Featured Websites

MEME-LaB: motif analysis in clusters.

October 5, 2014 |

Analysis of population structures and genome local ancestry has become increasingly important in population and disease genetics. With the advance of next generation sequencing technologies, complete genetic variants in individuals genomes are quickly generated, providing unprecedented opportunities...

ArrayCyGHt: a web application for analysis and visualization of array-CGH data.

October 5, 2014 |

ArrayCyGHt is a web-based application tool for analysis and visualization of microarray-comparative genomic hybridization (array-CGH) data. Full process of array-CGH data analysis, from normalization of raw data to the final visualization of copy number gain or loss, can be straightforwardly achieve...

Designing of peptides with desired half-life in intestine-like environment.

October 5, 2014 |

Merging the forward and reverse reads from paired-end sequencing is a critical task that can significantly improve the performance of downstream tasks, such as genome assembly and mapping, by providing them with virtually elongated reads. However, due to the inherent limitations of most paired-end s...

Detection and characterization of novel sequence insertions using paired-end next-generation sequencing.

October 5, 2014 |

SUMMARY: Picante is a software package that provides a comprehensive set of tools for analyzing the phylogenetic and trait diversity of ecological communities. The package calculates phylogenetic diversity metrics, performs trait comparative analyses, manipulates phenotypic and phylogenetic data, an...

Phenoclustering: online mining of cross-species phenotypes.

October 5, 2014 |

SUMMARY: Recently, several methods for analyzing phenotype data have been published, but only few are able to cope with data sets generated in different studies, with different methods, or for different species. We developed an online system in which more than 300 000 phenotypes from a wide variety ...

Mclip: motif detection based on cliques of gapped local profile-to-profile alignments.

October 5, 2014 |

A multitude of motif-finding tools have been published, which can generally be assigned to one of three classes: expectation-maximization, Gibbs-sampling or enumeration. Irrespective of this grouping, most motif detection tools only take into account similarities across ungapped sequence regions, po...

Towards de novo identification of metabolites by analyzing tandem mass spectra.

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),...

Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes.

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...


October 5, 2014 |

Beta-barrel membrane proteins encoded in various genomes ...

NestedMICA as an ab initio protein motif discovery tool.

October 5, 2014 |

MOTIVATION: With the rapid accumulation of genetic data for a multitude of different species, the availability of intuitive comparative genomic tools becomes an important requirement for the research community. Here we describe a web-based comparative viewer for mapping data, including genetic, phys...

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>