MOTIVATION: Targeted enrichment sequencing by next-generation sequencing is a common approach to interrogate specific loci or the whole exome in the human genome. The efficiency and the lack of bias in the enrichment process need to be assessed as a quality control step before performing downstream analysis of the sequence data. Tools that can report on the sensitivity, specificity, uniformity and other enrichment-specific features are needed. RESULTS: We have implemented the next-generation sequencing data Capture Assessment Tool (ngsCAT), a tool that takes the information of the mapped reads and the coordinates of the targeted regions as input files, and generates a report with metrics and figures that allows the evaluation of the efficiency of the enrichment process. The tool can also take as input the information of two samples allowing the comparison of two different experiments. AVAILABILITY AND IMPLEMENTATION: Documentation and downloads for ngsCAT can be found at http://www.bioinfomgp.org/ngscat.

MOTIVATION: Multiple 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. RESULTS: The 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. AVAILABILITY: The source code is available at http://www.ugr.es/~fortuno/MOSAStrE/MO-SAStrE.zip.

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.

In function approximation problems, one of the most common ways to evaluate a learning algorithm consists in partitioning the original data set (input/output data) into two sets: learning, used for building models, and test, applied for genuine out-of-sample evaluation. When the partition into learning and test sets does not take into account the variability and geometry of the original data, it might lead to non-balanced and unrepresentative learning and test sets and, thus, to wrong conclusions in the accuracy of the learning algorithm. How the partitioning is made is therefore a key issue and becomes more important when the data set is small due to the need of reducing the pessimistic effects caused by the removal of instances from the original data set. Thus, in this work, we propose a deterministic data mining approach for a distribution of a data set (input/output data) into two representative and balanced sets of roughly equal size taking the variability of the data set into consideration with the purpose of allowing both a fair evaluation of learning's accuracy and to make reproducible machine learning experiments usually based on random distributions. The sets are generated using a combination of a clustering procedure, especially suited for function approximation problems, and a distribution algorithm which distributes the data set into two sets within each cluster based on a nearest-neighbor approach. In the experiments section, the performance of the proposed methodology is reported in a variety of situations through an ANOVA-based statistical study of the results.

Affymetrix High Oligonucleotide expression arrays are widely used for the high-throughput assessment of gene expression of thousands of genes simultaneously. Although disputed by several authors, there are non-biological variations and systematic biases that must be removed as much as possible through the pre-processing step before an absolute expression level for every gene is assessed. It is important to evaluate microarray pre-processing procedures not only to the detection of differentially expressed genes, but also to classification, since a major use of microarrays is the expression-based phenotype classification. Thus, in this paper, we use several cancer microarray datasets to assess the influence of five different pre-processing methods in Support Vector Machine-based classification methodologies with different kernels: linear, Radial Basis Functions (RBFs) and polynomial.