Featured Websites

ABCGrid: Application for Bioinformatics Computing Grid.

October 5, 2014 |

We have developed a package named Application for Bioinformatics Computing Grid (ABCGrid). ABCGrid was designed for biology laboratories to use heterogeneous computing resources and access bioinformatics applications from one master node. ABCGrid is very easy to install and maintain at the premise o...


October 5, 2014 |

Database portal for marine ecological genomics ...

A structure filter for the Eukaryotic Linear Motif Resource.

October 5, 2014 |

SUMMARY: High-throughput RNA sequencing (RNA-seq) is rapidly emerging as a major quantitative transcriptome profiling platform. Here, we present DEGseq, an R package to identify differentially expressed genes or isoforms for RNA-seq data from different samples. In this package, we integrated three e...

Visual and statistical comparison of metagenomes.

October 5, 2014 |

SUMMARY: InterMap3D predicts co-evolving protein residues and plots them on the 3D protein structure. Starting with a single protein sequence, InterMap3D automatically finds a set of homologous sequences, generates an alignment and fetches the most similar 3D structure from the Protein Data Bank (PD...

FusionMap: detecting fusion genes from next-generation sequencing data at base-pair resolution.

October 5, 2014 |

SUMMARY: JABAWS is a web services framework that simplifies the deployment of web services for bioinformatics. JABAWS:MSA provides services for five multiple sequence alignment (MSA) methods (Probcons, T-coffee, Muscle, Mafft and ClustalW), and is the system employed by the Jalview multiple sequence...

AutoFACT: an automatic functional annotation and classification tool.

October 5, 2014 |

BACKGROUND: Assignment of function to new molecular sequence data is an essential step in genomics projects. The usual process involves similarity searches of a given sequence against one or more databases, an arduous process for large datasets. RESULTS: We present AutoFACT, a fully automated and cu...

HUGE - Human Unidentified Gene-Encoded large proteins

October 5, 2014 |

Large (>50 kDa) human proteins and cDNA sequences ...

rHVDM: an R package to predict the activity and targets of a transcription factor.

October 5, 2014 |

SUMMARY: Highly parallel genomic platforms like microarrays often present researchers with long lists of differentially expressed genes but contain little or no information on how these genes are regulated. rHVDM is a novel R package which uses gene expression time course data to predict the activit...

ProteomeCommons.org JAF: reference information and tools for proteomics.

October 5, 2014 |

SUMMARY: Analysis of proteomics data, specifically mass spectrometry data, commonly relies on libraries of known information such as atomic masses, known stable isotopes, atomic compositions of amino acids, observed modifications of known amino acids and ion masses that directly correspond to known ...

DV-Curve: a novel intuitive tool for visualizing and analyzing DNA sequences.

October 5, 2014 |

Top scoring pairs (TSPs) are pairs of genes whose relative rankings can be used to accurately classify individuals into one of two classes. TSPs have two main advantages over many standard classifiers used in gene expression studies: (i) a TSP is based on only two genes, which leads to easily interp...

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>