T-REX (Tree and reticulogram REConstruction) is a web server dedicated to the reconstruction of phylogenetic trees, reticulation networks and to the inference of horizontal gene transfer (HGT) events. T-REX includes several popular bioinformatics applications such as MUSCLE, MAFFT, Neighbor Joining, NINJA, BioNJ, PhyML, RAxML, random phylogenetic tree generator and some well-known sequence-to-distance transformation models. It also comprises fast and effective methods for inferring phylogenetic trees from complete and incomplete distance matrices as well as for reconstructing reticulograms and HGT networks, including the detection and validation of complete and partial gene transfers, inference of consensus HGT scenarios and interactive HGT identification, developed by the authors. The included methods allows for validating and visualizing phylogenetic trees and networks which can be built from distance or sequence data. The web server is available at: www.trex.uqam.ca.

Accurate detection of sequence similarity and homologous recombination are essential parts of many evolutionary analyses. We have developed SimPlot++, an open-source multiplatform application implemented in Python, which can be used to produce publication quality sequence similarity plots using 63 nucleotide and 20 amino acid distance models, to detect intergenic and intragenic recombination events using Φ, Max-χ², NSS or proportion tests, and to generate and analyze interactive sequence similarity networks. SimPlot++ supports multicore data processing and provides useful distance calculability diagnostics. SimPlot++ is freely available on GitHub at: https://github.com/Stephane-S/Simplot_PlusPlus, as both an executable file (for Windows) and Python scripts (for Windows/Linux/MacOS).

CRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated protein 9) is a popular and effective two-component technology used for targeted genetic manipulation. It is currently the most versatile and accurate method of gene and genome editing, which benefits from a large variety of practical applications. For example, in biomedicine, it has been used in research related to cancer, virus infections, pathogen detection, and genetic diseases. Current CRISPR/Cas9 research is based on data-driven models for on- and off-target prediction as a cleavage may occur at non-target sequence locations. Nowadays, conventional machine learning and deep learning methods are applied on a regular basis to accurately predict on-target knockout efficacy and off-target profile of given single-guide RNAs (sgRNAs). In this paper, we present an overview and a comparative analysis of traditional machine learning and deep learning models used in CRISPR/Cas9. We highlight the key research challenges and directions associated with target activity prediction. We discuss recent advances in the sgRNA–DNA sequence encoding used in state-of-the-art on- and off-target prediction models. Furthermore, we present the most popular deep learning neural network architectures used in CRISPR/Cas9 prediction models. Finally, we summarize the existing challenges and discuss possible future investigations in the field of on- and off-target prediction. Our paper provides valuable support for academic and industrial researchers interested in the application of machine learning methods in the field of CRISPR/Cas9 genome editing.

Multimodal emotion recognition is an emerging interdisciplinary field of research in the area of affective computing and sentiment analysis. It aims at exploiting the information carried by signals of different nature to make emotion recognition systems more accurate. This is achieved by employing a powerful multimodal fusion method. In this study, a hybrid multimodal data fusion method is proposed in which the audio and visual modalities are fused using a latent space linear map and then, their projected features into the cross-modal space are fused with the textual modality using a Dempster-Shafer (DS) theory-based evidential fusion method. The evaluation of the proposed method on the videos of the DEAP dataset shows its superiority over both decision-level and non-latent space fusion methods. Furthermore, the results reveal that employing Marginal Fisher Analysis (MFA) for feature-level audio-visual fusion results in higher improvement in comparison to cross-modal factor analysis (CFA) and canonical correlation analysis (CCA). Also, the implementation results show that exploiting textual users’ comments with the audiovisual content of movies improves the performance of the system.