MOTIVATION: In genetic science, large-scale international research collaborations represent a growing trend. These collaborations have demanding and challenging database, storage, retrieval and communication needs. These studies typically involve demographic and clinical data, in addition to the results from numerous genomic studies (omics studies) such as gene expression, eQTL, genome-wide association and methylation studies, which present numerous challenges, thus the need for data integration platforms that can handle these complex data structures. Inefficient methods of data transfer and access control still plague research collaboration. As science becomes more and more collaborative in nature, the need for a system that adequately manages data sharing becomes paramount. RESULTS: Biology-Related Information Storage Kit (BRISK) is a package of several web-based data management tools that provide a cohesive data integration and management platform. It was specifically designed to provide the architecture necessary to promote collaboration and expedite data sharing between scientists. AVAILABILITY AND IMPLEMENTATION: The software, documentation, Java source code and demo are available at http://genapha.icapture.ubc.ca/brisk/index.jsp. BRISK was developed in Java, and tested on an Apache Tomcat 6 server with a MySQL database. CONTACT: [email protected].

SUMMARY: We present the Sample-based Laboratory Information Management System (SLIMS), a powerful and user-friendly open source web application that provides all members of a laboratory with an interface to view, edit and create sample information. SLIMS aims to simplify common laboratory tasks with tools such as a user-friendly shopping cart for subjects, samples and containers that easily generates reports, shareable lists and plate designs for genotyping. Further key features include customizable data views, database change-logging and dynamically filled pre-formatted reports. Along with being feature-rich, SLIMS' power comes from being able to handle longitudinal data from multiple time-points and biological sources. This type of data is increasingly common from studies searching for susceptibility genes for common complex diseases that collect thousands of samples generating millions of genotypes and overwhelming amounts of data. LIMSs provide an efficient way to deal with this data while increasing accessibility and reducing laboratory errors; however, professional LIMS are often too costly to be practical. SLIMS gives labs a feasible alternative that is easily accessible, user-centrically designed and feature-rich. To facilitate system customization, and utilization for other groups, manuals have been written for users and developers. AVAILABILITY: Documentation, source code and manuals are available at http://genapha.icapture.ubc.ca/SLIMS/index.jsp. SLIMS was developed using Java 1.6.0, JSPs, Hibernate 3.3.1.GA, DB2 and mySQL, Apache Tomcat 6.0.18, NetBeans IDE 6.5, Jasper Reports 3.5.1 and JasperSoft's iReport 3.5.1.

SUMMARY: Traditional methods of genetic study design and analysis work well under the scenario that a handful of single nucleotide polymorphisms (SNPs) independently contribute to the risk of disease. For complex diseases, susceptibility may be determined not by a single SNP, but rather a complex interplay between SNPs. For large studies involving hundreds of thousands of SNPs, a brute force search of all possible combinations of SNPs associated with disease is not only inefficient, but also results in a multiple testing paradigm, whereby larger and larger sample sizes are needed to maintain statistical power. Pathway-based methods are an example of one of the many approaches in identifying a subset of SNPs to test for interaction. To help determine which SNP-SNP interactions to test, we developed Path, a software application designed to help researchers interface their data with biological information from several bioinformatics resources. To this end, our application brings together currently available information from nine online bioinformatics resources including the National Center for Biotechnology Information (NCBI), Online Mendelian Inheritance in Man (OMIM), Kyoto Encyclopedia of Genes and Genomes (KEGG), UCSC Genome Browser, Seattle SNPs, PharmGKB, Genetic Association Database, the Single Nucleotide Polymorphism database (dbSNP) and the Innate Immune Database (IIDB). AVAILABILITY: The software, example datasets and tutorials are freely available from http://genapha.icapture.ubc.ca/PathTutorial.

Abstract Summary: Multi-dimensional Automated Clustering Genotyping Tool (MACGT) is a Java application that clusters complex multi-dimensional vector data derived from single nucleotide polymorphism (SNP) genotyping experiments using mini-sequencing based microarray chemistries such as arrayed primer extension (APEX). Spot intensity output files from microarray experiments across multiple samples are imported into MACGT. The datasets can include four channels of intensity data for each spot, replica spots for each SNP probe and multiple probe types (APEX and allele-specific APEX probes) on both DNA strands for each SNP. MACGT automatically clusters these multi-dimensionality datasets for each SNP across multiple samples. Incorporation of additional array datasets from known samples that have previously validated SNP genotype calls allows unknown samples to be automatically assigned a genotype based on the clustering, along with numerical measures of confidence for each genotype call. Calling accuracy by MACGT exceeds 98% when applied to genotyping data from APEX microarrays, and can be increased to >99.5% by applying thresholds to the confidence measures. Availability: MACGT is open source and is freely available (under a GNU General Public License) from the iCAPTURE Centre web site, . Contact: [email protected] Supplementary information: Additional information, including Supplementary Figure S1, test data and a user's manual, is available from the iCAPTURE Centre web site (see above).

Abstract Summary: SNP Chart is a Java application for the visualization and interpretation of microarray genotyping data primarily derived from arrayed primer extension-based chemistries. Spot intensity output files from microarray analysis tools are imported into SNP Chart, together with a multi-channel TIFF image of the original array experiment and a list of the actual single nucleotide polymorphisms (SNPs) being tested. Data from different and/or replicate probes that interrogate the same SNP, but that are scattered across the array grid, can be reassembled into a single chart format, specific for the SNP. This allows a quick and very effective ‘visualization’/‘quality control’ of the data from multiple probes for the same SNP that can be easily interpreted and manually scored as a genotype. Availability: http://www.snpchart.ca Contact: [email protected] Supplementary information: A comprehensive manual describing SNP Chart is available at the above website, together with sample data files.