human vascular cells identifies TWIST1 as a causal gene for common vascular diseases

Genome-wide association studies have identified multiple novel genomic loci associated with vascular diseases. Many of these loci are common non-coding variants that affect the expression of disease-relevant genes within coronary vascular cells. To identify such genes on a genome-wide level, we performed deep transcriptomic analysis of genotyped primary human coronary artery smooth muscle cells (HCASMCs) and coronary endothelial cells (HCAECs) from the same subjects, including splicing Quantitative Trait Loci (sQTL), allele-specific expression (ASE), and colocalization analyses. We identified sQTLs for TARS2, YAP1, CFDP1, and STAT6 in HCASMCs and HCAECs, and 233 ASE genes, a subset of which are also GTEx eGenes in arterial tissues. Colocalization of GWAS association signals for coronary artery disease (CAD), migraine, stroke and abdominal aortic aneurysm with GTEx eGenes in aorta, coronary artery and tibial artery discovered novel candidate risk genes for these diseases. At the CAD and stroke locus tagged by rs2107595 we demonstrate colocalization with expression of the proximal gene TWIST1. We show that disrupting the rs2107595 locus alters TWIST1 expression and that the risk allele has increased binding of the NOTCH signaling protein RBPJ. Finally, we provide data that TWIST1 expression influences vascular SMC phenotypes, including proliferation and calcification, as a potential mechanism supporting a role for TWIST1 in CAD

Context-specific bayesian clustering for gene expression data

The recent growth in genomic data and measurement of genomewide expression patterns allows to examine gene regulation by transcription factors using computational tools. In this work, we present a class of mathematical models that help in understanding the connections between transcription factors and functional classes of genes based on genetic and genomic data. These models represent the joint distribution of transcription factor binding sites and of expression levels of a gene in a single model. Learning a combined probability model of binding sites and expression patterns enables us to improve the clustering of the genes based on the discovery of putative binding sites and to detect which binding sites and experiments best characterize a cluster. To learn such models from data, we introduce a new search method that rapidly learns a model according to a Bayesian score. We evaluate our method on synthetic data as well as on real data and analyze the biological insights it provides. 1

Context-Specific Bayesian Clustering for Gene Expression Data

The recent growth in genomic data and measurements of genome-wide expression patterns allows us to apply computational tools to examine gene regulation by transcription factors

CIS: Compound Importance Sampling Method for Transcription Factor Binding Site -value Estimation

Transcriptional regulation is mainly obtained by transcription factors that bind sequencespecific binding sites, and control the expression of nearby genes. By modeling such sites one can scan regulatory regions, searching for putative binding sites of a specific factor, thus constructing a genome-wide regulatory network. Recently, several works demonstrated the importance of rich probabilistic models that capture inner-dependencies within binding sites. Here we present a general, accurate and efficient method for estimating the statistical significance of putative binding sites, applicable to any probabilistic binding site model. Finally, we demonstrate the accuracy of the method using synthetic and real-life data