Opportunities available at all levels: postdoc / PhD student / intern / research engineer. Don't hesitate to get in touch!

	Léo Ackermann PhD student, Inria, co-supervised with Pierre Peterlongo
	Tam Truong PhD student, Irisa, co-supervised with Pierre Peterlongo and Dominique Lavenier
	Marie Picard MSc student, École normale supérieure de Rennes
	Ján Plachý MSc student, Charles University, co-supervised with Pavel Veselý

Alumni:

Selected publications

1. K. Břinda, L. Lima, S. Pignotti, N. Quinones-Olvera, K. Salikhov, R. Chikhi, G. Kucherov, Z. Iqbal, and M. Baym.

   Efficient and robust search of microbial genomes via phylogenetic compression.

   Nature Methods 22, 692–697, 2025.

   PDF

2. O. Sladký, P. Veselý, and K. Břinda.

   From superstring to indexing: a space-efficient index for unconstrained k-mer sets using the Masked Burrows-Wheeler Transform (MBWT).

   Bioinformatics Advances 6(1), vbaf290, 2026.

   PDF

3. K. Břinda, M. Baym, and G. Kucherov.

   Simplitigs as an efficient and scalable representation of de Bruijn graphs.

   Genome Biology 22:96, 2021.

   PDF

4. K. Břinda, A. Callendrello, K. C. Ma, D. R. MacFadden, T. Charalampous, R. S. Lee, L. Cowley, C. B. Wadsworth, Y. H. Grad, G. Kucherov, J. O’Grady, M. Baym, and W. P. Hanage.

   Rapid inference of antibiotic resistance and susceptibility by genomic neighbour typing.

   Nature Microbiology 5, 455–464, 2020.

   PDF

5. K. Břinda, M. Sykulski, and G. Kucherov.

   Spaced seeds improve k-mer-based metagenomic classification.

   Bioinformatics 31(22), 3584–3592, 2015.

   PDF

Complete list

See the complete publications page or Google Scholar.

A. Phylogenetic compression

Framework implementing phylogenetic compression, a technique using evolutionary history to guide compression and efficiently search large collections of microbial genomes using existing algorithms and data structures. This improves the compression ratios of assemblies, de Bruijn graphs, and k-mer indexes by one to two orders of magnitude.

Associated tools:

• Phylign – BLAST-like search across all pre-2019 bacteria on standard desktops and laptops.
• MiniPhy – Phylogenetic compression of extremely large genome collections.

B. Metagenomic classification

• ProPhyle - Accurate, resource-frugal, and deterministic metagenomic classification, based on k-mer propagation, simplitigs, and k-mer indexing using the Burrows-Wheeler Transform.
  

C. Genomic Neighbor Typing

A proof-of-concept framework for Genomic Neighbor Typing for within-minutes predictions of antibiotic resistance during nanopore sequencing. Pipeline, library, two species databases (S. pneumoniae and N. gonorrhoeae), and demonstrations of within-minutes diagnostic from isolates and metagenomes.

D. Simplitigs and masked superstrings

Novel generation of text representations for k-mer sets. Simplitigs provide compact representations of k-mer sets by covering de Bruijn graphs with vertex-disjoint paths that spell all original k-mers while drastically reducing sequence count and total length. Masked superstrings generalize this idea to overlap graphs, allowing arbitrary overlaps and using binary masks to eliminate unintended k-mers, which yields even shorter representations and unifies the handling of diverse k-mer sets. Building on these concepts, KmerCamel constructs near-optimal masked superstrings at large scale, and FMSI turns them into highly compressed BWT-based indexes that support fast membership queries and efficient set operations. ProphAsm and ProPhex provide the corresponding practical toolchain for generating these representations and using them in downstream k-mer indexing workflows.

• FMSI - memory-efficient k-mer set index based on masked superstrings and the Burrows–Wheeler Transform.
  

• KmerCamel - compressing k-mer sets using masked superstrings.
  

• ProphAsm - rapid and memory-efficient computation of simplitigs (spectrum-preserving string sets) and set operations with k-mer sets.
  

• ProPhex An efficient k-mer index based on the Burrows-Wheeler Transform.
  

E. Other tools

• RNFtools – A generic format for naming simulated sequencing reads using arbitrary tools and the associated toolkit and pipeline for read simulation and read mapper evaluation.
  

• SAMsift – Advanced filtering and tagging of SAM/BAM alignments using Python expressions.
  

• Disty McMatrixFace – Tool for computing a distance matrix from a core genome alignment.
  

• NanoSim-H – An easy-to-use simulator of nanopore reads, a fork of NanoSim.
  

For a full software list, see the GitHub page.

École normale supérieure de Rennes

• Experimental Bioinformatics; BOX (Fall 2023, 2024, and 2025; Lecturer)

  Master’s-level course (M1), focused on computational methods for genomics. Taught jointly with Léo Ackermann (before 2025 with Riccardo Vicedomini).

Harvard Medical School

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

  PhD-level course, focused on quantitative aspects of genetics and genomics, including computational and statistical methods of genomic analysis. Close collaboration with Profs. Shamil R. Sunyaev, Michael Baym, Cheng-Zhong Zhang, and Heng Li.

Czech Technical University in Prague

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

  Master’s-level course.

• Software Project; 01SWP1, 01SWP2 (Fall and spring semesters, 2010–2012; Supervisor)

  Master’s-level course.