Igor Martayan

@imartayan.bsky.social

PhD student in algorithmic bioinformatics at @bonsaiseqbioinfo.bsky.social. Interested in space-efficient data structures, sketching algorithms & high-performance computing https://igor.martayan.org

My secret 4th ALGO paper is out! We show a tight space lower on non-minimal k-perfect hash functions, generalize PtrHash into a non-minimal k-PHF, and then use it to develop a hash set implementation that is up to 1.6x faster than other hash sets! With Stefan {Hermann, Walzer} and Peter Sanders

arXiv cs.DS Data Structures and Algorithms@csds-bot.bsky.social · 4w ago

Ragnar Groot Koerkamp, Stefan Hermann, Peter Sanders, Stefan Walzer: Non-minimal k-perfect hashing: Tight lower bounds and an application to fast static hash tables https://arxiv.org/abs/2607.07257 https://arxiv.org/pdf/2607.07257 https://arxiv.org/html/2607.07257

In other news: Great SEA talk by Nathaniel Brown on Orbit, an efficient implementation of the move structure for run-length encoded permutations. Also, congrats on winning a best paper award with this work!

Nathaniel standing in front of his title slide at the start of the presentation.
Ben Langmead@benlangmead.bsky.social · last mo.

Movi 2 has appeared (as an advance article) in Bioinformatics 🧬 Faster, leaner pangenome queries — half the memory of Movi 1, ~30% faster. Paper: academic.oup.com/bioinformati... Code: github.com/mohsenzakeri/Movi (1/6)

Is anyone attending #RECOMB2026 with a talk in the Sequencing 2 session who would be willing to switch slots with a talk in Sequencing 1 a day earlier? We’d be very grateful. Please reach out if you might be willing to do this!

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This is now published in Genome Research (doi.org/10.1101/gr.2...). Thank you everyone for your feedback and also the anonymous reviewers who helped to greatly improve the paper. I hope this becomes a useful resource for the community.

Hash functions in nucleotide sequence analysis

Randomness is a powerful tool in the design and analysis of algorithms and data structures for nucleotide sequence data. Nucleotide sequences are not themselves random but are often randomized using hash functions. Despite their widespread use in genomics, there is no comprehensive review of the types of hash functions used and their various applications. In this survey intended for bioinformatic methods developers, we divide hash functions into four categories: scattering hash functions, permutations, minimum perfect hash functions, and locality-sensitive hash functions. For each category, we provide examples of both general-use hash functions that have been applied in nucleotide sequence analysis and hash functions that have been designed specifically for nucleotide sequence analysis. We highlight their salient properties, commonalities, differences, and application areas.

doi.org

PPaul Medvedev @pashadag.bsky.social · last yr.

1/4 Hash functions in genomic sequence analysis (tinyurl.com/4kk9ccmt) : a new survey written together with Ke Chen, Xiang Li, Qian Shi, and Mingfu Shao. Before submitting it, we are posting it online to get feedback from the community.