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Li J, Zhang L, Li H, Ping Y, Xu Q, Wang R, Tan R, Wang Z, Liu B, Wang Y. Integrated entropy-based approach for analyzing exons and introns in DNA sequences. BMC Bioinformatics 2019; 20:283. [PMID: 31182012 PMCID: PMC6557737 DOI: 10.1186/s12859-019-2772-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Numerous essential algorithms and methods, including entropy-based quantitative methods, have been developed to analyze complex DNA sequences since the last decade. Exons and introns are the most notable components of DNA and their identification and prediction are always the focus of state-of-the-art research. RESULTS In this study, we designed an integrated entropy-based analysis approach, which involves modified topological entropy calculation, genomic signal processing (GSP) method and singular value decomposition (SVD), to investigate exons and introns in DNA sequences. We optimized and implemented the topological entropy and the generalized topological entropy to calculate the complexity of DNA sequences, highlighting the characteristics of repetition sequences. By comparing digitalizing entropy values of exons and introns, we observed that they are significantly different. After we converted DNA data to numerical topological entropy value, we applied SVD method to effectively investigate exon and intron regions on a single gene sequence. Additionally, several genes across five species are used for exon predictions. CONCLUSIONS Our approach not only helps to explore the complexity of DNA sequence and its functional elements, but also provides an entropy-based GSP method to analyze exon and intron regions. Our work is feasible across different species and extendable to analyze other components in both coding and noncoding region of DNA sequences.
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Affiliation(s)
- Junyi Li
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055 China
| | - Li Zhang
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055 China
| | - Huinian Li
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055 China
| | - Yuan Ping
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055 China
| | - Qingzhe Xu
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055 China
| | - Rongjie Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001 China
| | - Renjie Tan
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001 China
| | - Zhen Wang
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031 China
| | - Bo Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001 China
| | - Yadong Wang
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055 China
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001 China
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Wu C, Chen J, Liu Y, Hu X. Improved Prediction of Regulatory Element Using Hybrid Abelian Complexity Features with DNA Sequences. Int J Mol Sci 2019; 20:ijms20071704. [PMID: 30959806 PMCID: PMC6480087 DOI: 10.3390/ijms20071704] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 04/01/2019] [Accepted: 04/02/2019] [Indexed: 12/14/2022] Open
Abstract
Deciphering the code of cis-regulatory element (CRE) is one of the core issues of current biology. As an important category of CRE, enhancers play crucial roles in gene transcriptional regulations in a distant manner. Further, the disruption of an enhancer can cause abnormal transcription and, thus, trigger human diseases, which means that its accurate identification is currently of broad interest. Here, we introduce an innovative concept, i.e., abelian complexity function (ACF), which is a more complex extension of the classic subword complexity function, for a new coding of DNA sequences. After feature selection by an upper bound estimation and integration with DNA composition features, we developed an enhancer prediction model with hybrid abelian complexity features (HACF). Compared with existing methods, HACF shows consistently superior performance on three sources of enhancer datasets. We tested the generalization ability of HACF by scanning human chromosome 22 to validate previously reported super-enhancers. Meanwhile, we identified novel candidate enhancers which have supports from enhancer-related ENCODE ChIP-seq signals. In summary, HACF improves current enhancer prediction and may be beneficial for further prioritization of functional noncoding variants.
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Affiliation(s)
- Chengchao Wu
- College of Informatics, Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan 430070, China.
| | - Jin Chen
- College of Science, Huazhong Agricultural University, Wuhan 430070, China.
| | - Yunxia Liu
- College of Informatics, Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan 430070, China.
| | - Xuehai Hu
- College of Informatics, Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan 430070, China.
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Ferrari A. Modeling Information Content Via Dirichlet-Multinomial Regression Analysis. MULTIVARIATE BEHAVIORAL RESEARCH 2017; 52:259-270. [PMID: 28207283 DOI: 10.1080/00273171.2017.1279957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Shannon entropy is being increasingly used in biomedical research as an index of complexity and information content in sequences of symbols, e.g. languages, amino acid sequences, DNA methylation patterns and animal vocalizations. Yet, distributional properties of information entropy as a random variable have seldom been the object of study, leading to researchers mainly using linear models or simulation-based analytical approach to assess differences in information content, when entropy is measured repeatedly in different experimental conditions. Here a method to perform inference on entropy in such conditions is proposed. Building on results coming from studies in the field of Bayesian entropy estimation, a symmetric Dirichlet-multinomial regression model, able to deal efficiently with the issue of mean entropy estimation, is formulated. Through a simulation study the model is shown to outperform linear modeling in a vast range of scenarios and to have promising statistical properties. As a practical example, the method is applied to a data set coming from a real experiment on animal communication.
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Affiliation(s)
- Alberto Ferrari
- a Department of Brain and Behavioural Sciences , University of Pavia
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Wu C, Yao S, Li X, Chen C, Hu X. Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in Human. Int J Mol Sci 2017; 18:E420. [PMID: 28212312 PMCID: PMC5343954 DOI: 10.3390/ijms18020420] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Revised: 02/03/2017] [Accepted: 02/08/2017] [Indexed: 02/02/2023] Open
Abstract
DNA methylation plays a significant role in transcriptional regulation by repressing activity. Change of the DNA methylation level is an important factor affecting the expression of target genes and downstream phenotypes. Because current experimental technologies can only assay a small proportion of CpG sites in the human genome, it is urgent to develop reliable computational models for predicting genome-wide DNA methylation. Here, we proposed a novel algorithm that accurately extracted sequence complexity features (seven features) and developed a support-vector-machine-based prediction model with integration of the reported DNA composition features (trinucleotide frequency and GC content, 65 features) by utilizing the methylation profiles of embryonic stem cells in human. The prediction results from 22 human chromosomes with size-varied windows showed that the 600-bp window achieved the best average accuracy of 94.7%. Moreover, comparisons with two existing methods further showed the superiority of our model, and cross-species predictions on mouse data also demonstrated that our model has certain generalization ability. Finally, a statistical test of the experimental data and the predicted data on functional regions annotated by ChromHMM found that six out of 10 regions were consistent, which implies reliable prediction of unassayed CpG sites. Accordingly, we believe that our novel model will be useful and reliable in predicting DNA methylation.
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Affiliation(s)
- Chengchao Wu
- College of Informatics, Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan 430070, China.
| | - Shixin Yao
- College of Science, Huazhong Agricultural University, Wuhan 430070, China.
| | - Xinghao Li
- College of Science, Huazhong Agricultural University, Wuhan 430070, China.
| | - Chujia Chen
- College of Informatics, Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan 430070, China.
| | - Xuehai Hu
- College of Informatics, Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan 430070, China.
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Compatibility and Conjugacy on Partial Arrays. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2016; 2016:5010316. [PMID: 27774111 PMCID: PMC5059777 DOI: 10.1155/2016/5010316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/27/2016] [Revised: 07/28/2016] [Accepted: 08/03/2016] [Indexed: 11/30/2022]
Abstract
Research in combinatorics on words goes back a century. Berstel and Boasson introduced the partial words in the context of gene comparison. Alignment of two genes can be viewed as a construction of two partial words that are said to be compatible. In this paper, we examine to which extent the fundamental properties of partial words such as compatbility and conjugacy remain true for partial arrays. This paper studies a relaxation of the compatibility relation called k-compability. It also studies k-conjugacy of partial arrays.
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Thomas D, Finan C, Newport MJ, Jones S. DNA entropy reveals a significant difference in complexity between housekeeping and tissue specific gene promoters. Comput Biol Chem 2015; 58:19-24. [PMID: 25988219 DOI: 10.1016/j.compbiolchem.2015.05.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Revised: 05/01/2015] [Accepted: 05/01/2015] [Indexed: 10/23/2022]
Abstract
BACKGROUND The complexity of DNA can be quantified using estimates of entropy. Variation in DNA complexity is expected between the promoters of genes with different transcriptional mechanisms; namely housekeeping (HK) and tissue specific (TS). The former are transcribed constitutively to maintain general cellular functions, and the latter are transcribed in restricted tissue and cells types for specific molecular events. It is known that promoter features in the human genome are related to tissue specificity, but this has been difficult to quantify on a genomic scale. If entropy effectively quantifies DNA complexity, calculating the entropies of HK and TS gene promoters as profiles may reveal significant differences. RESULTS Entropy profiles were calculated for a total dataset of 12,003 human gene promoters and for 501 housekeeping (HK) and 587 tissue specific (TS) human gene promoters. The mean profiles show the TS promoters have a significantly lower entropy (p<2.2e-16) than HK gene promoters. The entropy distributions for the 3 datasets show that promoter entropies could be used to identify novel HK genes. CONCLUSION Functional features comprise DNA sequence patterns that are non-random and hence they have lower entropies. The lower entropy of TS gene promoters can be explained by a higher density of positive and negative regulatory elements, required for genes with complex spatial and temporary expression.
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Affiliation(s)
- David Thomas
- Brighton and Sussex Medical School, University of Sussex, Brighton BN1 9PX, UK
| | - Chris Finan
- Brighton and Sussex Medical School, University of Sussex, Brighton BN1 9PX, UK
| | - Melanie J Newport
- Brighton and Sussex Medical School, University of Sussex, Brighton BN1 9PX, UK
| | - Susan Jones
- The James Hutton Institute, Invergowrie, Dundee DD2 5DA, UK
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Jin S, Tan R, Jiang Q, Xu L, Peng J, Wang Y, Wang Y. A generalized topological entropy for analyzing the complexity of DNA sequences. PLoS One 2014; 9:e88519. [PMID: 24533097 PMCID: PMC3922877 DOI: 10.1371/journal.pone.0088519] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2013] [Accepted: 01/06/2014] [Indexed: 11/19/2022] Open
Abstract
Topological entropy is one of the most difficult entropies to be used to analyze the DNA sequences, due to the finite sample and high-dimensionality problems. In order to overcome these problems, a generalized topological entropy is introduced. The relationship between the topological entropy and the generalized topological entropy is compared, which shows the topological entropy is a special case of the generalized entropy. As an application the generalized topological entropy in introns, exons and promoter regions was computed, respectively. The results indicate that the entropy of introns is higher than that of exons, and the entropy of the exons is higher than that of the promoter regions for each chromosome, which suggest that DNA sequence of the promoter regions is more regular than the exons and introns.
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Affiliation(s)
- Shuilin Jin
- Department of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Renjie Tan
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Qinghua Jiang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Li Xu
- College of Computer Science and Technology, Harbin Engineering University, Harbin, Heilongjiang, China
| | - Jiajie Peng
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Yong Wang
- Department of Mathematics, Harbin Institute of Technology, Harbin, Heilongjiang, China
- * E-mail: (Yadong Wang) (YW); (Yong Wang) (YW)
| | - Yadong Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China
- * E-mail: (Yadong Wang) (YW); (Yong Wang) (YW)
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Abstract
MOTIVATION Topological entropy has been one of the most difficult to implement of all the entropy-theoretic notions. This is primarily due to finite sample effects and high-dimensionality problems. In particular, topological entropy has been implemented in previous literature to conclude that entropy of exons is higher than of introns, thus implying that exons are more 'random' than introns. RESULTS We define a new approximation to topological entropy free from the aforementioned difficulties. We compute its expected value and apply this definition to the intron and exon regions of the human genome to observe that as expected, the entropy of introns are significantly higher than that of exons. We also find that introns are less random than expected: their entropy is lower than the computed expected value. We also observe the perplexing phenomena that introns on chromosome Y have atypically low and bimodal entropy, possibly corresponding to random sequences (high entropy) and sequences that posses hidden structure or function (low entropy). AVAILABILITY A Mathematica implementation is available at http://www.math.psu.edu/koslicki/entropy.nb CONTACT koslicki@math.psu.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- David Koslicki
- Department of Mathematics, Pennsylvania State University, State College, PA 16801, USA.
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Abstract
The quantitative underpinning of the information content of biosequences represents an elusive goal and yet also an obvious prerequisite to the quantitative modeling and study of biological function and evolution. Several past studies have addressed the question of what distinguishes biosequences from random strings, the latter being clearly unpalatable to the living cell. Such studies typically analyze the organization of biosequences in terms of their constituent characters or substrings and have, in particular, consistently exposed a tenacious lack of compressibility on behalf of biosequences. This article attempts, perhaps for the first time, an assessement of the structure and randomness of polypeptides in terms on newly introduced parameters that relate to the vocabulary of their (suitably constrained) subsequences rather than their substrings. It is shown that such parameters grasp structural/functional information, and are related to each other under a specific set of rules that span biochemically diverse polypeptides. Measures on subsequences separate few amino acid strings from their random permutations, but show that the random permutations of most polypeptides amass along specific linear loci.
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Affiliation(s)
- Alberto Apostolico
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30318, USA.
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Apostolico A. Maximal Words in Sequence Comparisons Based on Subword Composition. ALGORITHMS AND APPLICATIONS 2010. [DOI: 10.1007/978-3-642-12476-1_2] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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