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

Efficient discovery of frequently co-occurring mutations in a sequence database with matrix factorization

We have developed a robust method for efficiently tracking multiple co-occurring mutations in a sequence database. Evolution often hinges on the interaction of several mutations to produce significant phenotypic changes that lead to the proliferation of a variant. However, identifying numerous simultaneous mutations across a vast database of sequences poses a significant computational challenge. Our approach leverages a matrix factorization technique to automatically and efficiently pinpoint subsets of positions where co-mutations occur, appearing in a substantial number of sequences within the database. We validated our method using SARS-CoV-2 receptor-binding domains, comprising approximately seven hundred thousand sequences of the Spike protein, demonstrating superior performance compared to a reasonably exhaustive brute-force method. Furthermore, we explore the biological significance of the identified co-mutational positions (CMPs) and their potential impact on the virus's evolution and functionality, identifying key mutations in Delta and Omicron variants. This analysis underscores the significant role of identified CMPs in understanding the evolutionary trajectory. By tracking the "birth" and "death" of CMPs, we can elucidate the persistence and impact of specific groups of mutations across different viral strains, providing valuable insights into the virus' adaptability and thus, possibly aiding vaccine design strategies.

Ribotyping Staphylococcus epidermidis Using Probabilistic Sequence Analysis and Levenshtein Distance Algorithm

Staphylococcus epidermidis (S. epidermidis) live in different human locations and natural environments. For ribotyping S. epidermidis sub-species, 2507 PCR-amplified reads of 16S rRNA genes of S. epidermidis in a public dataset were used for probabilistic sequence analysis. A sequence probability logo (sequence pLogo) as a reference sequence of 16S rRNA genes of S. epidermidis was constructed. Through implementation of Levenshtein Distance algorithm, two 20-base pairs (bp) motifs, commonly present in 2507 PCR-amplified reads, were identified. The top 38 S. epidermidis isolates, which carried 16S rRNA nucleotide domains that were made of different sequences but have high similarity scores to two 20-bp motifs, were found from 11 human, 8 animal, 9 plant and 10 environmental samples, indicating that these two 20-bp motifs were broadly present in diverse S. epidermidis isolates. Thirty-one PCR-amplified reads of 16S rRNA genes, which were currently not in the dataset, were utilized to verify the feasibility of using two 20-bp motifs for ribotyping S. epidermidis sub-species. S. epidermidis S1, S3, but not S2, isolates on the human scalp carried a 20-bp sequence domain with high similarities to a 20-bp motif in the sequence pLogo. The phylogenetic tree showed that S. epidermidis S1, S2 and S3 were not from a single common ancestor. Two newly identified 20-bp motifs here, thus, provided reference nucleotide residues for ribotyping S. epidermidis.

Cross-domain information fusion and personalized recommendation in artificial intelligence recommendation system based on mathematical matrix decomposition

Given the challenges of inter-domain information fusion and data sparsity in collaborative filtering algorithms, this paper proposes a cross-domain information fusion matrix decomposition algorithm to enhance the accuracy of personalized recommendations in artificial intelligence recommendation systems. The study begins by collecting Douban movie rating data and social network information. To ensure data integrity, Levenshtein distance detection is employed to remove duplicate scores, while natural language processing technology is utilized to extract keywords and topic information from social texts. Additionally, graph convolutional networks are utilized to convert user relationships into feature vectors, and a unique thermal coding method is used to convert discrete user and movie information into binary matrices. To prevent overfitting, the Ridge regularization method is introduced to gradually optimize potential feature vectors. Weighted average and feature connection techniques are then applied to integrate features from different fields. Moreover, the paper combines the item-based collaborative filtering algorithm with merged user characteristics to generate personalized recommendation lists.In the experimental stage, the paper conducts cross-domain information fusion optimization on four mainstream mathematical matrix decomposition algorithms: alternating least squares method, non-negative matrix decomposition, singular value decomposition, and latent factor model (LFM). It compares these algorithms with the non-fused approach. The results indicate a significant improvement in score accuracy, with mean absolute error and root mean squared error reduced by 12.8% and 13.2% respectively across the four algorithms. Additionally, when k = 10, the average F1 score reaches 0.97, and the ranking accuracy coverage of the LFM algorithm increases by 54.2%. Overall, the mathematical matrix decomposition algorithm combined with cross-domain information fusion demonstrates clear advantages in accuracy, prediction performance, recommendation diversity, and ranking quality, and improves the accuracy and diversity of the recommendation system. By effectively addressing collaborative filtering challenges through the integration of diverse techniques, it significantly surpasses traditional models in recommendation accuracy and variety.

DUHI: Dynamically updated hash index clustering method for DNA storage

The exponential growth of global data leads to the problem of insufficient data storage capacity. DNA storage can be an ideal storage method due to its high storage density and long storage time. However, the DNA storage process is subject to unavoidable errors that can lead to increased cluster redundancy during data reading, which in turn affects the accuracy of the data reads. This paper proposes a dynamically updated hash index (DUHI) clustering method for DNA storage, which clusters sequences by constructing a dynamic core index set and using hash lookup. The proposed clustering method is analyzed in terms of overall reliability evaluation and visualization evaluation. The results show that the DUHI clustering method can reduce the redundancy of more than 10% of the sequences within the cluster and increase the reconstruction rate of the sequences to more than 99%. Therefore, our method solves the high redundancy problem after DNA sequence clustering, improves the accuracy of data reading, and promotes the development of DNA storage.

Application of third-generation sequencing to herbal genomics

There is a long history of traditional medicine use. However, little genetic information is available for the plants used in traditional medicine, which limits the exploitation of these natural resources. Third-generation sequencing (TGS) techniques have made it possible to gather invaluable genetic information and develop herbal genomics. In this review, we introduce two main TGS techniques, PacBio SMRT technology and Oxford Nanopore technology, and compare the two techniques against Illumina, the predominant next-generation sequencing technique. In addition, we summarize the nuclear and organelle genome assemblies of commonly used medicinal plants, choose several examples from genomics, transcriptomics, and molecular identification studies to dissect the specific processes and summarize the advantages and disadvantages of the two TGS techniques when applied to medicinal organisms. Finally, we describe how we expect that TGS techniques will be widely utilized to assemble telomere-to-telomere (T2T) genomes and in epigenomics research involving medicinal plants.