A new DNA sequence entropy-based Kullback-Leibler algorithm for gene clustering

Dehghanzadeh, Houshang; Ghaderi–Zefrehei, Mostafa; Mirhoseini, Seyed Ziaeddin; Esmaeilkhaniyan, Saeid; Haruna, Ishaku L.; Najafabadi, Hamed Amirpour

doi:10.1007/s13353-020-00543-x

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…The highest microRNA length corresponds to bta-mir-486, which is 123 bp, while its entropy is not the highest at any of the orders. Investigating the genes and exons affecting milk production indicated that NOP2, YWHAH genes (with lengths of 60167 and 1445, respectively) and exon 1 of HSP and ACTR2 genes which are the largest and smallest investigated genes and exons in this study meet the maximum and minimum entropy values, respectively [23,24]. Also, a study on the global genome information analyzed the genome's entropy results of 25 different species according to the information theory and plotted a two-dimensional graph based on the chromosome length and entropy values for the genome of each specie.…”

Section: Different Types Of Clustering Methodsmentioning

confidence: 63%

“…Ghaderi et al (2016) implemented a simple form of this criterion to find the distance between the contigs of the Escherichia coli genome which affects mastitis [26]. Also, Dehghanzadeh et al used it to cluster several genes in dairy cattle [23]. Porto-Diaz et al (2012), who employed the information theory for clustering 12 microarray data, indicated that this is a fast and reliable way of clustering large volumes of data and considered it better compared to other methods.…”

Section: Discussionmentioning

confidence: 99%

“…As mentioned above, the entropy values have been calculated at three orders. The index that appears in H represents the order of entropy [23,24].…”

Section: Calculation Of the Entropy Ordersmentioning

confidence: 99%

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Clustering Some MicroRNAs Expressed in the Breast Tissue Using Shannon Information Theory and Comparing the Results With UPGMA, Neighbor-Joining, and Maximum-Likelihood Methods

2020

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Background: Because milk and milk products play a vital role in human nutrition, dairy cattle farmers are working in increasing milk production or changing its composition. For this reason, researching the genes which play an important role in milk production and its composition is of high value. Information theory is an interdisciplinary branch of mathematics which overlaps with communications engineering, biology, and medicine. It has been used in genetic and bioinformatics analyses such as the biological structures and sequences. Materials and methods: In this study, a total of 20 microRNAs from those affecting the breast tissue and mammary glands have been extracted from the microRNA database. For each microRNA sequence, the entropy values of the first- to third-order were calculated and the Kullback-Leibler divergence criteria were estimated. Then, the Kullback-Leibler divergence matrix of the microRNAs was considered as the inputs for clustering methods. All calculations were performed in the R program. The biological pathway of each target was predicted using the KEGG server. Results: MicroRNAs are divided into two main groups based upon comparing and analyzing all the created clusters. The first group contains 18 microRNA and the second group contains 2 microRNAs at the first- and third-order entropies. The second-order entropy contains 19 microRNA in the first group and only 1 microRNA in the second group. The clustering topology changes as the entropy order changes from 1 to 3, with the most significant changes being seen in the clustering resulted from the third-order entropy. Conclusion: In the proposed method of clustering, we obtained a biological grouping of genes. There is a good concordance between most of the microRNAs within one cluster and their biological pathway. The algorithm is applicable for clustering a range of genes and even genomes based on their DNA sequences entropy. Our method can help assign and predict the biological activity of those genes that lack robust annotations because it relies only on the DNA sequence and length of the genes.

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Section: Different Types Of Clustering Methodsmentioning

confidence: 63%

Section: Discussionmentioning

confidence: 99%

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Clustering Some MicroRNAs Expressed in the Breast Tissue Using Shannon Information Theory and Comparing the Results With UPGMA, Neighbor-Joining, and Maximum-Likelihood Methods

2020

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“…Different orders of entropies were used to derive information-based gene clustering. These authors used the same in silico method of results confirmation as we did in this study PLOS ONE [22]. In silico confirmation of biological function has found its way in gene association studies, nucleotide polymorphism detection, differential gene expression analysis and novel gene prediction [23][24][25].…”

Section: Plos Onementioning

confidence: 83%

“…Based on metabolic pathway annotations we saw that the proposed clustering method yielded logical and fast results. This method also doesn't have the disadvantages of aligning allowed the genes with actual length and content to be considered and also didn't require high compute memory for long sequences [21]. Different orders of entropies were used to derive information-based gene clustering.…”

Section: Plos Onementioning

confidence: 99%

A new gene tree algorithm employing DNA sequences of bovine genome using discrete Fourier transformation

et al. 2023

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Within the realms of human thoughts on nature, Fourier analysis is considered as one of the greatest ideas currently put forwarded. The Fourier transform shows that any periodic function can be rewritten as the sum of sinusoidal functions. Having a Fourier transform view on real-world problems like the DNA sequence of genes, would make things intuitively simple to understand in comparison with their initial formal domain view. In this study we used discrete Fourier transform (DFT) on DNA sequences of a set of genes in the bovine genome known to govern milk production, in order to develop a new gene clustering algorithm. The implementation of this algorithm is very user-friendly and requires only simple routine mathematical operations. By transforming the configuration of gene sequences into frequency domain, we sought to elucidate important features and reveal hidden gene properties. This is biologically appealing since no information is lost via this transformation and we are therefore not reducing the number of degrees of freedom. The results from different clustering methods were integrated using evidence accumulation algorithms to provide in insilico validation of our results. We propose using candidate gene sequences accompanied by other genes of biologically unknown function. These will then be assigned some degree of relevant annotation by using our proposed algorithm. Current knowledge in biological gene clustering investigation is also lacking, and so DFT-based methods will help shine a light on use of these algorithms for biological insight.

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