• Data and services integration (more info)
The catalogued scientific knowledge needed to produce a more complete view of any biological process is scattered across various external, heterogeneous and geographically distributed databases. Heterogeneity in formats and storage media, besides the diversity and dispersion of data, make difficult to use this plethora of interrelated information. The integration of these information sources for unified access, independently of possible internal changes, is a clear and important technological priority.
In this research area we focus in the design and deplyment or integration software architectures, based on metadata repositories and programatic interfases to provide necessary functionalities, for example discovery, invocation and documentation of tools, data persistence systems, etc. The platform is able to access, link, combine and query biological data sets easily and efficiently by integrating a high number of disperse web-based services.
Related Clients
| MOWServ at the National Institute for Bioinformatics : |  |
| Advancing Clinico Genomics Trials (ACGT EU-project): |  |
| jORCA: easily integration at: |  |
The general goal of Knowledge Discovering from Databases (KDD) technologies is the extraction and abstraction of any type of pattern, perturbation, relationship or association from the analysed data. One of the most useful KDD outcomes are in the form of association rules that disclose hidden co-ocurrence of items as a set of antecedents and associated consequents.
We have strongly applied KDD techniques in gene-expresion experiments (DNA arrays). The outputs from the reading devices, representing one experiment, are combined to conform a consolidated table with the expression values of genes (rows) in the different experiments (columns). The main component of this table is the gene expression-matrix. However, frequently each row also contains additional information about the particular properties of the gene: function, pathway, chromosome location, GO-terms, etc., that are called gene-metadata. At the same time, also the experiments or samples are described using different descriptors about the procedance, morphology, clinical information, etc. that are named sample-metadata.
In this research area we develop new algorithms to extend the correlation beyond the expression values including both, genes and sample metadata.
Related Clients
ARco: Association rule collaborative tool:
A high number of genomics projects are well underway producing large quantities of comparative genomic data leading a growth of the information management problems. In this context, experiment data management become a time-consuming and prone to error task due to the diversity of interrelated datatypes, high volume of data produced in geographically disperse laboratories and the needs for flexible and powerful access to query, combine, share, and protect the information. In this research area we have designed a generic, customisable and flexible genomics’ project management system, named pUMA (projects at the UMA) a web-based open-source application to support information management problems in genomic projects. pUMA has been used to manage several interdisciplinary ongoing projects, including the Spanish Solanaceae project (ESP-SOL), olive; riraaf; etc.
Implementations
| Tomato project (Genoma-España): |  |
| Olive project (Genoma-España): |  |
| Reacciones Adversas a Alergenos y Fármacos: |  |
Genomics has also challenged high-performance computing with a broad spectrum of hard demanding applications (CPU intensive, huge memory requirements, storage capacity and I/O bounded algorithms). We drive new solutions that seemed unaffordable only few years ago. Those strategies use parallel computers to solve computationally expensive algorithms whose computational patterns range from regular, such as database searching applications, to very irregularly structured patterns (phylogenetic trees). Fine- and coarse-grained parallel strategies have been addressed for these very diverse set of applications. Diverse computer architectures have also been studied, ranging from networks of commodity multi-computers and more powerful workstations to super-computers. Our parallel approaches include dynamic load distribution, speculative computation, network-bandwidth optimisation, intelligent task scheduling, as a way to avoid the most common sources of inefficiency in parallel computing.
References
* O.Trelles-Salazar, E.L.Zapata, J.M.Carazo; “On an efficient parallelization of exhaustive sequence comparison algorithms”; Computer Applications in BioSciences (1994) 10(5):509-511
* Ceron, C., Dopazo, J., Zapata, E.L., Carazo, J.M. and Trelles, O.; “Parallel Implementation for DNAml Program on Message-Passing Architectures”; Parallel Computing and Applications, vol 24 (5-6),June 98 pp.701-716
* Trelles O., Andrade M.A., Valencia A., Zapata E.L., and Carazo J.M.; “Computational Space Reduction and Parallelization of a new Clustering Approach for Large Groups of Sequences”; Bioinformatics vol.14 no.5 1998 (pp.439-451); (formerly CABIOS)
* Trelles O.; “On the parallelisation of bioinformatics applications”; Briefings in bioinformatics (May, 2001) vol.2 (2) pp. 181-194
* Trelles Oswaldo and Rodríguez Andrés; “Parallel Metaheuristics in Bioinformatics: A new class of algorithms”; Wiley Series on Parallel and Distributed Computing, Edited by: Enrique Alba, (ISBN-13-978-0-471-67806-9) pp: 517-549
In this area we apply automatic video analysis systems for semantic content recognition in living-organism’s videos. The system is able to extract from such videos metadata describing the actions and behavior of individual organisms (cells, bacteria, mice, etc). Starting with image processing procedures to identify the objects of interest, the system tracks the movements and actions of such organism in the video sequences, detecting and recording the relevant behavioral events. The metadata obtained, that describe the organism behavior present at the video content, are also organized in a database allowing a by-content accessing of the videos, obtaining important analytical information from the subsequent queries. The database search system enables the retrieving and visualization of selected video sequences matching a given query-by-content criteria. The efficiency and accuracy of the presented system increase analytical power for uncovering and studying the effect of drugs on a variety of behavioral models.
Keywords: Computer vision, video analysis, object recognition, tracking, medicine.
See VideoCatalyzer: 
References
* Rodriguez, A.; Shotton,D.; Guil, N. and Trelles, O.; “Automatic analysis of the content of cell biological video and database organization of their metadata descriptors”; IEEE Transactions in Multimedia (February 2004) Vol 6, No. 1, pages 119-128
* Rodríguez, A.; Shotton,D.; Guil, N. and Trelles, O.; “Analysis and Description of the Semantic Content of Cell Biological Videos”; Multimedia Tools and Applications, 25, 37-58, 2005
* Manuel J. Martín-Vázquez; Mario A. Trelles; Alejandro Sola; R. Glen Calderhead and Oswaldo
We work in the developments of a controlled collaborative teaching environment tool specifically focused on emulating a real classroom environment. The main features this tool offers are: a) controlled access based on a central registry; b) distributed architecture that allows simultaneous online classrooms; c) centralized control of the students intercommunications; d) simulation of requests to speak ("raise hands") in the classroom; e) optimized bandwidth usage to reduce unneeded transmission, offering fluid communication between the participants; f) easy and intuitive interface, even for very young children; g) set of teaching resources such as blackboard and display of presentations.
See: ODISEA: On-Line synchronous distance learning
We Also maintain a virtual campus at: www.bitlab-es.com/e-learning
Our group is focussed on computational methodologies for the analysis and interpretation of large-scale expression data sets generated by DNA micro-array experiments (see PreP and double-Scan www.bitlab-es.com/prep and engene www.bitlab-es.com/engenet software). Moreover, our group has a long tradition of interfacing with life-sciences teams to offer not only technological support for data-management but also data analysis support.
Refs: tomato (ESP-SOL project), porcine (infection process in porcine by PCV2), strawberry (involved factors in strawberry ripening) and olive-tree (OLIGEN project).
Image processing an intelligent systems are of essential importance in industrial and biomedical research, clinical diagnostic applications in dermatology and aesthetic surgery, and in assessing the results of that surgery. Particularly in clinical applications, the importance of an accurate and objective method of measuring skin or lesions state (e.g. coarseness, pore size, degree of wrinkling) and pigment changes, using measurable quantitative parameters, is very clear. The true value of skin or leion “quality” can only be realized in this manner, because visual inspection and grading is subjective and is affected by several factors, including: viewing geometry, ambient illumination, assessor’s experience and visual acuity, etc., and therefore lacks accuracy and the ability to record and reproduce the measurements consistently.
The general approach in pur developments in this area is to build a sampling catalog based on both expert and automatic computer assessed tissue quality. By ‘tissue quality’ we mean mainly color and texture (coarseness, wrinkles, directionality, et cetera). Based on this catalog, differences in tissue quality can be assessed as a function of the differences in the catalog position of the different samples (i.e. before and after treatment). These differences can be used as an objective comparative measurement of the improvement obtained by treatment.
