Bioinformatics research group

We develop computational methods for faster and more reliable analysis of genomic sequences. Some examples of such methods are:
* prediction of antibiotic resistance and plasmid content of bacterial isolate from its genomic sequence [1,2];
* detection of microbial pathogens from human cell-free DNA, from wastewater samples and from other relevant metagenomic samples;
* detection of rare variants in personal human genomes [3,4];
* surveillance of food composition and food origin, based on traces of DNA detected from food [5].

Our future plan is to develop a large set of DNA sequence-based predictive models and software for surveillance of bacterial pathogens (virulence, resistance to drugs or cleaning agents), microbiome and food composition.


Aun E, Brauer A, Kisand V, Tenson T and Remm M. (2018). A k-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria. PLoS Computational Biology, 14(10):e1006434

Roosaare M, Puustusmaa M, Möls M, Vaher M, and Remm M. (2018). PlasmidSeeker: identification of known plasmids from bacterial whole genome sequencing reads. PeerJ, 6: e4588

Puurand T, Kukuškina V, Pajuste FD and Remm M. (2019). AluMine: alignment-free method for the discovery of polymorphic Alu element insertions. Mobile DNA, 10:31

Kaplinski L, Möls M, Puurand T, Pajuste FD, Remm M (2021). KATK: Fast genotyping of rare variants directly from unmapped sequencing reads. Human Mutation, 42(6):777-786

Raime K, Krjutškov K and Remm M. (2020). Method for the Identification of Plant DNA in Food Using Alignment-Free Analysis of Sequencing Reads: A Case Study on Lupin. Front. Plant Sci., 11:646

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Research and Development Projects


IMCB in-house seminars: spring 2023

MR Angela Ivask teadusgrupp Autor Aleksandr Käkinen

Microbe & material interactions research group