Since its release in 2000, WormBase (http://www.wormbase.org) has grown from a small resource focusing on a single species and serving a dedicated research community, to one now spanning 15 species essential to the broader biomedical and agricultural research fields. To enhance the rate of curation, we have automated the identification of key data in the scientific literature and use similar methodology for data extraction. To ease access to the data, we are collaborating with journals to link entities in research publications to their report pages at WormBase. To facilitate discovery, we have added new views of the data, integrated large-scale datasets and expanded descriptions of models for human disease. Finally, we have introduced a dramatic overhaul of the WormBase website for public beta testing. Designed to balance complexity and usability, the new site is species-agnostic, highly customizable, and interactive. Casual users and developers alike will be able to leverage the public RESTful application programming interface (API) to generate custom data mining solutions and extensions to the site. We report on the growth of our database and on our work in keeping pace with the growing demand for data, efforts to anticipate the requirements of users and new collaborations with the larger science community.

High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called “ReactomeFIViz”, which utilizes a highly reliable gene functional interaction network combined with human curated pathways derived from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.

The Database for Bacterial Group II Introns (http://webapps2.ucalgary.ca/~groupii/index.html#) provides a catalogue of full-length, non-redundant group II introns present in bacterial DNA sequences in GenBank. The website is divided into three sections. The first section provides general information on group II intron properties, structures and classification. The second and main section lists information for individual introns, including insertion sites, DNA sequences, intron-encoded protein sequences and RNA secondary structure models. The final section provides tools for identification and analysis of intron sequences. These include a step-by-step guide to identify introns in genomic sequences, a local BLAST tool to identify closest intron relatives to a query sequence, and a boundary-finding tool that predicts 5′ and 3′ intron–exon junctions in an input DNA sequence. Finally, selected intron data can be downloaded in FASTA format. It is hoped that this database will be a useful resource not only to group II intron and RNA researchers, but also to microbiologists who encounter these unexpected introns in genomic sequences.