A novel alignment-free DNA sequence similarity analysis approach based on top-k n-gram match-up

Delibaş, Emre; Arslan, Ahmet; Şeker, Abdulkadir; Di̇ri̇, Banu

doi:10.1016/j.jmgm.2020.107693

Cited by 9 publications

(13 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The comparative result is presented in Table 9 . We can see that our method achieves 100% accuracy for this dataset too which is very promising and clearly ahead (19% more accurate) from Delibaş et al [ 9 ] result. It also indicates our method is very effective and efficient for whole genome DNA sequence similarity identification.…”

Section: Resultssupporting

confidence: 66%

“…The details of the 6 standard genome datasets are (i) complete mitochondrial DNA sequences of 25 cichild fish samples (Table 1 ), (ii) 8 Yersinia strains (Table 2 ), (iii) 16 S ribosomal DNA of 13 Bacteria [ 8 , 9 ], (iv) 18 Eutherian mammals [ 8 , 9 ], (v) HIV-1 [ 25 ], and (vi) HEV [ 25 ].…”

Section: Resultsmentioning

confidence: 99%

“…We choose another existing dataset 18 Eutherian Mammal used by Delibaş et al [ 9 ], Jin et al [ 15 ] etc. Sequence length varies approximately from 16 to 17 thousand.…”

Section: Resultsmentioning

confidence: 99%

“…Because it indicates how faster an algorithm can provide results. Different researchers including Delibaş et al [ 9 ] computed the complexity in terms of machine clock cycle. However, we discuss our time complexity in two steps.…”

Section: Resultsmentioning

confidence: 99%

“…However, they did not apply their method for a large benchmark dataset to find their methods’ accuracy, error and rank. Again, Delibaş et al [ 9 ] proposed based solution and calculated from the count of s. They applied their system in different datasets including AFproject [ 40 ] fish benchmark dataset where they achieved rank 6 with 68% accuracy.…”

Section: Background Studymentioning

confidence: 99%

See 4 more Smart Citations

A fast and efficient algorithm for DNA sequence similarity identification

Uddin

Islam

Hassan

et al. 2022

Complex Intell. Syst.

View full text Add to dashboard Cite

DNA sequence similarity analysis is necessary for enormous purposes including genome analysis, extracting biological information, finding the evolutionary relationship of species. There are two types of sequence analysis which are alignment-based (AB) and alignment-free (AF). AB is effective for small homologous sequences but becomes NP-hard problem for long sequences. However, AF algorithms can solve the major limitations of AB. But most of the existing AF methods show high time complexity and memory consumption, less precision, and less performance on benchmark datasets. To minimize these limitations, we develop an AF algorithm using a 2D $$k-mer$$ k - m e r count matrix inspired by the CGR approach. Then we shrink the matrix by analyzing the neighbors and then measure similarities using the best combinations of pairwise distance (PD) and phylogenetic tree methods. We also dynamically choose the value of k for $$k-mer$$ k - m e r . We develop an efficient system for finding the positions of $$k-mer$$ k - m e r in the count matrix. We apply our system in six different datasets. We achieve the top rank for two benchmark datasets from AFproject, 100% accuracy for two datasets (16 S Ribosomal, 18 Eutherian), and achieve a milestone for time complexity and memory consumption in comparison to the existing study datasets (HEV, HIV-1). Therefore, the comparative results of the benchmark datasets and existing studies demonstrate that our method is highly effective, efficient, and accurate. Thus, our method can be used with the top level of authenticity for DNA sequence similarity measurement.

show abstract

Section: Resultssupporting

confidence: 66%

Section: Resultsmentioning

confidence: 99%

“…We choose another existing dataset 18 Eutherian Mammal used by Delibaş et al [ 9 ], Jin et al [ 15 ] etc. Sequence length varies approximately from 16 to 17 thousand.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Background Studymentioning

confidence: 99%

See 3 more Smart Citations

A fast and efficient algorithm for DNA sequence similarity identification

Uddin

Islam

Hassan

et al. 2022

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

Phylogeny Reconstruction Using $$k-mer$$ Derived Transition Features

Yousuf,

Uddin,

Islam

et al. 2024

Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning

View full text Add to dashboard Cite

DNA N-gram Analysis Framework (DNAnamer): A generalized N-gram frequency analysis framework for the supervised classification of DNA sequences

Malamon

2024

Preprint

View full text Add to dashboard Cite

In 1948, Claude Shannon published a mathematical system describing the probabilistic relationships between the letters of a natural language and their subsequent order or syntax structure. By counting unique, reoccurring sequences of letters called N-grams, this language model was used to generate recognizable English sentences from N-gram frequency probability tables. More recently, N-gram analysis methodologies have been successful in addressing many complex problems in a variety of domains, from language processing to genomics. One such example is the common use of N-gram frequency patterns and supervised classification models to determine authorship and plagiarism. In this methodology, DNA is a language model where nucleotides are analogous to the letters of a word and nucleotide N-grams are analogous to the words of a sentence. Because DNA contains highly conserved and identifiable nucleotide sequence frequency patterns, this approach can be applied to a variety of classification and data reduction problems, such as identifying species based on unknown DNA segments. Other useful applications of this methodology include the identification of functional gene elements, sequence contamination, and sequencing artifacts. To this end, I present DNAnamer, a generalized and extensible methodological framework and analysis toolkit for the supervised classification of DNA sequences based on their N-gram frequency patterns.

show abstract

A novel alignment-free DNA sequence similarity analysis approach based on top-k n-gram match-up

Cited by 9 publications

References 37 publications

A fast and efficient algorithm for DNA sequence similarity identification

A fast and efficient algorithm for DNA sequence similarity identification

Phylogeny Reconstruction Using $$k-mer$$ Derived Transition Features

DNA N-gram Analysis Framework (DNAnamer): A generalized N-gram frequency analysis framework for the supervised classification of DNA sequences

Contact Info

Product

Resources

About