Aljes Binkevich

Bioinformatician

Bioinformatician, Cellular Genetics Informatics

I’m a Bioinformatician with a strong foundation in transcriptomics (bulk RNA-Seq and Visium)

Education:
I earned my bachelor’s degree in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology. My studies gave me a strong foundation in mathematical modeling, computational techniques, and the physical principles that I now apply to solve biological problems.

Research Interests:
I’m passionate about developing tools and workflows for the analysis of biological data, making it easier for researchers to extract meaningful insights from complex datasets.

Skills and Tools:

I rely on a diverse toolkit to support my research:

  • Nextflow: For robust and reproducible pipeline development.
  • Python: For data analysis, machine learning, and visualization, including libraries like Pandas, PyTorch, and Matplotlib.
  • Containerization (Docker, Singularity): To ensure reproducibility across environments.
  • ML and DL: Experience in classical machine learning and natural language processing

Through my work, I aim to make biological data analysis more efficient and reliable, contributing to research that has a lasting impact.

Research Experience:

  • Sanger Institute: I’m currently working at the Wellcome Sanger Institute, where I focus on developing pipelines for the analysis of Single Cell data
  • Skoltech: While at Skoltech in the Philipp Khaitovich group, I studied the prefrontal cortex of humans, chimpanzees, and macaques using 10x Genomics Visium data. My work involved identifying genes with human-specific expression patterns
  • EMBL-EBI: At EMBL-EBI, I developed and validated the CellTypeScanner tool, combining Nextflow and R to streamline cell type classification workflows.
  • BostonGene: I worked on identifying subtypes of cervical cancer using RNA-seq data and linked them to patient survival and immunotherapy outcomes. I also developed a method to apply qPCR-like scoring to RNA-seq data and built machine-learning models to predict neuroendocrine prostate cancer.

My timeline