Featured Websites

BioXSD: the common data-exchange format for everyday bioinformatics web services.

October 5, 2014 |

SUMMARY: Enumeration of the dense sub-graphs of a graph is of interest in community discovery and membership problems, including dense sub-graphs that overlap each other. Described herein is ODES (Overlapping DEnse Sub-graphs), pthreads parallelized software to extract all overlapping maximal sub-gr...

Large-scale learning of combinatorial transcriptional dynamics from gene expression.

October 5, 2014 |

SurvJamda (Survival prediction by joint analysis of microarray data) is an R package that utilizes joint analysis of microarray gene expression data to predict patients survival and risk assessment. Joint analysis can be performed by merging datasets or meta-analysis to increase the sample size and ...

Robust synthetic biology design: stochastic game theory approach.

October 5, 2014 |

SUMMARY: LINKDATAGEN is a perl tool that generates linkage mapping input files for five different linkage mapping tools using data from all 11 HAPMAP Phase III populations. It provides rudimentary error checks and is easily amended for personal linkage mapping preferences. Availability and Implement...

Systematic assignment of thermodynamic constraints in metabolic network models.

October 5, 2014 |

The R package mosclust (model order selection for clustering problems) implements algorithms based on the concept of stability for discovering significant structures in bio-molecular data. The software library provides stability indices obtained through different data perturbations methods (resampli...

LASAGNA: a novel algorithm for transcription factor binding site alignment.

October 5, 2014 |

Combining heterogeneous sources of data is essential for accurate prediction of protein function. The task is complicated by the fact that while sequence-based features can be readily compared across species, most other data are species-specific. In this paper, we present a multi-view extension to G...

ORFDB

October 5, 2014 |

Collection of ORFs that are sold by Invitrogen ...

Inferring cluster-based networks from differently stimulated multiple time-course gene expression data.

October 5, 2014 |

MOTIVATION: Functional similarity between proteins is evident at both the sequence and structure levels. SeSAW is a web-based program for identifying functionally or evolutionarily conserved motifs in protein structures by locating sequence and structural similarities, and quantifying these at the l...

MiRonTop: mining microRNAs targets across large scale gene expression studies.

October 5, 2014 |

SUMMARY: Current challenges in microRNA (miRNA) research are to improve the identification of in vivo mRNA targets and clarify the complex interplay existing between a specific miRNA and multiple biological networks. MiRonTop is an online java web tool that integrates DNA microarrays or high-through...

QuadBase

October 5, 2014 |

G-quadruplex motifs in the promoters of human, chimpanzee, rat, mouse and bacterial genes ...

DIP - Database of Interacting Proteins

October 5, 2014 |

Experimentally-determined protein-protein interactions ...


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>