Featured Websites

ReMark: an automatic program for clustering orthologs flexibly combining a Recursive and a Markovclustering algorithms.

October 5, 2014 |

SUMMARY: ReMark is a fully automatic tool for clustering orthologs by combining a Recursive and a Markov clustering (MCL) algorithms. The ReMark detects and recursively clusters ortholog pairs through reciprocal BLAST best hits between multiple genomes running software program (RecursiveClustering.j...

Decombinator: a tool for fast, efficient gene assignment in T-cell receptor sequences using a finite state machine.

October 5, 2014 |

Comparative methods for RNA secondary structure prediction use evolutionary information from RNA alignments to increase prediction accuracy. The model is often described in terms of stochastic context-free grammars (SCFGs), which generate a probability distribution over secondary structures. It is, ...


October 5, 2014 |

Human coding SNPs mapped onto protein domains ...

CSA - Catalytic Site Atlas

October 5, 2014 |

Enzyme active sites and catalytic residues in enzymes of known 3D structure ...

Logical modelling of the role of the Hh pathway in the patterning of the Drosophila wing disc.

October 5, 2014 |

In the Arabidopsis thaliana regulatory element analyzer (AtREA) server, we have integrated sequence data, genome-wide expression data and functional annotation data in three application modules which will be useful to identify major regulatory targets of a user-provided cis-regulatory element (CRE),...

Using a mutual information-based site transition network to map the genetic evolution of influenza A/H3N2 virus.

October 5, 2014 |

SUMMARY: ERNEST Reaction Network Equilibria Study Toolbox is a MATLAB package which, by checking various different criteria on the structure of a chemical reaction network, can exclude the multistationarity of the corresponding reaction system. The results obtained are independent of the rate consta...

LegumeTFDB: an integrative database of Glycine max, Lotus japonicus and Medicago truncatula transcription factors.

October 5, 2014 |

We have established a database named LegumeTFDB to provide access to transcription factor (TF) repertoires of three major legume species: soybean (Glycine max), Lotus japonicus and Medicago truncatula. LegumeTFDB integrates unique information for each TF gene and family, including sequence features,...

Adding Some SPICE to DAS.

October 5, 2014 |

SUMMARY: The distributed annotation system (DAS) defines a communication protocol used to exchange biological annotations. It is motivated by the idea that annotations should not be provided by single centralized databases but instead be spread over multiple sites. Data distribution, performed by DA...


October 5, 2014 |

Protein families and domains ...

EGAN: exploratory gene association networks.

October 5, 2014 |

Exploratory Gene Association Networks (EGAN) is a Java desktop application that provides a point-and-click environment for contextual graph visualization of high-throughput assay results. By loading the entire network of genes, pathways, interactions, annotation terms and literature references direc...

Protein-protein binding affinity prediction on a diverse set of structures.

MOTIVATION: Accurate binding free energy functions for protein-protein interactions are imperative for a wide range of purposes. Their construction is predicated upon ascertaining the factors that influence binding and their relative importance. A recent benchmark of binding affinities has allowed, for the first time, the evaluation and construction of binding free energy models using a diverse set of complexes, and a systematic assessment of our ability to model the energetics of conformational changes. RESULTS: We construct a large set of molecular descriptors using commonly available tools, introducing the use of energetic factors associated with conformational changes and disorder to order transitions, as well as features calculated on structural ensembles. The descriptors are used to train and test a binding free energy model using a consensus of four machine learning algorithms, whose performance constitutes a significant improvement over the other state of the art empirical free energy functions tested. The internal workings of the learners show how the descriptors are used, illuminating the determinants of protein-protein binding. AVAILABILITY: The molecular descriptor set and descriptor values for all complexes are available in the supplementary. A web server for the learners and coordinates for the bound and unbound structures can be accessed from the website: http://bmm.cancerresearchuk.org/%7EAffinity CONTACT: paul.bates@cancer.org.uk.

Reconstructing transcription factor activities in hierarchical transcription network motifs.

MOTIVATION: A knowledge of the dynamics of transcription factors is fundamental to understand the transcriptional regulation mechanism. Nowadays an experimental measure of transcription factor activities in vivo represents a challenge. Several methods have been developed to infer these activities from easily measurable quantities such as mRNA expression of target genes. A limitation of these methods is represented by the fact that they rely on very simple single-layer structures, typically consisting of one or more transcription factors regulating a number of target genes. RESULTS: We present a novel statistical inference methodology to reverse engineer the dynamics of transcription factors in hierarchical network motifs such as feed-forward loops. The approach we present is based on a continuous time representation of the system where the high level master transcription factor is represented as a two state Markov jump process driving a system of differential equations. We solve the inference problem using an efficient variational approach and demonstrate our method on simulated data and two real datasets. The results on real data show that the predictions of our approach can capture biological behaviours in a more effective way than single-layer models of transcription, and can lead to novel biological insights. AVAILABILITY: http://homepages.inf.ed.ac.uk/gsanguin/software.html CONTACT: g.sanguinetti@ed.ac.uk.

survcomp: an R/Bioconductor package for performance assessment and comparison of survival models.

SUMMARY: The survcomp package provides functions to assess and statistically compare the performance of survival/risk prediction models. It implements state-of-the-art statistics to (i) measure the performance of risk prediction models, (ii) combine these statistical estimates from multiple datasets using a meta-analytical framework, and (iii) statistically compare the performance of competitive models. AVAILABILITY: The R/Bioconductor package survcomp is provided open source under the Artistic-2.0 License with a user manual containing installation, operating instructions and use case scenarios on real datasets. survcomp requires R version 2.13.0 or higher.URL: http://bioconductor.org/packages/release/bioc/html/survcomp.html CONTACT: Benjamin Haibe-Kains <bhaibeka@jimmy.harvard.edu>, Markus Schröder <mschroed@jimmy.harvard.edu>