Abstract
AbstractMotivationK-mer hashing is a common operation in many foundational bioinformatics problems. However, generic string hashing algorithms are not optimized for this application. Strings in bioinformatics use specific alphabets, a trait leveraged for nucleic acid sequences in earlier work. We note that amino acid sequences, with complexities and context that cannot be captured by generic hashing algorithms, can also benefit from a domain-specific hashing algorithm. Such a hashing algorithm can accelerate and improve the sensitivity of bioinformatics applications developed for protein sequences.ResultsHere, we present aaHash, a recursive hashing algorithm tailored for amino acid sequences. This algorithm utilizes multiple hash levels to represent biochemical similarities between amino acids. aaHash performs ∼10X faster than generic string hashing algorithms in hashing adjacentk-mers.Availability and implementationaaHash is available online athttps://github.com/bcgsc/btlliband is free for academic use.
Publisher
Cold Spring Harbor Laboratory
Reference24 articles.
1. The emerging landscape of single-molecule protein sequencing technologies;Nat Methods,2021
2. UniProt: the Universal Protein Knowledgebase in 2023;Nucleic Acids Res,2023
3. Space/time trade-offs in hash coding with allowable errors;Commun ACM,1970
4. Chakravarti, I.M. et al. (1967) Handbook of methods of applied statistics. Wiley Series in Probability and Mathematical Statistics (USA) eng.