The broad spectrum of demanding applications (CPU intensive, huge memory requirements, storage capacity and I/O bounded algorithms) within genomics presents a challenge for high performance computing.

We drive new solutions that seemed unaffordable only a few years ago. These strategies use parallel computers to solve computationally expensive algorithms that range from computationally regular patterns, such as database searching applications, to very irregularly structured patterns, such as phylogenetic trees. Fine and coarse-grained parallel strategies are addressed for this very diverse set of applications. Different computer architectures are also used, ranging from networks of commodity multi-computers and more powerful workstations to super-computers. In order to avoid the most common sources of inefficiency in parallel computing, our approaches include dynamic load distribution, speculative computation, network-bandwidth optimisation, and intelligent task scheduling.

• 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

Genome comparison is a traditional problem with high memory and CPU time requisites. State-of-the-art software face limitations in terms of computational space to be explored and the amount of memory used during computation. These limitations do not allow comparing very large sequences, therefore our efforts have been concreted in overcoming such limitations. More concretely, we have redesigned the process and we have used HPC techniques in the development of the comparison process. The result is a program able to compare genomes without the limitations of original software.

Starting with the simple collection of ungapped local alignments produced by genome comparison procedures we are also able to detect and identify blocks of large rearrangements taking into account repeats, tandem repeats and duplications. To the best of our knowledge, our approach is the first one available as a coherent workflow, thus outperforming current state-of-the-art software tools. In addition, we are able to classify the type of rearrangement what is not possible in the equivalent software. The results obtained are an important source of information for breakpoints refinement and featuring, as well as for the estimation of the Evolutionary Events frequencies to be used in inter-genome distance proposals, etc.

The field of metagenomics, defined as the direct genetic analysis of uncultured samples of genomes contained within an environmental sample, is gaining increasing popularity. The aim of studies of metagenomics is to determine the species present in an environmental community and identify changes in the abundance of species under different conditions. Our work in this field has been focused on developing new tools and datafile specifications that facilitate the identification of differences in abundance of reads assigned to taxa (mapping), enable the detection of reads of low-abundance bacteria (producing evidence of their presence), provide new concepts for filtering spurious matches, etc. In addition, we work on innovative visualization ideas for improved display of metagenomic diversity are also proposed to better understand how reads are mapped to taxa.

• Óscar Torreño and Oswaldo Trelles. Breaking computational barriers in pairwise genome comparison. BMC Bioinformatics, 2015, 16:250. DOI: 10.1186/s12859-015-0679-9.
• Arjona-Medina, J. A., & Trelles, O. (2015, November). Computational Synteny Block: A framework to identify evolutionary events. In Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on (pp. 5-12). IEEE.
• Arjona-Medina, J. A., & Trelles, O. (2016). Refining borders of genome-rearrangements including repetitions. BMC genomics, 17(8), 433.
• Pérez-Wohlfeil, E., Arjona-Medina, J. A., Torreno, O., Ulzurrun, E., & Trelles, O. (2016). Computational workflow for the fine-grained analysis of metagenomic samples. BMC genomics, 17(8), 351.

The scientific knowledge needed to produce a more complete view of any biological process is scattered across various external, heterogeneous and geographically distributed databases. The heterogeneity in formats and storage media, as well as the diversity and dispersion of data, makes it difficult to use this plethora of interrelated information. As such, the integration of these information sources for unified access is a clear and important technological priority.

In this research area we focus on the design and deployment of integration software architectures, based on metadata repositories and programmatic interfaces to provide necessary features. This includes discovery, invocation and documentation of tools, data persistence systems, etc. The platform is able to access, link, and query biological data sets easily and efficiently by integrating a high number of disperse web-based services.

• MOWServ at the National Institute for Bioinformatics
• Advancing Clinico Genomics Trials (ACGT EU-project)
• jORCA: easily integrating

The general goal of Knowledge Discovery in Databases (KDD) technologies is the extraction and abstraction of patterns, perturbations, relationships or associations from the analysed data. Association rules that disclose hidden co-occurrence of items as a set of antecedents and associated consequents is one of the most useful KDD outcomes.

We have extensively applied KDD techniques in gene-expression experiments (DNA arrays). The experimental outputs are combined to form 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, each row frequently contains additional information about the particular properties of the gene: function, pathway, chromosome location, GO-terms, etc., known as gene-metadata. At the same time, the experiments or samples are described using different descriptors such as precedence, morphology, clinical information, known as sample-metadata.

In this research area, we develop new algorithms to extend the correlation beyond the expression values by taking into account both gene and sample metadata.

• 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 customisable and flexible genomics project management system. pUMA has been used to manage several on-going interdisciplinary projects, including the Spanish Solanaceae project (ESP-SOL) and RIRAAF, amongst others.

• Tomato project (Genoma-España)
• Olive project (Genoma-España)
• Reacciones Adversas a Alergenos y Fármacos

In this area, we apply automatic analysis systems for semantic content recognition in videos of living organisms. The system is able to extract metadata from these videos describing the actions and behaviour of individual organisms (cells, bacteria, mice, etc.). Starting with image processing procedures to identify objects of interest, the system tracks the movements and actions of the organism in the video sequences, detecting and recording the relevant behavioural events.

The metadata obtained that describes the behaviour of the organism are also organised in a database. The database search system enables the retrieval and visualisation of selected video sequences that match a given query-by-content criteria. The efficiency and accuracy of the presented system increases analytical power for uncovering and studying the effect of drugs on a variety of behavioural models.

See VideoCatalyzer

Keywords: Computer vision, video analysis, object recognition, tracking, medicine.

• 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 on the development 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) centralised 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

Our group is focused 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 interacting with life-sciences teams to not only offer technological support for data-management, but also data analysis support.

• Tomato (ESP-SOL project)
• Porcine (infection process in porcine by PCV2)
• Strawberry (involved factors in strawberry ripening)
• Olive-tree (OLEAGEN project)

Image processing and intelligent systems are of vital importance in industrial and biomedical research, dermatological diagnostic applications and surgery, and post-surgical assessment.

In particular, an accurate and objective method of measuring skin or lesion state (e.g. coarseness, pore size, degree of wrinkling) and pigment changes, using measurable quantitative parameters, is required for clinical applications. The true value of skin or lesion “quality” can only be obtained 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. Visual inspection and grading therefore lacks accuracy, and the ability to record and reproduce the measurements in a consistent manner.

The general approach in our developments in this area is to build a sampling catalogue based on both expert and automatic computer assessed tissue quality. By ’tissue quality’, we refer mainly to colour and texture (coarseness, wrinkles, directionality, etc.). Based on this catalogue, differences in tissue quality can be assessed as a function of the differences in the catalogue position of the different samples (i.e. before and after treatment). As such, these differences can be used as an objective comparative measurement of the improvement obtained by treatment.