Mining Contiguous Sequential Generators in Biological Sequences

Zhang, Jingsong; Wang, Yinglin; Zhang, Chao; Shi, Yongyong

doi:10.1109/tcbb.2015.2495132

Cited by 21 publications

(7 citation statements)

References 37 publications

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“…(ii) ORF8_t-28144-c, NSP4_c-8782-t, NSP14_c-18060-t, NSP13_a-17858-g, and NSP13_c-17747-t; and (iii) N_g-28881-a, N_g-28882-a, and N_g-28883-c. We further quantified the cooccurrence significance (the ratio of co-occurrence mutants to all sequence examples, also called Support (17)(18)(19) in data mining) among the top 11 mutation sites (mutation rate>10%) ( Fig. 2E to 2I).…”

Section: Strong Co-occurrent Mutations Appeared On Multiple Sites Ovementioning

confidence: 99%

Multi-site co-mutations and 5’UTR CpG immunity escape drive the evolution of SARS-CoV-2

Zhang

Kang

Liu

et al. 2020

Preprint

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The SARS-CoV-2 infected cases and the caused mortalities have been surging since the COVID-19 pandemic. Viral mutations emerge during the virus circulating in the population, which is shaping the viral infectivity and pathogenicity. Here we extensively analyzed 6698 SARS-CoV-2 whole genome sequences with specific sample collection dates in NCBI database. We found that four mutations, i.e., 5’UTR_c-241-t, NSP3_c-3037-t, NSP12_c-14408-t, and S_a-23403-g, became the dominant variants and each of them represented nearly 100% of all virus sequences since the middle May, 2020. Notably, we found that co-occurrence rates of three significant multi-site co-mutational patterns, i.e., (i) S_a-23403-g, NSP12_c-14408-t, 5’UTR_c-241-t, NSP3_c-3037-t, and ORF3a_c-25563-t; (ii) ORF8_t-28144-c, NSP4_c-8782-t, NSP14_c-18060-t, NSP13_a-17858-g, and NSP13_c-17747-t; and (iii) N_g-28881-a, N_g-28882-a, and N_g-28883-c, reached 66%, 90%, and nearly 100% of recent sequences, respectively. Moreover, we found significant decrease of CpG dinucleotide at positions 241(c)-242(g) in the 5’UTR during the evolution, which was verified as a potential target of human zinc finger antiviral protein (ZAP). The four dominant mutations, three significant multi-site co-mutations, and the potential escape mutation of ZAP-target in 5’UTR region contribute to the rapid evolution of SARS-CoV-2 virus in the population, thus shaping the viral infectivity and pathogenicity. This study provides valuable clues and frameworks to dissect the viral replication and virus-host interactions for designing effective therapeutics.One Sentence SummaryFour dominant mutations, three significant multi-site co-mutations, and 5’UTR CpG escape contribute to the rapid evolution of SARS-CoV-2 virus.

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Section: Strong Co-occurrent Mutations Appeared On Multiple Sites Ovementioning

confidence: 99%

Multi-site co-mutations and 5’UTR CpG immunity escape drive the evolution of SARS-CoV-2

Zhang

Kang

Liu

et al. 2020

Preprint

Self Cite

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“…Several studies [22], [43] combined closed and contiguous constraints for SPM with the aim of obtaining a more concise pattern set without loss of information. Other recent studies applied CSPM to a wide range of realistic applications, such as vehicle trajectory analysis [19], [24], protocol specification extraction [44], and biological sequence analysis [20], [45].…”

Section: Cspm and Ucspmmentioning

confidence: 99%

Utility-driven Mining of Contiguous Sequences

Zhang¹,

Dai²,

Du³

et al. 2021

Preprint

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Recently, contiguous sequential pattern mining (CSPM) gained interest as a research topic, due to its varied potential real-world applications, such as web log and biological sequence analysis. To date, studies on the CSPM problem remain in preliminary stages. Existing CSPM algorithms lack the efficiency to satisfy users' needs and can still be improved in terms of runtime and memory consumption. In addition, existing algorithms were developed to deal with simple sequence data, working with only one event at a time. Complex sequence data, which represent multiple events occurring simultaneously, are also commonly observed in real life. In this paper, we propose a novel algorithm, fast utility-driven contiguous sequential pattern mining (FUCPM), to address the CSPM problem. FUCPM adopts a compact sequence information list and instance chain structures to store the necessary information of the database and candidate patterns. For further efficiency, we develop the global unpromising items pruning and local unpromising items pruning strategies, based on sequence-weighted utilization and item-extension utilization, to reduce the search space. Extensive experiments on real-world and synthetic datasets demonstrate that FUCPM outperforms the state-of-the-art algorithms and is scalable enough to handle complex sequence data.

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“…We compare the substrings identified by Lexis with the substrings generated by a recent contiguous pattern mining algorithm called ConSgen [27] (we could only run it on the smallest available dataset). Additionally, we compare the Lexis substrings with the set of patterns containing all 2and 3-grams of the data.…”

Section: Compressionmentioning

confidence: 99%

Lexis

Siyari

Dilkina

Dovrolis

2016

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Data represented as strings abounds in biology, linguistics, document mining, web search and many other fields. Such data often have a hierarchical structure, either because they were artificially designed and composed in a hierarchical manner or because there is an underlying evolutionary process that creates repeatedly more complex strings from simpler substrings. We propose a framework, referred to as Lexis, that produces an optimized hierarchical representation of a given set of "target" strings. The resulting hierarchy, "Lexis-DAG", shows how to construct each target through the concatenation of intermediate substrings, minimizing the total number of such concatenations or DAG edges. The Lexis optimization problem is related to the smallest grammar problem. After we prove its NP-hardness for two cost formulations, we propose an efficient greedy algorithm for the construction of Lexis-DAGs. We also consider the problem of identifying the set of intermediate nodes (substrings) that collectively form the "core" of a Lexis-DAG, which is important in the analysis of Lexis-DAGs. We show that the Lexis framework can be applied in diverse applications such as optimized synthesis of DNA fragments in genomic libraries, hierarchical structure discovery in protein sequences, dictionary-based text compression, and feature extraction from a set of documents.1 Lexis means "word" in Greek.

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Mining Contiguous Sequential Generators in Biological Sequences

Cited by 21 publications

References 37 publications

Multi-site co-mutations and 5’UTR CpG immunity escape drive the evolution of SARS-CoV-2

Multi-site co-mutations and 5’UTR CpG immunity escape drive the evolution of SARS-CoV-2

Utility-driven Mining of Contiguous Sequences

Lexis

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