NGSEP 4: Efficient and accurate identification of orthogroups and whole-genome alignment.

Whole-genome alignment allows researchers to understand the genomic structure and variation among genomes. Approaches based on direct pairwise comparisons of DNA sequences require large computational capacities. As a consequence, pipelines combining tools for orthologous gene identification and synteny have been developed.

Machine learning models for accurate prioritization of variants of uncertain significance.

The growing use of next-generation sequencing technologies on genetic diagnosis has produced an exponential increase in the number of variants of uncertain significance (VUS). In this manuscript, we compare three machine learning methods to classify VUS as Pathogenic or No pathogenic, implementing a Random Forest (RF), a Support Vector Machine (SVM), and a Multilayer Perceptron. To train the models, we extracted high-quality variants from ClinVar that were previously classified as VUS.