bioRxivBioinformaticsPreprint
A massively parallel algorithm for finding non-existing sequences in genomes
Abstract
We discuss a method for producing a set of absent words in a reference genome with a guaranteed Hamming distance along all positions and additional information about the number of mismatches, their location and the position of the best match. We implemented it exploiting the massively parallelism of modern GPUs hardware: the code is available at https://bitbucket.org/mfalda/cuda_keeseek/.
- Citations
- 0
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- A randomized parallel algorithm for efficiently finding near-optimal universal hitting sets
Barış Ekim, Bonnie Berger, Yaron Orenstein·Lecture notes in computer sciencePeer-reviewed·Jan 1, 2020
- SW#db: GPU-accelerated exact sequence similarity database search
Matija Korpar, Martin Sosic, … Mile Sikic·bioRxiv·Jan 14, 2015
- Faster sequence alignment through GPU-accelerated restriction of the seed-and-extend search space
Richard Wilton, Tamas Budavari, … Alex Szalay·bioRxiv·Aug 1, 2014
- Fast and compact matching statistics analytics
Fabio Cunial, Olgert Denas, Djamal Belazzougui·BioinformaticsPeer-reviewed·Jan 31, 2022
- VSTseed: periodic spaced seeds for reads with substitutions
Valeriy Titarenko, Sofya S. Titarenko·bioRxiv·Jun 9, 2021
- Sassy: Fuzzy Searching DNA Sequences using SIMD
Rick Beeloo, Ragnar Groot Koerkamp·BioinformaticsPeer-reviewed·May 1, 2026