SUMMARY: The evolutionary history of gene families can be complex due to duplications and losses. This complexity is compounded by the large number of genomes simultaneously considered in contemporary comparative genomic analyses. As provided by several orthology databases, hierarchical orthologous groups (HOGs) are sets of genes that are inferred to have descended from a common ancestral gene within a species clade. This implies that the set of HOGs defined for a particular clade correspond to the ancestral genes found in its last common ancestor. Furthermore, by keeping track of HOG composition along the species tree, it is possible to infer the emergence, duplications and losses of genes within a gene family of interest. However, the lack of tools to manipulate and analyse HOGs has made it difficult to extract, display and interpret this type of information. To address this, we introduce interactive HOG analysis method, an interactive JavaScript widget to visualize and explore gene family history encoded in HOGs and python HOG analysis method, a python library for programmatic processing of genes families. These complementary open source tools greatly ease adoption of HOGs as a scalable and interpretable concept to relate genes across multiple species. AVAILABILITY AND IMPLEMENTATION: iHam's code is available at https://github.com/DessimozLab/iHam or can be loaded dynamically. pyHam's code is available at https://github.com/DessimozLab/pyHam and or via the pip package 'pyham'.

UNLABELLED: There is an increasing need for rich and dynamic biological data visualizations in bioinformatic web applications. New standards in web technologies, like SVG or Canvas, are now supported by most modern web browsers allowing the blossoming of powerful visualizations in biological data analysis. The exploration of different ways to visualize genomic data is still challenging due to the lack of flexible tools to develop them. Here, I present a set of libraries aimed at creating powerful tree- and track-based visualizations for the web. Its modularity and rich API facilitate the development of many different visualizations ranging from simple species trees to complex visualizations comprising per-node data annotations or even simple genome browsers. AVAILABILITY AND IMPLEMENTATION: The TnT libraries have been written in Javascript, licensed under the APACHE 2.0 license and hosted at https://github.com/tntvis CONTACT: [email protected].

Abstract Motivation: We present a Web service to access Ensembl data using Representational State Transfer (REST). The Ensembl REST server enables the easy retrieval of a wide range of Ensembl data by most programming languages, using standard formats such as JSON and FASTA while minimizing client work. We also introduce bindings to the popular Ensembl Variant Effect Predictor tool permitting large-scale programmatic variant analysis independent of any specific programming language. Availability and implementation: The Ensembl REST API can be accessed at http://rest.ensembl.org and source code is freely available under an Apache 2.0 license from http://github.com/Ensembl/ensembl-rest . Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

SUMMARY: There is increasing evidence showing that co-expression of genes that cluster along the genome is a common characteristic of eukaryotic transcriptomes. Several algorithms have been used to date in the identification of these kinds of gene organization. Here, we present a web tool called CROC that aims to help in the identification and analysis of genomic gene clusters. This method has been successfully used before in the identification of chromosomal clusters in different eukaryotic species. AVAILABILITY: The web server is freely available to non-commercial users at the following address: http://metagenomics.uv.es/CROC/.

Abstract Contact: [email protected]