Mikhail Karasikov

Mikhail Karasikov

PhD Candidate

ETH Zurich

About me

I focus on designing algorithms and compressed data structures for indexing very large collections of sequences and develop methods scalable to the entire sequence read archive. These methods build graph representations that enable analysis and queries, which would otherwise be practically impossible using only the raw data.

Prior to my Ph.D. at ETH Zurich, I studied Math, Physics, and Optimal Control at the Moscow Institute of Physics and Technology (MIPT). Then, I did a double Master’s program studying Mathematics and Machine Learning at MIPT and Skoltech. At the same time, I completed a two-year CS program at the Yandex School of Data Analysis and then interned at Inria Grenoble-Rhône-Alpes working on various problems of computational structural biology.

Interests
  • Machine Learning
  • Bioinformatics
  • Computational Biology
  • Compressed Data Structures
Free time
Education

  • Ph.D. in Computer Science, present

    ETH Zurich, Zurich, Switzerland

  • M.Sc. in Math. and Computer Science, 2017

    Skolkovo Institute of Science and Technology (Skoltech), Moscow, Russia

  • M.Sc. in Applied Math. and Physics, 2017

    Moscow Institute of Physics and Technology (MIPT), Moscow, Russia

  • PG Dip. in Computer Science, 2016

    Yandex School of Data Analysis, Moscow, Russia

  • B.Sc. in Applied Math. and Physics, 2015

    Moscow Institute of Physics and Technology (MIPT), Moscow, Russia

Recent & Upcoming Talks

Searching in nucleotide archives at Petabase scale with MetaGraph
Zurich Seminars in Bioinformatics 2022 (UZH Irchel Y55-l-06/08, Zurich, Switzerland).
Nov 24, 2022 12:15 PM
Project Slides
Searching in nucleotide archives at Petabase scale with MetaGraph
Biological Data Science - CSHL Meeting 2022 (Cold Spring Harbor, New York, USA).
Nov 9, 2022 — Nov 12, 2022
Project Poster
Scalable Indexing of Sequence Data in Annotated Genome Graphs
Talk at JOBIM 2022 (Rennes, France).
Jul 7, 2022 3:00 PM
Project Slides
Scalable Indexing of Sequence Data in Annotated Genome Graphs
Multi-genome Representations with MetaGraph
Talk at IGGSy 2022 (Ascona, Switzerland).
Jul 4, 2022 11:15 AM
Project Slides
Multi-genome Representations with MetaGraph
Lossless Indexing with Counting de Bruijn Graphs
Talk at RECOMB 2022 (La Jolla, California, USA).
May 24, 2022 2:30 PM
Project Slides Paper
Lossless Indexing with Counting de Bruijn Graphs
See all events

Projects

Ocean Microbiomics Database
Collaboration with Sunagawa Lab. Provided k-mer based sequence search and Counting de Bruijn graph indexes for the Genome Collection of the Ocean Microbiomics Database. Other contributors: Lucas Paoli, Harun Mustafa, Andre Kahles. (Published in Nature).
Project Paper
Ocean Microbiomics Database
MetaGraph
A C++ framework library for indexing very large collections of DNA/Protein sequences and a tool for sequence search, alignment, and assembly. Although the target use cases of MetaGraph overlap with BLAST, MetaGraph mainly focuses on the scalable indexing of raw sequencing data in annotated de Bruijn graphs with up to $\sim 10^{12}$ nodes and $\sim 10^{7}$ annotation labels. It also provides an online platform MetaGraph Online. Other contributors: Marc Zimmermann, Thomas Zhou, the MetaGraph team.
Preprint Github
MetaGraph
Compressed Hybrid Bit Vector Representations
A C++ library with hybrid schemes for representing bit vectors in compressed space.
Benchmarks 1e7 Benchmarks 1e9 Github
Compressed Hybrid Bit Vector Representations
GeoDNA
A portal for sequence search and geographical positioning based on the metagenomic MetaSUB data. The initial prototype was set up on a weekend but it served well and was also used as a base for the MetaGraph Search platform. Other contributors: Marc Zimmermann, Jiayu Chen, André Kahles, Thomas Zhou. (Published in Cell).
Paper Github News
GeoDNA
De Bruijn Graph Visualizer
A web app visualizing de Bruijn graphs and the BOSS table (Bowe et al.). Developed to interactively illustrate the core data structure used as a k-mer index for graph representation in MetaGraph.
Github
De Bruijn Graph Visualizer
Protein Scoring
A method for single-model coarse-grained protein quality assessment developed during my internship at Inria Grenoble-Rhône-Alpes (with Guillaume Pagès and Sergei Grudinin).
Slides Paper Gitlab
Protein Scoring
Sentiment Analysis
A small demo for sentiment analysis of reviews on Russian banks (one of those quick hands-on projects I did when studied at YSDA).
After a night of “bombarding” banki.ru with random web scraping requests, our dorm network got banned by IP. Fortunately, the 5k successfully scraped reviews were enough to complete the project. However, my roommates and I had been reminded about this every time we had to use a VPN to check reviews on banks since then.
Activity Prediction
Classification of time-series data from smartphone accelerometer sensor. Implemented as a practical demonstration of the methods developed in my B.Sc. thesis.
Paper Github
Activity Prediction

Featured Publications

Mikhail Karasikov, Harun Mustafa, Gunnar Rätsch, André Kahles (2021). Lossless Indexing with Counting de Bruijn Graphs. In RECOMB 2022.

Cite Project DOI Preprint Code

Daniel Danciu, Mikhail Karasikov, Harun Mustafa, André Kahles, Gunnar Rätsch (2021). Topology-based Sparsification of Graph Annotations. In ISMB/ECCB 2021.

PDF Cite Project Slides DOI Code

Mikhail Karasikov, Harun Mustafa, Daniel Danciu, Marc Zimmermann, Christopher Barber, Gunnar Rätsch, André Kahles (2020). MetaGraph: Indexing and Analysing Nucleotide Archives at Petabase-scale. In bioRxiv.

Cite Project DOI Preprint Code

Mikhail Karasikov, Guillaume Pagès, Sergei Grudinin (2018). Smooth Orientation-Dependent Scoring Function for Coarse-Grained Protein Quality Assessment. In Bioinformatics.

PDF Cite Project DOI Code

Mikhail Karasikov, Harun Mustafa, Amir Joudaki, Sara Javadzadeh No, Gunnar Rätsch, André Kahles (2018). Sparse Binary Relation Representations for Genome Graph Annotation. In RECOMB 2019.

PDF Cite Project Slides DOI Code

Teaching

Courses TAed at ETH Zürich, Institute for Machine Learning:

Contact