Nataliya Timoshevskaya
Scientist II at University of Kentucky
Lexington, Kentucky, United States
Summary
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Senior🎓
Top SchoolNataliya Timoshevskaya is a computational biologist and Scientist II at the Spinal Cord and Brain Injury Research Center, University of Kentucky, with a decade of experience turning complex genomic, epigenetic, and transcriptomic data into well-documented analysis pipelines. Trained as a mathematician and computer scientist (PhD), she pairs deep algorithmic knowledge with practical high-performance computing skills developed through work on suffix-array optimization, MapReduce workflows, and cloud/cluster deployments. Her career spans postdoctoral research and software-focused roles where she led end-to-end analyses from experimental design through interpretation, annotation, and structural variant discovery. Nataliya’s uncommon combination of discrete math background and hands-on bioinformatics lets her design robust, reproducible pipelines for large and repetitive genomes, making her a strong fit for academic labs or companies needing scalable genomic data solutions.
10 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy (Ph.D.), Mathematics and Computer Science, Doctor of Philosophy (Ph.D.), Mathematics and Computer Science at Tomsk State University
English, Russian
Github Skills (16)
bam8
bioinformatics7
systems-biology6
bioconda5
sbml5
genome4
genomics3
sam3
ruby2
python2
lua2
genome-analysis2
rna-seq1
molecule1
system-analysis1
Programming languages (4)
Github contributions (5)
timnat/DifCover
Sep 2017 - Jan 2023
The DifCover pipeline aims to identify regions in a reference genome for which the read coverage of a sample1 to the reference is significantly different from the read coverage of a sample2. “Significantly different” is determined by user defined threshold on a ration between average coverages of given samples. The pipeline allows to exclude from a consideration the under-represented fragments (with low coverage in sequencing of both samples) and/or the regions that carry repetitive sequences. Both cases can be misleading in the coverage analysis. The DifCover pipeline is specifically oriented to the analysis of large genomes and can handle very fragmented assemblies.
Contributions:6 releases, 100 commits, 100 pushes in 5 years 5 months
timnat/SparseGenotyping
Oct 2018 - Jan 2019
SparseGenotyping - is a program that allows to define a consensus genotype for scaffolds based on low-coverage SNP calls by accumulating number of reads that support one or another genotype across the length a scaffold.
Contributions:2 PRs, 14 pushes, 3 branches in 2 months
scaffoldconsensuscallsgenotypegenomics
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