Karel Břinda

Karel Břinda

Permanent Researcher (Inria Starting Faculty)

Inria/IRISA Rennes


I’m a permanent researcher / Inria Starting Faculty at Inria, the French National Research Institute for Computer Science and Automation. I’m based at the Rennes Research Center, where I’m part of the broader GenScale project team.

I work in the area of computational genomics, with a particular focus on pathogens and antibiotic resistance, and the goal of achieving their rapid diagnosis and real-time surveillance. To do so, I develop novel algorithms, data structures, software tools, and genomic databases, which are then provided to the scientific community as building blocks for larger efforts. I’m particularly interested in non-traditional applications of portable genomic technologies, such as nanopore sequencing and CRISPR-based tests, as well as in moving computation from large computational clusters to ordinary laptops and developing comprehensive sequence data search engines.

Download my CV.

  • Algorithms
  • Computational genomics
  • Rapid diagnostics
  • Pathogens
  • Antibiotic resistance
  • Postdoctoral Fellow, Research Associate, 2017–2021

    Harvard Medical School (Department of Biomedical Informatics) & Harvard TH Chan School of Public Health (Center for Communicable Disease Dynamics)

  • PhD in Computer Science, 2013–2016

    Université Paris-Est (Gaspard Monge Institute), France

  • BSc, MSc in Math. Computer Science, 2008–2013

    Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Czech Republic

Selected Publications

  1. K. Břinda, M. Baym, and G. Kucherov, “Simplitigs as an efficient and scalable representation of de Bruijn graphs,” Genome Biology, 2021.
    [preprint] [journal] [software] [analyses]

  2. K. Břinda et al., “Rapid inference of antibiotic resistance and susceptibility by Genomic Neighbour Typing,” Nature Microbiology, 2020.
    [preprint] [journal] [software] [analyses]

  3. K. Břinda, V. Boeva, and G. Kucherov, “RNF: A general framework to evaluate NGS read mappers,” Bioinformatics, 2016.
    [preprint] [journal] [software]

  4. K. Břinda, M. Sykulski, and G. Kucherov, “Spaced seeds improve k-mer-based metagenomic classification,” Bioinformatics, 2015.
    [preprint] [journal] [analyses]

For a full publication list, see my Google Scholar page.



An accurate, resource-frugal, and deterministic metagenomic classifier, based on k-mer propagation, simplitigs, and k-mer indexing using the Burrows-Wheeler Transform. Written in Python. See http://prophyle.github.io.



A proof-of-concept framework for Genomic Neighbor Typing for real-time predictions of antibiotic resistance during nanopore sequencing. Pipeline, tool, library, two species databases (S. pneumoniae and N. gonorrhoeae), and demonstrations of within-minutes diagnostic from isolates and metagenomes. Written in Python/Snakemake/Make. See https://github.com/c2-d2/rase.


A tool for a rapid and memory-efficient computation of simplitigs (spectrum-preserving string sets) and set operations with k-mer sets. Written in C++. See http://github.com/prophyle/prophasm..



Online variant and consensus caller, based on streaming and maintaining variant statistics in small counters per individual genomic positions, which enables real-time analyzes of unsorted SAM/BAM data. Written in C++. See http://github.com/karel-brinda/ococo.



An efficient k-mer index based on the Burrows-Wheeler Transform and a rolling window. Co-developed with Kamil Salikhov. Written in C. See http://github.com/prophyle/prophex.



A generic format for naming simulated sequencing reads using arbitrary tools and the associated toolkit and pipeline for read simulation and read mapper evaluation. Written in Snakemake/Python. See http://rnftools.github.io.



A simulator of dynamic read mapping. Written in Python/Snakemake. See http://github.com/karel-brinda/dymas.


Advanced filtering and tagging of SAM/BAM alignments using Python expressions. See http://github.com/karel-brinda/samsift.


Disty McMatrixFace

Tool for computing a distance matrix from a core genome alignment. Written in C++. See http://github.com/c2-d2/disty.



Simulator of nanopore reads (a fork of the NanoSim package). See http://github.com/karel-brinda/nanosim-h.



Snakemake bioinformatics library (retired). See http://github.com/karel-brinda/smbl.


Prospective students and postdocs

I’m hiring! If you are interested in working with me as a student (M1, M2, or PhD) or a postdoc on a topic related to rapid diagnostics of antibiotic resistance, sequence data search engines, or computational metagenomics, please contact me at karel.brinda@inria.fr.

Harvard Medical School

  • Concepts in Genome Analysis (BMIF 201) (Fall 2019; TA)

    Instructors: Profs. Shamil R. Sunyaev, Michael Baym, Cheng-Zhong Zhang, and Heng Li

    The course focused on quantitative aspects of genetics and genomics, including computational and statistical methods of genomic analysis.

Czech Technical University in Prague

  • Assistive Technology (01ASTE) (Falls 2010–2012 ; Instructor)

  • Software Project (01SWP1, 01SWP2) (Falls and springs 2010–2012 ; Supervisor)



  • BBC World Service – Science in Action – 13 Feb 2020

    Our paper about rapid diagnostics of antibiotic resistance was covered by BBC World Service in the show Science in Action (13 Feb 2020; starts at 8.10 minutes). 2020-BBC-ScienceInAction-GNT.mp3

  • The Bioinformatics Chat – Spectrum-preserving string sets and simplitigs – 28 Feb 2020

    Our paper about simplitigs for an efficient and scalable representation of de Bruijn graph was covered by the The Bioinformatics Chat podcast series (#42, 28 Feb 2020). 2020-TheBioinformaticsChat-Simplitigs.mp3

Other media coverage of my work