José F. Aldana-Montes

Intelligent client for integrating bioinformatics services

MOTIVATION In addition to existing bioinformatics software, a lot of new tools are being developed world wide to supply services for an ever growing, widely dispersed and heterogeneous collection of biological data. The integration of these resources under a common platform is a challenging task. To this end, several groups are developing integration technologies, in which services are usually registered in some sort of catalogue to allow novel discovering and accessing mechanisms to be implemented. However, each service demands specific interfaces to accommodate their parameters and it is a complicated task linking the different service inputs and outputs to solve a biological problem. RESULTS In this work we address the design and implementation of a versatile web client to access BioMOBY compatible services (a system by which a client can interact with multiple sources of biological data regardless of the underlying format or schema) using the service description stored in the BioMOBY catalogue. The automatic interface generator significantly reduces developing time and produces uniform service access mechanisms. The design and proof of concept (for such a client) including the generic interface generator have been developed and implemented in the National Institute for Bioinformatics in Spain. AVAILABILITY The INB (National Institute for Bioinformatics, Spain) platform is available at www.inab.org/MOWServ

Evaluation of two heuristic approaches to solve the ontology meta-matching problem

Nowadays many techniques and tools are available for addressing the ontology matching problem, however, the complex nature of this problem causes existing solutions to be unsatisfactory. This work aims to shed some light on a more flexible way of matching ontologies. Ontology meta-matching, which is a set of techniques to configure optimum ontology matching functions. In this sense, we propose two approaches to automatically solve the ontology meta-matching problem. The first one is called maximum similarity measure, which is based on a greedy strategy to compute efficiently the parameters which configure a composite matching algorithm. The second approach is called genetics for ontology alignments and is based on a genetic algorithm which scales better for a large number of atomic matching algorithms in the composite algorithm and is able to optimize the results of the matching process.Nowadays many techniques and tools are available for addressing the ontology matching problem, however, the complex nature of this problem causes existing solutions to be unsatisfactory. This work aims to shed some light on a more flexible way of matching ontologies. Ontology meta-matching, which is a set of techniques to configure optimum ontology matching functions. In this sense, we propose two approaches to automatically solve the ontology meta-matching problem. The first one is called maximum similarity measure, which is based on a greedy strategy to compute efficiently the parameters which configure a composite matching algorithm. The second approach is called genetics for ontology alignments and is based on a genetic algorithm which scales better for a large number of atomic matching algorithms in the composite algorithm and is able to optimize the results of the matching process.

Solving molecular flexible docking problems with metaheuristics

Graphical abstractDisplay Omitted HighlightsFour algorithms (two GAs, PSO, and DE) are used to solve molecular docking problems.A set of 83 docking instances is generated based on real existing molecules.A thorough experimentation with statistical assessment of results is performed.Relevant solutions are analyzed from the point of view of their biological meaning. The main objective of the molecular docking problem is to find a conformation between a small molecule (ligand) and a receptor molecule with minimum binding energy. The quality of the docking score depends on two factors: the scoring function and the search method being used to find the lowest binding energy solution. In this context, AutoDock 4.2 is a popular C++ software package in the bioinformatics community providing both elements, including two genetic algorithms, one of them endowed with a local search strategy. This paper principally focuses on the search techniques for solving the docking problem. In using the AutoDock 4.2 scoring function, the approach in this study is twofold. On the one hand, a number of four metaheuristic techniques are analyzed within an extensive set of docking problems, looking for the best technique according to the quality of the binding energy solutions. These techniques are thoroughly evaluated and also compared with popular well-known docking algorithms in AutoDock 4.2. The metaheuristics selected are: generational and a steady-state Genetic Algorithm, Differential Evolution, and Particle Swarm Optimization. On the other hand, a C++ version of the jMetal optimization framework has been integrated inside AutoDock 4.2, so that all the algorithms included in jMetal are readily available to solve docking problems. The experiments reveal that Differential Evolution obtains the best overall results, even outperforming other existing algorithms specifically designed for molecular docking.

Polyamines: metabolism to systems biology and beyond

Summary.Polyamines and the metabolic and physiopathological processes in which they are involved represent an active field of research that has been continuously growing since the seventies. In the last years, the trends in the focused areas of interest within this field since the 1970s have been confirmed. The impact of “-omics” in polyamine research remains too low in comparison with its deep impact on other biological research areas. These high-throughput approaches, along with systems biology and, in general, more systemic and holistic approaches should contribute to a renewal of this research area in the near future.

ReprOlive: a database with linked data for the olive tree (Olea europaea L.) reproductive transcriptome.

Plant reproductive transcriptomes have been analyzed in different species due to the agronomical and biotechnological importance of plant reproduction. Here we presented an olive tree reproductive transcriptome database with samples from pollen and pistil at different developmental stages, and leaf and root as control vegetative tissues http://reprolive.eez.csic.es). It was developed from 2,077,309 raw reads to 1,549 Sanger sequences. Using a pre-defined workflow based on open-source tools, sequences were pre-processed, assembled, mapped, and annotated with expression data, descriptions, GO terms, InterPro signatures, EC numbers, KEGG pathways, ORFs, and SSRs. Tentative transcripts (TTs) were also annotated with the corresponding orthologs in Arabidopsis thaliana from TAIR and RefSeq databases to enable Linked Data integration. It results in a reproductive transcriptome comprising 72,846 contigs with average length of 686 bp, of which 63,965 (87.8%) included at least one functional annotation, and 55,356 (75.9%) had an ortholog. A minimum of 23,568 different TTs was identified and 5,835 of them contain a complete ORF. The representative reproductive transcriptome can be reduced to 28,972 TTs for further gene expression studies. Partial transcriptomes from pollen, pistil, and vegetative tissues as control were also constructed. ReprOlive provides free access and download capability to these results. Retrieval mechanisms for sequences and transcript annotations are provided. Graphical localization of annotated enzymes into KEGG pathways is also possible. Finally, ReprOlive has included a semantic conceptualisation by means of a Resource Description Framework (RDF) allowing a Linked Data search for extracting the most updated information related to enzymes, interactions, allergens, structures, and reactive oxygen species.

Semantically Enhanced Recommender Systems

Recommender Systems have become a significant area in the context of web personalization, given the large amount of available data. Ontologies can be widely taken advantage of in recommender systems, since they provide a means of classifying and discovering of new information about the items to recommend, about user profiles and even about their context. We have developed a semantically enhanced recommender system based on this kind of ontologies. In this paper we present a description of the proposed system.

Systems biology metabolic modeling assistant

SUMMARY We present Systems Biology Metabolic Modeling Assistant (SBMM Assistant), a tool built using an ontology-based mediator, and designed to facilitate metabolic modeling through the integration of data from repositories that contain valuable metabolic information. This software can be used for the visualization, design and management of metabolic networks; selection, integration and storage of metabolic information; and as an assistant for kinetic modeling. AVAILABILITY SBMM Assistant for academic use is freely available at http://www.sbmm.uma.es.

jMetalCpp: optimizing molecular docking problems with a C++ metaheuristic framework

MOTIVATION Molecular docking is a method for structure-based drug design and structural molecular biology, which attempts to predict the position and orientation of a small molecule (ligand) in relation to a protein (receptor) to produce a stable complex with a minimum binding energy. One of the most widely used software packages for this purpose is AutoDock, which incorporates three metaheuristic techniques. We propose the integration of AutoDock with jMetalCpp, an optimization framework, thereby providing both single- and multi-objective algorithms that can be used to effectively solve docking problems. RESULTS The resulting combination of AutoDock + jMetalCpp allows users of the former to easily use the metaheuristics provided by the latter. In this way, biologists have at their disposal a richer set of optimization techniques than those already provided in AutoDock. Moreover, designers of metaheuristic techniques can use molecular docking for case studies, which can lead to more efficient algorithms oriented to solving the target problems. AVAILABILITY AND IMPLEMENTATION  jMetalCpp software adapted to AutoDock is freely available as a C++ source code at http://khaos.uma.es/AutodockjMetal/.

A Survey on Disk Oriented Querying and Reasoning on the Semantic Web

This paper presents a description of seven systems, which use database technology to both represent knowledge persistently and make scalable queries on it, in the Semantic Web context. From the study of these systems we can deduce that a lot of work regarding massive storage of knowledge has already been carried out. We can observe an evolution from RDF to OWL in most up-to-date systems. The analyzed approaches carry out a mapping between the (structure of the) ontology and the data model of the persistence management system. Nevertheless, there is still considerable work to be done regarding efficient disk oriented Abox (ontology instances) querying and reasoning. We think that due to the Semantic Web features many problems related to assertional reasoning should be addressed. We also believe that the physical design of the knowledge base is a very important task, which includes the development of both specific storage structures and indexes for knowledge storage and retrieval.This paper presents a description of seven systems, which use database technology to both represent knowledge persistently and make scalable queries on it, in the Semantic Web context. From the study of these systems we can deduce that a lot of work regarding massive storage of knowledge has already been carried out. We can observe an evolution from RDF to OWL in most up-to-date systems. The analyzed approaches carry out a mapping between the (structure of the) ontology and the data model of the persistence management system. Nevertheless, there is still considerable work to be done regarding efficient disk oriented Abox (ontology instances) querying and reasoning. We think that due to the Semantic Web features many problems related to assertional reasoning should be addressed. We also believe that the physical design of the knowledge base is a very important task, which includes the development of both specific storage structures and indexes for knowledge storage and retrieval.

Semantic similarity measurement using historical google search patterns

Computing the similarity between terms (or short text expressions) that have the same meaning but which are not lexicographically similar is a key challenge in the information integration field. The problem is that techniques for textual semantic similarity measurement often fail to deal with words not covered by synonym dictionaries. In this paper, we try to solve this problem by determining the semantic similarity for terms using the knowledge inherent in the search history logs from the Google search engine. To do that, we have designed and evaluated four algorithmic methods for measuring the semantic similarity between terms using their associated history search patterns. These algorithmic methods are: a) frequent co-occurrence of terms in search patterns, b) computation of the relationship between search patterns, c) outlier coincidence on search patterns, and d) forecasting comparisons. We have shown experimentally that some of these methods correlate well with respect to human judgment when evaluating general purpose benchmark datasets, and significantly outperform existing methods when evaluating datasets containing terms that do not usually appear in dictionaries.