Daniel Glez-Peña

ALTER: program-oriented conversion of DNA and protein alignments

ALTER is an open web-based tool to transform between different multiple sequence alignment formats. The originality of ALTER lies in the fact that it focuses on the specifications of mainstream alignment and analysis programs rather than on the conversion among more or less specific formats. In addition, ALTER is capable of identify and remove identical sequences during the transformation process. Besides its user-friendly environment, ALTER allows access to its functionalities in a programmatic way through a Representational State Transfer web service. ALTER’s front-end and its API are freely available at http://sing.ei.uvigo.es/ALTER/ and http://sing.ei.uvigo.es/ALTER/api/, respectively.

AIBench: A rapid application development framework for translational research in biomedicine

Applied research in both biomedical discovery and translational medicine today often requires the rapid development of fully featured applications containing both advanced and specific functionalities, for real use in practice. In this context, new tools are demanded that allow for efficient generation, deployment and reutilization of such biomedical applications as well as their associated functionalities. In this context this paper presents AIBench, an open-source Java desktop application framework for scientific software development with the goal of providing support to both fundamental and applied research in the domain of translational biomedicine. AIBench incorporates a powerful plug-in engine, a flexible scripting platform and takes advantage of Java annotations, reflection and various design principles in order to make it easy to use, lightweight and non-intrusive. By following a basic input-processing-output life cycle, it is possible to fully develop multiplatform applications using only three types of concepts: operations, data-types and views. The framework automatically provides functionalities that are present in a typical scientific application including user parameter definition, logging facilities, multi-threading execution, experiment repeatability and user interface workflow management, among others. The proposed framework architecture defines a reusable component model which also allows assembling new applications by the reuse of libraries from past projects or third-party software.

WhichGenes: a web-based tool for gathering, building, storing and exporting gene sets with application in gene set enrichment analysis

WhichGenes is a web-based interactive gene set building tool offering a very simple interface to extract always-updated gene lists from multiple databases and unstructured biological data sources. While the user can specify new gene sets of interest by following a simple four-step wizard, the tool is able to run several queries in parallel. Every time a new set is generated, it is automatically added to the private gene-set cart and the user is notified by an e-mail containing a direct link to the new set stored in the server. WhichGenes provides functionalities to edit, delete and rename existing sets as well as the capability of generating new ones by combining previous existing sets (intersection, union and difference operators). The user can export his sets configuring the output format and selecting among multiple gene identifiers. In addition to the user-friendly environment, WhichGenes allows programmers to access its functionalities in a programmatic way through a Representational State Transfer web service. WhichGenes front-end is freely available at http://www.whichgenes.org/, WhichGenes API is accessible at http://www.whichgenes.org/api/.

Mass-Up: an all-in-one open software application for MALDI-TOF mass spectrometry knowledge discovery.

BackgroundMass spectrometry is one of the most important techniques in the field of proteomics. MALDI-TOF mass spectrometry has become popular during the last decade due to its high speed and sensitivity for detecting proteins and peptides. MALDI-TOF-MS can be also used in combination with Machine Learning techniques and statistical methods for knowledge discovery. Although there are many software libraries and tools that can be combined for these kind of analysis, there is still a need for all-in-one solutions with graphical user-friendly interfaces and avoiding the need of programming skills.ResultsMass-Up, an open software multiplatform application for MALDI-TOF-MS knowledge discovery is herein presented. Mass-Up software allows data preprocessing, as well as subsequent analysis including (i) biomarker discovery, (ii) clustering, (iii) biclustering, (iv) three-dimensional PCA visualization and (v) classification of large sets of spectra data.ConclusionsMass-Up brings knowledge discovery within reach of MALDI-TOF-MS researchers. Mass-Up is distributed under license GPLv3 and it is open and free to all users at http://sing.ei.uvigo.es/mass-up.

geneCBR: a translational tool for multiple-microarray analysis and integrative information retrieval for aiding diagnosis in cancer research

BackgroundBioinformatics and medical informatics are two research fields that serve the needs of different but related communities. Both domains share the common goal of providing new algorithms, methods and technological solutions to biomedical research, and contributing to the treatment and cure of diseases. Although different microarray techniques have been successfully used to investigate useful information for cancer diagnosis at the gene expression level, the true integration of existing methods into day-to-day clinical practice is still a long way off. Within this context, case-based reasoning emerges as a suitable paradigm specially intended for the development of biomedical informatics applications and decision support systems, given the support and collaboration involved in such a translational development. With the goals of removing barriers against multi-disciplinary collaboration and facilitating the dissemination and transfer of knowledge to real practice, case-based reasoning systems have the potential to be applied to translational research mainly because their computational reasoning paradigm is similar to the way clinicians gather, analyze and process information in their own practice of clinical medicine.ResultsIn addressing the issue of bridging the existing gap between biomedical researchers and clinicians who work in the domain of cancer diagnosis, prognosis and treatment, we have developed and made accessible a common interactive framework. Our geneCBR system implements a freely available software tool that allows the use of combined techniques that can be applied to gene selection, clustering, knowledge extraction and prediction for aiding diagnosis in cancer research. For biomedical researches, geneCBR expert mode offers a core workbench for designing and testing new techniques and experiments. For pathologists or oncologists, geneCBR diagnostic mode implements an effective and reliable system that can diagnose cancer subtypes based on the analysis of microarray data using a CBR architecture. For programmers, geneCBR programming mode includes an advanced edition module for run-time modification of previous coded techniques.ConclusiongeneCBR is a new translational tool that can effectively support the integrative work of programmers, biomedical researches and clinicians working together in a common framework. The code is freely available under the GPL license and can be obtained at http://www.genecbr.org.

BioDR: Semantic indexing networks for biomedical document retrieval

In Biomedical research, retrieving documents that match an interesting query is a task performed quite frequently. Typically, the set of obtained results is extensive containing many non-interesting documents and consists in a flat list, i.e., not organized or indexed in any way. This work proposes BioDR, a novel approach that allows the semantic indexing of the results of a query, by identifying relevant terms in the documents. These terms emerge from a process of Named Entity Recognition that annotates occurrences of biological terms (e.g. genes or proteins) in abstracts or full-texts. The system is based on a learning process that builds an Enhanced Instance Retrieval Network (EIRN) from a set of manually classified documents, regarding their relevance to a given problem. The resulting EIRN implements the semantic indexing of documents and terms, allowing for enhanced navigation and visualization tools, as well as the assessment of relevance for new documents.

BioAnnote: A software platform for annotating biomedical documents with application in medical learning environments

Automatic term annotation from biomedical documents and external information linking are becoming a necessary prerequisite in modern computer-aided medical learning systems. In this context, this paper presents BioAnnote, a flexible and extensible open-source platform for automatically annotating biomedical resources. Apart from other valuable features, the software platform includes (i) a rich client enabling users to annotate multiple documents in a user friendly environment, (ii) an extensible and embeddable annotation meta-server allowing for the annotation of documents with local or remote vocabularies and (iii) a simple client/server protocol which facilitates the use of our meta-server from any other third-party application. In addition, BioAnnote implements a powerful scripting engine able to perform advanced batch annotations.

Fast human serum profiling through chemical depletion coupled to gold-nanoparticle-assisted protein separation

The use of chemical protein depletion in conjunction with gold-based nanoparticles for fast matrix assisted laser desoption ionization time of flight mass spectrometry-based human serum profiling was assessed. The following variables influencing the process were optimized: (i) amount of nanoparticles, (ii) sample pH, (iii) amount of protein and (iv) incubation time. pH was found the most important factor to be controlled, with an optimum range comprised between 5.8 and 6.4. The minimum incubation time to obtain an adequate profiling was 30 min. Using this approach, serum from five patients with lymphoma, five patients with myeloma and from two healthy volunteers were correctly classified using Principal component analysis.

A novel ensemble of classifiers that use biological relevant gene sets for microarray classification

Since the introduction of DNA microarray technology, there has been an increasing interest on clinical application for cancer diagnosis. However, in order to effectively translate the advances in the field of microarray-based classification into the clinic area, there are still some problems related with both model performance and biological interpretability of the results. In this paper, a novel ensemble model is proposed able to integrate prior knowledge in the form of gene sets into the whole microarray classification process. Each gene set is used as an informed feature selection subset to train several base classifiers in order to estimate their accuracy. This information is later used for selecting those classifiers comprising the final ensemble model. The internal architecture of the proposed ensemble allows the replacement of both base classifiers and the heuristics employed to carry out classifier fusion, thereby achieving a high level of flexibility and making it possible to configure and adapt the model to different contexts. Experimental results using different datasets and several gene sets show that the proposal is able to outperform classical alternatives by using existing prior knowledge adapted from publicly available databases.

Web scraping technologies in an API world

Web services are the de facto standard in biomedical data integration. However, there are data integration scenarios that cannot be fully covered by Web services. A number of Web databases and tools do not support Web services, and existing Web services do not cover for all possible user data demands. As a consequence, Web data scraping, one of the oldest techniques for extracting Web contents, is still in position to offer a valid and valuable service to a wide range of bioinformatics applications, ranging from simple extraction robots to online meta-servers. This article reviews existing scraping frameworks and tools, identifying their strengths and limitations in terms of extraction capabilities. The main focus is set on showing how straightforward it is today to set up a data scraping pipeline, with minimal programming effort, and answer a number of practical needs. For exemplification purposes, we introduce a biomedical data extraction scenario where the desired data sources, well-known in clinical microbiology and similar domains, do not offer programmatic interfaces yet. Moreover, we describe the operation of WhichGenes and PathJam, two bioinformatics meta-servers that use scraping as means to cope with gene set enrichment analysis.