ArticleIEEE transactions on information theory2021
Levenshtein Distance, Sequence Comparison and Biological Database Search.
Article in IEEE transactions on information theory, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
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- Deciphering Complex Interactions Between LTR Retrotransposons and Three Papaver Species Using LTR_Stream.Genomics, proteomics & bioinformatics · 2025Article
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- NLP-like deep learning aided in identification and validation of thiosulfinate tolerance clusters in diverse bacteria.mSphere · 2025Article
- Racial and urban-rural disparities in lung cancer care: Insight from a Latent Class Growth Analysis.Journal of cancer policy · 2025Article
- Assessment and Integration of Large Language Models for Automated Electronic Health Record Documentation in Emergency Medical Services.Journal of medical systems · 2025Article
- TransMA: an explainable multi-modal deep learning model for predicting properties of ionizable lipid nanoparticles in mRNA delivery.Briefings in bioinformatics · 2025Article
- Ribotyping Staphylococcus epidermidis Using Probabilistic Sequence Analysis and Levenshtein Distance Algorithm.Current microbiology · 2025Article
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3 authors.
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Abstract
Levenshtein edit distance has played a central role-both past and present-in sequence alignment in particular and biological database similarity search in general. We start our review with a history of dynamic programming algorithms for computing Levenshtein distance and sequence alignments. Following, we describe how those algorithms led to heuristics employed in the most widely used software in bioinformatics, BLAST, a program to search DNA and protein databases for evolutionarily relevant similarities. More recently, the advent of modern genomic sequencing and the volume of data it generates has resulted in a return to the problem of local alignment. We conclude with how the mathematical formulation of Levenshtein distance as a metric made possible additional optimizations to similarity search in biological contexts. These modern optimizations are built around the low metric entropy and fractional dimensionality of biological databases, enabling orders of magnitude acceleration of biological similarity search.
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