MOTIVATION: Sequence analyses oriented to investigate specific features, patterns and functions of protein and DNA/RNA sequences usually require tools based on graphic interfaces whose main characteristic is their intuitiveness and interactivity with the user's expertise, especially when curation or primer design tasks are required. However, interface-based tools usually pose certain computational limitations when managing large sequences or complex datasets, such as genome and transcriptome assemblies. Having these requirments in mind we have developed SeqEditor an interactive software tool for nucleotide and protein sequences' analysis. RESULT: SeqEditor is a cross-platform desktop application for the analysis of nucleotide and protein sequences. It is managed through a Graphical User Interface and can work either as a graphical sequence browser or as a fasta task manager for multi-fasta files. SeqEditor has been optimized for the management of large sequences, such as contigs, scaffolds or even chromosomes, and includes a GTF/GFF viewer to visualize and manage annotation files. In turn, this allows for content mining from reference genomes and transcriptomes with similar efficiency to that of command line tools. SeqEditor also incorporates a set of tools for singleplex and multiplex PCR primer design and pooling that uses a newly optimized and validated search strategy for target and species-specific primers. All these features make SeqEditor a flexible application that can be used to analyses complex sequences, design primers in PCR assays oriented for diagnosis, and/or manage, edit and personalize reference sequence datasets. AVAILABILITYAND IMPLEMENTATION: SeqEditor was developed in Java using Eclipse Rich Client Platform and is publicly available at https://gpro.biotechvana.com/download/SeqEditor as binaries for Windows, Linux and Mac OS. The user manual and tutorials are available online at https://gpro.biotechvana.com/tool/seqeditor/manual. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Numerous sequencing studies, including transcriptomics of host-pathogen systems, sequencing of hybrid genomes, xenografts, mixed species systems, metagenomics and meta-transcriptomics, involve samples containing genetic material from divergent organisms. A crucial step in these studies is identifying from which organism each sequencing read originated, and the experimental design should be directed to minimize biases caused by cross-mapping of reads to incorrect source genomes. Additionally, pooling of sufficiently different genetic material into a single sequencing library could significantly reduce experimental costs but requires careful planning and assessment of the impact of cross-mapping. Having these applications in mind we designed Crossmapper, the first to our knowledge tool able to assess cross-mapping prior to sequencing, therefore allowing optimization of experimental design. RESULTS: Using any combination of reference genomes, Crossmapper performs read simulation and back-mapping of those reads to the pool of references, quantifies and reports the cross-mapping rates for each organism. Crossmapper performs these analyses with numerous user-specified parameters, including, among others, read length, read layout, coverage, mapping parameters, genomic or transcriptomic data. Additionally, it outputs the results in highly interactive and publication-ready reports. This allows the user to perform multiple comparisons at once and choose the experimental setup minimizing cross-mapping rates. Moreover, Crossmapper can be used for resource optimization in sequencing facilities by pooling different samples into one sequencing library. AVAILABILITY AND IMPLEMENTATION: Crossmapper is a command line tool implemented in Python 3.6 and available as a conda package, allowing effortless installation. The source code, detailed information and a step-by-step tutorial is available at our GitHub page https://github.com/Gabaldonlab/crossmapper. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Nowadays, a dataset includes a huge number of features with irrelevant and redundant ones. Feature selection is required for a better machine-learning algorithms' performance. A system for feature selection is proposed in this work using a sine cosine algorithm (SCA). SCA is a new stochastic search algorithm for optimization problems. SCA optimization adaptively balances the exploration and exploitation to find the optimal solution quickly. The SCA can quickly explore the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporates both classification accuracy and feature size reduction. The proposed system was tested on 18 datasets and shows an advance over other search methods as particle swarm optimization (PSO) and genetic algorithm (GA) optimizers commonly used in this context using different evaluation indicators.

Community detection in complex networks has attracted a lot of attention in recent years. Communities play special roles in the structure-function relationship; therefore, detecting communities can be a way to identify substructures that could correspond to important functions. Social networks can be formalized by a statistical model in which interactions between actors are generated based on some assumptions. We adopt the idea and introduce a statistical model of the interactions between social network's actors, and we use Bayesian network (probabilistic graphical model) to show the relation between model variables. Through the use Expectation Maximization (EM) algorithm, we drive estimates for the model parameters and propose a community detection algorithm based on the EM estimates. The proposed algorithm works well with directed and undirected networks, and with weighted and un-weighted networks. The algorithm yields very promising results when applied to the community detection problem.

Community detection in complex networks has attracted a lot of attention in recent years. Community detection can be viewed as an optimization problem, in which an objective function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. Many single-objective optimization techniques have been used to solve the problem however those approaches have its drawbacks since they try optimizing one objective function and this results to a solution with a particular community structure property. More recently researchers viewed the problem as a multi-objective optimization problem and many approaches have been proposed to solve it. However which objective functions could be used with each other is still under debated since many objective functions have been proposed over the past years and in somehow most of them are similar in definition. In this paper we use Genetic Algorithm (GA) as an effective optimization technique to solve the community detection problem as a single-objective and multi-objective problem, we use the most popular objectives proposed over the past years, and we show how those objective correlate with each other, and their performances when they are used in the single-objective Genetic Algorithm and the Multi-Objective Genetic Algorithm and the community structure properties they tend to produce.