This is the ALMA's solution to clone management tasks in DNA arrays experiments. It includes a versatil and flexible database schema suitable to organize and share the DNA arrays data in your institution.

  • Multiwell plate oriented

  • Adaptable to any DNA array facility

  • Minimum hardware and software requirements (dynamic HTML based forms)

  • Developed by biologists with experience in performing DNA arrays experiments.

This is the only system currently available that assists researchers in organizing and managing the large number of biological samples involved in a typical DNA array experiment.

The system provides on-screen simulation of the fabrication of a DNA array, covering all stages from obtaining the primary multiwell plates to the final layout, design and fabrication of the array.

In addition, the system records all manipulations of the multiwell plates made by a liquid-handling robot, so creating an audit trail which is then used to identify, annotate and correct any incidences arising.

For more information on ALMAZen click here.

This is ALMA´s propietary clustering algorithm for microarray analysis. The Self-Organising Tree Algorithm (SOTA), is a neural network that grows adopting the topology of a binary tree. The result of the algorithm is a hierarchical cluster obtained with the accuracy and robustness of a neural network. SOTA clustering confers several advantages over classical clustering methods:

  • SOTA is a divisive method. The growing can be stopped at the desired hierarchical level.

  • A criterion to stop the growing of the tree, based on the approximate distribution of probability obtained by randomisation of the original data set, is provided.

  • Obtaining average gene expression patterns is a built-in feature of the algorithm.

  • Since SOTA runtimes are approximately linear with the number of items to be classified, it is especially suitable for dealing with huge amounts of data.

  • SOTA can be applied to any data providing that they can be coded as a series of numbers and that a computable measure of similarity between data items can be used.

  • SOTA maps Gene Ontology data into the structure of clusters.

You can test the power of the SOTA algorithm on our web-based SOTA server at

JSOTA is an implementation of SOTA that includes a window interface written in Java and the SOTA core written in C++. It is available for a wide range of platforms, including Windows, Linux and Solaris.

You can download for free a demo version of JSOTA and install it on your own computer. If you are interested in purchasing the full version of JSOTA, mail us at .

Now, you can download the full version of JSOTA for free if you are an academic user. Read the following academic user agreement and follow the intructions in it. You have to agree not to use JSOTA for any commercial purpose, and not to tranfer it to any other sites, outside your own research group. After registering, you will receive shortly, a password by e-mail so that you can download the full version of JSOTA from our servers.

JSOTA snapshots


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