Exploratory data analysis of a clinical study group: Development of a procedure for exploring multidimensional data.

Thorough knowledge of the structure of analyzed data allows to form detailed scientific hypotheses and research questions. The structure of data can be revealed with methods for exploratory data analysis. Due to multitude of available methods, selecting those which will work together well and facilitate data interpretation is not an easy task.

Distinguishing mirtrons from canonical miRNAs with data exploration and machine learning methods.

Mirtrons are non-canonical microRNAs encoded in introns the biogenesis of which starts with splicing. They are not processed by Drosha and enter the canonical pathway at the Exportin-5 level. Mirtrons are much less evolutionary conserved than canonical miRNAs.

Quantiprot - a Python package for quantitative analysis of protein sequences.

Background: The field of protein sequence analysis is dominated by tools rooted in substitution matrices and alignments. A complementary approach is provided by methods of quantitative characterization. A major advantage of the approach is that quantitative properties defines a multidimensional solution space, where sequences can be related to each other and differences can be meaningfully interpreted.

Applying PyRosetta molecular energies to separate properly oriented protein models from mirror models, obtained from contact maps.

Reconstructing protein structure based on contact maps leads to two types of models: properly oriented models and mirror models. This is due to the fact that contact maps do not include information on protein chirality. Therefore, both types of model orientations share the same contact map and are geometrically allowed.

Fast assessment of structural models of ion channels based on their predicted current-voltage characteristics.

Computational prediction of protein structures is a difficult task, which involves fast and accurate evaluation of candidate model structures. We propose to enhance single-model quality assessment with a functionality evaluation phase for proteins whose quantitative functional characteristics are known. In particular, this idea can be applied to evaluation of structural models of ion channels, whose main function - conducting ions - can be quantitatively measured with the patch-clamp technique providing the current-voltage characteristics.

Evaluating the significance of protein functional similarity based on gene ontology.

Gene ontology is among the most successful ontologies in the biomedical domain. It is used to describe, unambiguously, protein molecular functions, cellular localizations, and processes in which proteins participate. The hierarchical structure of gene ontology allows quantifying protein functional similarity by application of algorithms that calculate semantic similarities.

Automated procedure for contact-map-based protein structure reconstruction.

Knowledge of the three-dimensional structures of ion channels allows for modeling their conductivity characteristics using biophysical models and can lead to discovering their cellular functionality. Recent studies show that quality of structure predictions can be significantly improved using protein contact site information. Therefore, a number of procedures for protein structure prediction based on their contact-map have been proposed.

Quality assessment of protein model-structures based on structural and functional similarities.

Background: Experimental determination of protein 3D structures is expensive, time consuming and sometimes impossible. A gap between number of protein structures deposited in the World Wide Protein Data Bank and the number of sequenced proteins constantly broadens. Computational modeling is deemed to be one of the ways to deal with the problem.