Showing papers by "Matej Lexa published in 2015"

Transposable elements and G-quadruplexes

Abstract: A significant part of eukaryotic genomes is formed by transposable elements (TEs) containing not only genes but also regulatory sequences. Some of the regulatory sequences located within TEs can form secondary structures like hairpins or three-stranded (triplex DNA) and four-stranded (quadruplex DNA) conformations. This review focuses on recent evidence showing that G-quadruplex-forming sequences in particular are often present in specific parts of TEs in plants and humans. We discuss the potential role of these structures in the TE life cycle as well as the impact of G-quadruplexes on replication, transcription, translation, chromatin status, and recombination. The aim of this review is to emphasize that TEs may serve as vehicles for the genomic spread of G-quadruplexes. These non-canonical DNA structures and their conformational switches may constitute another regulatory system that, together with small and long non-coding RNA molecules and proteins, contribute to the complex cellular network resulting in the large diversity of eukaryotes.

Hammock: a hidden Markov model-based peptide clustering algorithm to identify protein-interaction consensus motifs in large datasets

Abstract: Motivation: Proteins often recognize their interaction partners on the basis of short linear motifs located in disordered regions on proteins' surface. Experimental techniques that study such motifs use short peptides to mimic the structural properties of interacting proteins. Continued development of these methods allows for large-scale screening, resulting in vast amounts of peptide sequences, potentially containing information on multiple protein-protein interactions. Processing of such datasets is a complex but essential task for large-scale studies investigating protein-protein interactions. Results: The software tool presented in this article is able to rapidly identify multiple clusters of sequences carrying shared specificity motifs in massive datasets from various sources and generate multiple sequence alignments of identified clusters. The method was applied on a previously published smaller dataset containing distinct classes of ligands for SH3 domains, as well as on a new, an order of magnitude larger dataset containing epitopes for several monoclonal antibodies. The software successfully identified clusters of sequences mimicking epitopes of antibody targets, as well as secondary clusters revealing that the antibodies accept some deviations from original epitope sequences. Another test indicates that processing of even much larger datasets is computationally feasible.

A flexible denormalization technique for data analysis above a deeply-structured relational database: biomedical applications

Abstract: Relational databases are sometimes used to store biomedical and patient data in large clinical or international projects. This data is inherently deeply structured, records for individual patients contain varying number of variables. When ad-hoc access to data subsets is needed, standard database access tools do not allow for rapid command prototyping and variable selection to create flat data tables. In the context of Thalamoss, an international research project on β-thalassemia, we developed and experimented with an interactive variable selection method addressing these needs. Our newly-developed Python library sqlAutoDenorm.py automatically generates SQL commands to denormalize a subset of database tables and their relevant records, effectively generating a flat table from arbitrarily structured data. The denormalization process can be controlled by a small number of user-tunable parameters. Python and R/Bioconductor are used for any subsequent data processing steps, including visualization, and Weka is used for machine-learning above the generated data.