Our paper titled “A k-mer-based maximum likelihood method for estimating distances of reads to genomes enables genome-wide phylogenetic placement” has been accepted in RECOMB 2025. I’m looking forward to my presentation in Seoul, which will be my first visit to East Asia.
We introduced a technique to estimate read-to-genome distances from k-mer hits. The idea is based on the search for matching k-mers up to a certain Hamming distance in a colored k-mer index, and then finding the maximum likelihood distance based on k-mer matches and corresponding distances for each hitting reference. These distances approximate alignment Hamming distance at the read level, and 1-ANI between a query genome and the matching reference(s) on average across all reads. We then propose an intuitive and accurate heuristic to place reads on an existing backbone phylogeny in a principled way using a likelihood ratio test.
Availability: The tool is available on GitHub, and under active development. All results, auxiliary data, and scripts used in the analyses can be found in the shared.krepp repository.
Our paper has been published in Genome Research
I presented our latest tool for phylogenetic placement in RECOMB 2025