Giulio Ermanno Pibiri

@jermp.bsky.social

Associate Prof. of CS at Ca' Foscari University of Venice. Indexing, Data Compression, Algorithms.

I have known about this result for about 3 years, but since Thomas and Jakob are working in industry it took some time to get the paper written. A nice contrast to the rush to publish that we see too often, and as a bonus the paper is (partly) Lean formalized. Congratulations on this achievement!

PSA: @jermp.bsky.social has a new release of sshash! It eliminates the ‘canonical’ choice when indexing, b/c the new index gives the best of both worlds by adopting a forward canonical minimizer scheme. Size of the unidirectional index/speed of canonical (faster in fact) github.com/jermp/sshash...

Release Version 6.0.0 · jermp/sshash

What's Changed Refactor SSHash indexing to unify parsing modalities and optimize performance by @jermp in #93 Full Changelog: v5.1.1...v6.0.0

github.com

Excited to be at #ISMB2026 this year and to be presenting the work @jermp.bsky.social and I have done on improving the SSHash data structure academic.oup.com/bioinformati.... Presentation is tomorrow, July 13 at 2:20 in the International Ballroom Center. Stop by if you're attending!

Optimizing sparse and skew hashing: faster k-mer dictionaries

AbstractMotivation. Representing a set of k-mers—strings of length k—in small space under fast lookup queries is a fundamental requirement for several appl

academic.oup.com

P

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.

This preprint significantly improves the original design of SSHash to accelerate query and construction time. Looking back at one’s own work is very important and sometimes surprising ☺️

Rob Patro@robp.bsky.social · 9mo ago

Very excited about this latest work led by @jermp.bsky.social! Since it's initial release, SSHash has served as the basis for several other tools (Fulgor, piscem, etc.). It was already very fast. It is now *substantially* faster! www.biorxiv.org/content/10.6...