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SIMS install SIMS user guide
SIMS configuration guide
SIMS installation guide
SIMS tutorial  SIMS source
AIMS quick install AIMS user guide
AIMS installation guide
AIMS tutorial
GWA tutorial
git://git.simbioms.org/git/AIMS.git
SAIL quick install SAIL user guide SAIL tutorial git://git.simbioms.org/git/SAIL.git
Emanta install git://git.simbioms.org/git/emanta.git

 

Sample and Assay Information Management Systems (SIMS and AIMS)

As the names suggest, SIMS is designed to collect phenotypical, environmental and technical information about samples, while AIMS handles the experimental data from high-throughput assays. SIMS provides a simple solution for data anonymization by creating identifiers linked to person’s information in a separate module.  The main features include customizability and compatibility with standard data formats MAGE-TAB and ISA-TAB. AIMS is designed to adapt for any highthrouput technological platform (genomics, molecular, imaging etc) and for easy extraction of captured data for analysis. It is linked to SIMS through a three-level hierarchy: a person can be linked to one or more samples, a sample can have one or more aliquots. Each aliquot can have one or more assays performed on it, and each assay can be linked to one or more data files. Assays are grouped in experiments and studies, each of which can have one or more data files attached. For instance, raw microarray data files would be normally linked to individual assays, while normalized gene expression matrices to experiments. Assays are technology-specific; the current AIMS configurations includegenotyping, sequencing, proteomics and metabonomics.SIMS and AIMS central objects are shown here.

The two systems can be installed and used independently, or jointly—if a laboratory already has a local informatics system for sample or assay data, it can be used jointly with AIMS or SIMS, respectively. Publication

Availability search for samples (SAIL)

Biomedical studies are growing rapidly in both size and complexity. Sample collection sizes are approaching millions of samples, with thousands of phenotypes or other types of data collected. Technical solutions are needed to browse, index, annotate and manage such complex content. We have developed a system addressing this need, SAIL (“Sample Availability System”). The system is designed to hold phenotype availability information and meta data about samples,experiments and phenotypes, submitted by data owners or databases that contain actual measurement data. By being geared towards a complexity level that focus on describing data and availability rather than the measurements themselves, SAIL provides a way to browse and summarise complex content across diverse resources. This can be used in many different scenarios from designing new studies, understanding previous studies, and finding data and biomaterial from different cohorts that can be combined in meta-analysis studies. SAIL also contains functionality to annotate collections, with tools to create new vocabularies or use terms from standard sample ontologies, and to combine and harmonise vocabularies. The system can handle real valued data as well as availability data, which is useful to match study samples from different cohorts and to find the availability of samples in certain ranges of study parameters or measurements. The SAIL system is an easy and flexible tool for researchers and co-ordinators of sample collections to browse, understand and update large and complex content across many diverse resources to maximize their scientific value and usability. Publication

Administration of a collaborative study (Emanta)

As cross-institutional collaboration is becoming more and more prevalent, managing such collaboration is also becoming more laborious. To ease the workload of managers and principal investigators, we have developed a web application that eases access rightsmanagement, provides a central repository for project documentation and information and facilitates scheduling and reporting.



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