3GOLD: optimized Levenshtein distance for clustering third-generation sequencing data

Robert Logan; Zoe Fleischmann; Sofia Annis; Amy Wangsness Wehe; Jonathan L Tilly; Dori C Woods; Konstantin Khrapko

doi:10.1186/s12859-022-04637-7

3GOLD: optimized Levenshtein distance for clustering third-generation sequencing data

BMC Bioinformatics. 2022 Mar 20;23(1):95. doi: 10.1186/s12859-022-04637-7.

Authors

Robert Logan^{1

2}, Zoe Fleischmann¹, Sofia Annis¹, Amy Wangsness Wehe³, Jonathan L Tilly¹, Dori C Woods¹, Konstantin Khrapko⁴

Affiliations

¹ College of Science, Department of Biology, Northeastern University, 330 Huntington Ave, Boston, MA, 02115, USA.
² Department of Biology, Eastern Nazarene College, 23 E Elm Ave, Quincy, MA, 02170, USA.
³ Health and Natural Sciences Division, Mathematics Department, Fitchburg State University, Fitchburg, MA, 01420-2697, USA.
⁴ College of Science, Department of Biology, Northeastern University, 330 Huntington Ave, Boston, MA, 02115, USA. k.khrapko@northeastern.edu.

Abstract

Background: Third-generation sequencing offers some advantages over next-generation sequencing predecessors, but with the caveat of harboring a much higher error rate. Clustering-related sequences is an essential task in modern biology. To accurately cluster sequences rich in errors, error type and frequency need to be accounted for. Levenshtein distance is a well-established mathematical algorithm for measuring the edit distance between words and can specifically weight insertions, deletions and substitutions. However, there are drawbacks to using Levenshtein distance in a biological context and hence has rarely been used for this purpose. We present novel modifications to the Levenshtein distance algorithm to optimize it for clustering error-rich biological sequencing data.

Results: We successfully introduced a bidirectional frameshift allowance with end-user determined accommodation caps combined with weighted error discrimination. Furthermore, our modifications dramatically improved the computational speed of Levenstein distance. For simulated ONT MinION and PacBio Sequel datasets, the average clustering sensitivity for 3GOLD was 41.45% (S.D. 10.39) higher than Sequence-Levenstein distance, 52.14% (S.D. 9.43) higher than Levenshtein distance, 55.93% (S.D. 8.67) higher than Starcode, 42.68% (S.D. 8.09) higher than CD-HIT-EST and 61.49% (S.D. 7.81) higher than DNACLUST. For biological ONT MinION data, 3GOLD clustering sensitivity was 27.99% higher than Sequence-Levenstein distance, 52.76% higher than Levenshtein distance, 56.39% higher than Starcode, 48% higher than CD-HIT-EST and 70.4% higher than DNACLUST.

Conclusion: Our modifications to Levenshtein distance have improved its speed and accuracy compared to the classic Levenshtein distance, Sequence-Levenshtein distance and other commonly used clustering approaches on simulated and biological third-generation sequenced datasets. Our clustering approach is appropriate for datasets of unknown cluster centroids, such as those generated with unique molecular identifiers as well as known centroids such as barcoded datasets. A strength of our approach is high accuracy in resolving small clusters and mitigating the number of singletons.

Keywords: Clustering; Edit-distance; Single-molecule sequencing.

MeSH terms

Algorithms*
Cluster Analysis
High-Throughput Nucleotide Sequencing*
Sequence Analysis, DNA

Abstract

MeSH terms

Grants and funding