Francisco Ortuño

Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columns

MOTIVATIONnMultiple sequence alignments (MSAs) are widely used approaches in bioinformatics to carry out other tasks such as structure predictions, biological function analyses or phylogenetic modeling. However, current tools usually provide partially optimal alignments, as each one is focused on specific biological features. Thus, the same set of sequences can produce different alignments, above all when sequences are less similar. Consequently, researchers and biologists do not agree about which is the most suitable way to evaluate MSAs. Recent evaluations tend to use more complex scores including further biological features. Among them, 3D structures are increasingly being used to evaluate alignments. Because structures are more conserved in proteins than sequences, scores with structural information are better suited to evaluate more distant relationships between sequences.nnnRESULTSnThe proposed multiobjective algorithm, based on the non-dominated sorting genetic algorithm, aims to jointly optimize three objectives: STRIKE score, non-gaps percentage and totally conserved columns. It was significantly assessed on the BAliBASE benchmark according to the Kruskal-Wallis test (P < 0.01). This algorithm also outperforms other aligners, such as ClustalW, Multiple Sequence Alignment Genetic Algorithm (MSA-GA), PRRP, DIALIGN, Hidden Markov Model Training (HMMT), Pattern-Induced Multi-sequence Alignment (PIMA), MULTIALIGN, Sequence Alignment Genetic Algorithm (SAGA), PILEUP, Rubber Band Technique Genetic Algorithm (RBT-GA) and Vertical Decomposition Genetic Algorithm (VDGA), according to the Wilcoxon signed-rank test (P < 0.05), whereas it shows results not significantly different to 3D-COFFEE (P > 0.05) with the advantage of being able to use less structures. Structural information is included within the objective function to evaluate more accurately the obtained alignments.nnnAVAILABILITYnThe source code is available at http://www.ugr.es/~fortuno/MOSAStrE/MO-SAStrE.zip.

Using cited references to improve the retrieval of related biomedical documents

BackgroundA popular query from scientists reading a biomedical abstract is to search for topic-related documents in bibliographic databases. Such a query is challenging because the amount of information attached to a single abstract is little, whereas classification-based retrieval algorithms are optimally trained with large sets of relevant documents. As a solution to this problem, we propose a query expansion method that extends the information related to a manuscript using its cited references.ResultsData on cited references and text sections in 249,108 full-text biomedical articles was extracted from the Open Access subset of the PubMed Central® database (PMC-OA). Of the five standard sections of a scientific article, the Introduction and Discussion sections contained most of the citations (mean = 10.2 and 9.9 citations, respectively). A large proportion of articles (98.4%) and their cited references (79.5%) were indexed in the PubMed® database.Using the MedlineRanker abstract classification tool, cited references allowed accurate retrieval of the citing document in a test set of 10,000 documents and also of documents related to six biomedical topics defined by particular MeSH® terms from the entire PMC-OA (p-value<0.01).Classification performance was sensitive to the topic and also to the text sections from which the references were selected. Classifiers trained on the baseline (i.e., only text from the query document and not from the references) were outperformed in almost all the cases. Best performance was often obtained when using all cited references, though using the references from Introduction and Discussion sections led to similarly good results. This query expansion method performed significantly better than pseudo relevance feedback in 4 out of 6 topics.ConclusionsThe retrieval of documents related to a single document can be significantly improved by using the references cited by this document (p-value<0.01). Using references from Introduction and Discussion performs almost as well as using all references, which might be useful for methods that require reduced datasets due to computational limitations. Cited references from particular sections might not be appropriate for all topics. Our method could be a better alternative to pseudo relevance feedback though it is limited by full text availability.

Optimization of multiple sequence alignment methodologies using a multiobjective evolutionary algorithm based on NSGA-II

Multiple sequence alignment (MSA) is one of the most studied approach in Bioinformatics to carry out other outstanding tasks like structural predictions, biological function analysis or next-generation sequencing. However, MSA algorithms do not achieve consistent results in all cases, as alignments become difficult when sequences have low similarity. In other words, each algorithm is focused in specific features of sequences and their results depend on them. For this reason, each approach could align better those sections of sequences that include such features, obtaining partially optimal solutions. In this work, a multiobjective evolutionary algorithm based on NSGA-II will be implemented in order to assemble previously aligned sequences, trying to avoid suboptimal alignments.

Predicting the accuracy of multiple sequence alignment algorithms by using computational intelligent techniques

Multiple sequence alignments (MSAs) have become one of the most studied approaches in bioinformatics to perform other outstanding tasks such as structure prediction, biological function analysis or next-generation sequencing. However, current MSA algorithms do not always provide consistent solutions, since alignments become increasingly difficult when dealing with low similarity sequences. As widely known, these algorithms directly depend on specific features of the sequences, causing relevant influence on the alignment accuracy. Many MSA tools have been recently designed but it is not possible to know in advance which one is the most suitable for a particular set of sequences. In this work, we analyze some of the most used algorithms presented in the bibliography and their dependences on several features. A novel intelligent algorithm based on least square support vector machine is then developed to predict how accurate each alignment could be, depending on its analyzed features. This algorithm is performed with a dataset of 2180 MSAs. The proposed system first estimates the accuracy of possible alignments. The most promising methodologies are then selected in order to align each set of sequences. Since only one selected algorithm is run, the computational time is not excessively increased.

Prognosis Relevance of Serum Cytokines in Pancreatic Cancer.

The overall survival of patients with pancreatic ductal adenocarcinoma is extremely low. Although gemcitabine is the standard used chemotherapy for this disease, clinical outcomes do not reflect significant improvements, not even when combined with adjuvant treatments. There is an urgent need for prognosis markers to be found. The aim of this study was to analyze the potential value of serum cytokines to find a profile that can predict the clinical outcome in patients with pancreatic cancer and to establish a practical prognosis index that significantly predicts patients outcomes. We have conducted an extensive analysis of serum prognosis biomarkers using an antibody array comprising 507 human cytokines. Overall survival was estimated using the Kaplan-Meier method. Univariate and multivariate Coxs proportional hazard models were used to analyze prognosis factors. To determine the extent that survival could be predicted based on this index, we used the leave-one-out cross-validation model. The multivariate model showed a better performance and it could represent a novel panel of serum cytokines that correlates to poor prognosis in pancreatic cancer. B7-1/CD80, EG-VEGF/PK1, IL-29, NRG1-beta1/HRG1-beta1, and PD-ECGF expressions portend a poor prognosis for patients with pancreatic cancer and these cytokines could represent novel therapeutic targets for this disease.

Comparing different machine learning and mathematical regression models to evaluate multiple sequence alignments

The evaluation of multiple sequence alignments (MSAs) is still an open task in bioinformatics. Current MSA scores do not agree about how alignments must be accurately evaluated. Consequently, it is not trivial to know the quality of MSAs when reference alignments are not provided. Recent scores tend to use more complex evaluations adding supplementary biological features. In this work, a set of novel regression approaches are proposed for the MSA evaluation, comparing several supervised learning and mathematical methodologies. Therefore, the following models specifically designed for regression are applied: regression trees, a bootstrap aggregation of regression trees (bagging trees), least-squares support vector machines (LS-SVMs) and Gaussian processes. These algorithms consider a heterogeneous set of biological features together with other standard MSA scores in order to predict the quality of alignments. The most relevant features are then applied to build novel score schemes for the evaluation of alignments. The proposed algorithms are validated by using the BAliBASE benchmark. Additionally, an statistical ANOVA test is performed to study the relevance of these scores considering three alignment factors. According to the obtained results, the four regression models provide accurate evaluations, even outperforming other standard scores such as BLOSUM, PAM or STRIKE.

Integrative multi-platform meta-analysis of gene expression profiles in pancreatic ductal adenocarcinoma patients for identifying novel diagnostic biomarkers

Applying differentially expressed genes (DEGs) to identify feasible biomarkers in diseases can be a hard task when working with heterogeneous datasets. Expression data are strongly influenced by technology, sample preparation processes, and/or labeling methods. The proliferation of different microarray platforms for measuring gene expression increases the need to develop models able to compare their results, especially when different technologies can lead to signal values that vary greatly. Integrative meta-analysis can significantly improve the reliability and robustness of DEG detection. The objective of this work was to develop an integrative approach for identifying potential cancer biomarkers by integrating gene expression data from two different platforms. Pancreatic ductal adenocarcinoma (PDAC), where there is an urgent need to find new biomarkers due its late diagnosis, is an ideal candidate for testing this technology. Expression data from two different datasets, namely Affymetrix and Illumina (18 and 36 PDAC patients, respectively), as well as from 18 healthy controls, was used for this study. A meta-analysis based on an empirical Bayesian methodology (ComBat) was then proposed to integrate these datasets. DEGs were finally identified from the integrated data by using the statistical programming language R. After our integrative meta-analysis, 5 genes were commonly identified within the individual analyses of the independent datasets. Also, 28 novel genes that were not reported by the individual analyses (‘gained’ genes) were also discovered. Several of these gained genes have been already related to other gastroenterological tumors. The proposed integrative meta-analysis has revealed novel DEGs that may play an important role in PDAC and could be potential biomarkers for diagnosing the disease.

Identification of gene expression profiling associated with erlotinib-related skin toxicity in pancreatic adenocarcinoma patients

Erlotinib is an epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor that showed activity against pancreatic ductal adenocarcinoma (PDAC). The drugs most frequently reported side effect as a result of EGFR inhibition is skin rash (SR), a symptom which has been associated with a better therapeutic response to the drug. Gene expression profiling can be used as a tool to predict which patients will develop this important cutaneous manifestation. The aim of the present study was to identify which genes may influence the appearance of SR in PDAC patients. The study included 34 PDAC patients treated with erlotinib: 21 patients developed any grade of SR, while 13 patients did not (controls). Before administering any chemotherapy regimen and the development of SR, we collected RNA from peripheral blood samples of all patients and studied the differential gene expression pattern using the Illumina microarray platform HumanHT-12 v4 Expression BeadChip. Seven genes (FAM46C, IFITM3, GMPR, DENND6B, SELENBP1, NOL10, and SIAH2), involved in different pathways including regulatory, migratory, and signalling processes, were downregulated in PDAC patients with SR. Our results suggest the existence of a gene expression profiling significantly correlated with erlotinib-induced SR in PDAC that could be used as prognostic indicator in this patients.

Advances in bioinformatics and biomedical engineering - special issue of IWBBIO 2013

In the present issue of Theoretical Biology and Medical Modelling (TBioMed), it is a pleasure to present you a selection of 8 extended versions of selected papers from the International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2013) held in Granada (Spain) during March 18-20, 2013. IWBBIO 2013 seeks to provide a discussion forum for scientists, engineers, educators and students about the latest ideas and realizations in the foundations, theory, models and applications for interdisciplinary and multidisciplinary research encompassing disciplines of computer science, mathematics, statistics, biology, bioinformatics, and biomedicine. The aims of IWBBIO 2013 is to create a friendly environment that could lead to the establishment or strengthening of scientific collaborations and exchanges among attendees, and therefore, IWBBIO 2013 solicited high-quality original research papers (including significant work-in-progress) on any aspect of Bioinformatics, Biomedicine and Biomedical Engineering. New computational techniques and methods in machine learning, data mining, text analysis, pattern recognition, data integration, genomics and evolution, next generation sequencing data, protein and RNA structure, protein function and proteomics, medical informatics and translational bioinformatics, computational systems biology, modelling and simulation and their application in the life science domain, biomedicine and biomedical engineering were especially encouraged. At the end of the submission process of IWBBIO 2013, and after a careful peer review and evaluation process (each submission was reviewed by at least 2 program committee members or additional reviewer), 122 papers were accepted for oral or poster presentation, according to the recommendations of reviewers and the authors’ preferences. A number of authors were invited to submit an extended version of their conference paper to be considered for special publication in this issue of Theoretical Biology and Medical Modelling (TBioMed). These authors were selected after the recommendation of the reviewers of the conference papers, the opinion of the chairs of the different sessions and the guest editors. The extended versions were again carefully reviewed by at least two independent and anonymous experts and the accepted papers, after this new review process, are presented in this issue.

Evaluating multiple sequence alignments using a LS-SVM approach with a heterogeneous set of biological features

Multiple sequence alignment (MSA) is an essential approach to apply in other outstanding bioinformatics tasks such as structural predictions, biological function analyses or phylogenetic modeling. However, current MSA methodologies do not reach a consensus about how sequences must be accurately aligned. Moreover, these tools usually provide partially optimal alignments, as each one is focused on specific features. Thus, the same set of sequences can provide quite different alignments, overall when sequences are less related. Consequently, researchers and biologists do not agree on how the quality of MSAs should be evaluated in order to decide the most adequate methodology. Therefore, recent evaluations tend to use more complex scores including supplementary biological features. In this work, we address the evaluation of MSAs by using a novel supervised learning approach based on Least Square Support Vector Machine (LS-SVM). This algorithm will include a set of heterogeneous features and scores in order to determine the alignment accuracies. It is assessed by means of the benchmark BAliBASE.