Alisa Lyskova

I am a bioinformatics scientist focused on genomic data analysis, variant calling, and machine learning applications for biological data. I combine strong computational skills with domain knowledge gained through laboratory and applied bioinformatics work.

My experience spans NGS/TGS data processing, alignment and variant-calling toolchains, annotation, repeat analysis, phasing, and immune profiling. I regularly use NextFlow, Snakemake, Docker and HPC environments with SLURM to produce reproducible pipelines.

I have practical expertise with Python (Biopython), R, Bash and machine learning frameworks including TensorFlow and scikit-learn, applying these to GWAS analysis, classification, regression and clustering tasks. I am comfortable managing biological databases (NCBI, ENA, UCSC, GEO) and visualizing results with IGV.

I collaborate across research labs and clinical research settings, bringing rigor in pipeline development, tool selection and large-scale genomic analyses to support translational and research projects.