Laboratory of Medical Microbiology

Laboratory of Medical Microbiology

Head of the Laboratory: Pavel Borovikov, Candidate of Mathematical Sciences

E-mail: p_borovikov@oparina4.ru

 

FOCUS AREA

The staff of the laboratory includes biologists, bioinformaticians, biostatisticians, real-time system developers, genomics specialists.

NGS (next-generation sequencing) data analysis

Pipeline for analysis of whole human genomes and exomes consists of the following parts:

• Quality control of the reads
• Alignment of reads to hg38 reference genome
• Analysis of differences from the reference genome
• Interpretation of differences using frequency and pathogenicity databases
• Filtering results data

 

Bionformatics

Biological data analysis (NGS, microarray, MS, PCR), biostatistics.

• Analysis of microRNAs role in the pathogenesis of endometriosis and its diagnosis
• The use of NMF and tSNE in cell typing based on gene expression
• Assessment and visualization of differential gene expression
• Software development for HLA and KIR class gene typing
• The use of NMF method to distinguish whole-genome expression in tissues into individual cell types
• Atlas of gene expression in the healthy endometrium

 

Software development

Development of scalable software systems, databases.

• System for support and automation of non-invasive DNA screening
• Databases for medical and scientific research
• Plugins for automation of sequence data processing for the ION Torrent Suite

 

Mathematical models

Models of complex biological systems and intracellular processes, analysis of biological and medical data.

• Modeling of degradation process of biopolymer scaffolds
• Development of a dynamic model of the microbiota structure using graph theory methods
• Mathematical model of biomaterial resorption for targeted medication delivery
• Model of the hydrolysis of aliphatic polyesters in a biological environment with limited water diffusion

 

Machine learning

Development, training, and implementation of neural network technology, methods of deep learning, and computer vision.

• Neural network for the classification of pathologies according to mass spectrometric analysis
• Search for visual patterns, computer vision, and object tracking in a video based on the method for finding the direction of newborn gaze and their visual activity analysis
• Search for hidden movements based on the analysis of the video with sucking activity of preterm newborns

 

ACHIEVEMENTS

The laboratory creation was supported by a grant from the Ministry of Science and Higher Education of the Russian Federation.

The Laboratory has the Agreement with the Ministry of Science and Higher Education of the Russian Federation to work on the project “Automated decision support system in obstetric pathology”.