Integrated entropy-based approach for analyzing exons and introns in DNA sequences

Li, Junyi; Zhang, Li; Li, Huinian; Ping, Yuan; Xu, Qingzhe; Wang, Rongjie; Tan, Renjie; Zhen, Wang; Liu, Bo; Wang, Yadong

doi:10.1186/s12859-019-2772-y

Cited by 15 publications

(10 citation statements)

References 43 publications

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“…It has been used to characterize the randomness of DNA sequences [51]. More specific varieties of entropies such as the GTE have been successfully applied in [52,53] to explore and to compare the complexity of introns, exons, and promoter regions. Based on the findings of these studies, we included GTE as an additional feature in our analysis which could provide an important information.…”

Section: Sequence Features Used To Predict Promotersmentioning

confidence: 99%

Identification of Regulatory SNPs Associated with Vicine and Convicine Content of Vicia faba Based on Genotyping by Sequencing Data Using Deep Learning

Heinrich

Wutke

Das

et al. 2020

Genes

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Faba bean (Vicia faba) is a grain legume, which is globally grown for both human consumption as well as feed for livestock. Despite its agro-ecological importance the usage of Vicia faba is severely hampered by its anti-nutritive seed-compounds vicine and convicine (V+C). The genes responsible for a low V+C content have not yet been identified. In this study, we aim to computationally identify regulatory SNPs (rSNPs), i.e., SNPs in promoter regions of genes that are deemed to govern the V+C content of Vicia faba. For this purpose we first trained a deep learning model with the gene annotations of seven related species of the Leguminosae family. Applying our model, we predicted putative promoters in a partial genome of Vicia faba that we assembled from genotyping-by-sequencing (GBS) data. Exploiting the synteny between Medicago truncatula and Vicia faba, we identified two rSNPs which are statistically significantly associated with V+C content. In particular, the allele substitutions regarding these rSNPs result in dramatic changes of the binding sites of the transcription factors (TFs) MYB4, MYB61, and SQUA. The knowledge about TFs and their rSNPs may enhance our understanding of the regulatory programs controlling V+C content of Vicia faba and could provide new hypotheses for future breeding programs.

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Section: Sequence Features Used To Predict Promotersmentioning

confidence: 99%

Identification of Regulatory SNPs Associated with Vicine and Convicine Content of Vicia faba Based on Genotyping by Sequencing Data Using Deep Learning

Heinrich

Wutke

Das

et al. 2020

Genes

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“…Our previous work shows generalized topological entropy is a complete form of topological entropy [ 21 , 22 ] and it is defined as: In Eq. 2 , n ω fulfils and k ≤ n ω .…”

Section: Methodsmentioning

confidence: 99%

IIMLP: integrated information-entropy-based method for LncRNA prediction

Xiao

et al. 2021

BMC Bioinformatics

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Background The prediction of long non-coding RNA (lncRNA) has attracted great attention from researchers, as more and more evidence indicate that various complex human diseases are closely related to lncRNAs. In the era of bio-med big data, in addition to the prediction of lncRNAs by biological experimental methods, many computational methods based on machine learning have been proposed to make better use of the sequence resources of lncRNAs. Results We developed the lncRNA prediction method by integrating information-entropy-based features and machine learning algorithms. We calculate generalized topological entropy and generate 6 novel features for lncRNA sequences. By employing these 6 features and other features such as open reading frame, we apply supporting vector machine, XGBoost and random forest algorithms to distinguish human lncRNAs. We compare our method with the one which has more K-mer features and results show that our method has higher area under the curve up to 99.7905%. Conclusions We develop an accurate and efficient method which has novel information entropy features to analyze and classify lncRNAs. Our method is also extendable for research on the other functional elements in DNA sequences.

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“…Shannon entropy plays a significant role in the field of information theory. Recently several studies have shown the importance of entropy in DNA sequences for example, to investigate exons and introns in the DNA sequences [34], to identify DNA sequence diversity between different alleles within one individual [35]. To the best of our knowledge there is no method incorporating entropy-based features.…”

Section: Nfeature Overviewmentioning

confidence: 99%

Nfeature: A platform for computing features of nucleotide sequences

Mathur

Patiyal

Dhall

et al. 2021

Preprint

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In the past few decades, public repositories on nucleotides have increased with exponential rates. This pose a major challenge to researchers to predict the structure and function of nucleotide sequences. In order to annotate function of nucleotide sequences it is important to compute features/attributes for predicting function of these sequences using machine learning techniques. In last two decades, several software/platforms have been developed to elicit a wide range of features for nucleotide sequences. In order to complement the existing methods, here we present a platform named Nfeature developed for computing wide range of features of DNA and RNA sequences. It comprises of three major modules namely Composition, Correlation, and Binary profiles. Composition module allow to compute different type of compositions that includes mono-/di-tri-nucleotide composition, reverse complement composition, pseudo composition. Correlation module allow to compute various type of correlations that includes auto-correlation, cross-correlation, pseudo-correlation. Similarly, binary profile is developed for computing binary profile based on nucleotides, di-nucleotides, di-/tri-nucleotide properties. Nfeature also allow to compute entropy of sequences, repeats in sequences and distribution of nucleotides in sequences. In addition to compute feature in whole sequence, it also allows to compute features from part of sequence like split-composition, N-terminal, C-terminal. In a nutshell, Nfeature amalgamates existing features as well as number of novel features like nucleotide repeat index, distance distribution, entropy, binary profile, and properties. This tool computes a total of 29217 and 14385 features for DNA and RNA sequence, respectively. In order to provide, a highly efficient and user-friendly tool, we have developed a standalone package and web-based platform (https://webs.iiitd.edu.in/raghava/nfeature).

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Integrated entropy-based approach for analyzing exons and introns in DNA sequences

Cited by 15 publications

References 43 publications

Identification of Regulatory SNPs Associated with Vicine and Convicine Content of Vicia faba Based on Genotyping by Sequencing Data Using Deep Learning

Identification of Regulatory SNPs Associated with Vicine and Convicine Content of Vicia faba Based on Genotyping by Sequencing Data Using Deep Learning

IIMLP: integrated information-entropy-based method for LncRNA prediction

Nfeature: A platform for computing features of nucleotide sequences

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