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Silva R, Padovani K, Góes F, Alves R. geneRFinder: gene finding in distinct metagenomic data complexities. BMC Bioinformatics 2021; 22:87. [PMID: 33632132 PMCID: PMC7905635 DOI: 10.1186/s12859-021-03997-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 02/04/2021] [Indexed: 12/01/2022] Open
Abstract
Background Microbes perform a fundamental economic, social, and environmental role in our society. Metagenomics makes it possible to investigate microbes in their natural environments (the complex communities) and their interactions. The way they act is usually estimated by looking at the functions they play in those environments and their responsibility is measured by their genes. The advances of next-generation sequencing technology have facilitated metagenomics research however it also creates a heavy computational burden. Large and complex biological datasets are available as never before. There are many gene predictors available that can aid the gene annotation process though they lack handling appropriately metagenomic data complexities. There is no standard metagenomic benchmark data for gene prediction. Thus, gene predictors may inflate their results by obfuscating low false discovery rates. Results We introduce geneRFinder, an ML-based gene predictor able to outperform state-of-the-art gene prediction tools across this benchmark by using only one pre-trained Random Forest model. Average prediction rates of geneRFinder differed in percentage terms by 54% and 64%, respectively, against Prodigal and FragGeneScan while handling high complexity metagenomes. The specificity rate of geneRFinder had the largest distance against FragGeneScan, 79 percentage points, and 66 more than Prodigal. According to McNemar’s test, all percentual differences between predictors performances are statistically significant for all datasets with a 99% confidence interval. Conclusions We provide geneRFinder, an approach for gene prediction in distinct metagenomic complexities, available at gitlab.com/r.lorenna/generfinder and https://osf.io/w2yd6/, and also we provide a novel, comprehensive benchmark data for gene prediction—which is based on The Critical Assessment of Metagenome Interpretation (CAMI) challenge, and contains labeled data from gene regions—available at https://sourceforge.net/p/generfinder-benchmark.
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Affiliation(s)
- Raíssa Silva
- Vale Institute of Technology, Boaventura da Silva, 955, Belém, BR, 66055-090, Brazil.,PPGCC, Federal University of Pará, Augusto Corrêa, 01, Belém, BR, 66075-110, Brazil
| | - Kleber Padovani
- PPGCC, Federal University of Pará, Augusto Corrêa, 01, Belém, BR, 66075-110, Brazil
| | - Fabiana Góes
- ICMC, University of São Paulo, Trab. São Carlense, 400, São Carlos, BR, 13566-590, Brazil
| | - Ronnie Alves
- Vale Institute of Technology, Boaventura da Silva, 955, Belém, BR, 66055-090, Brazil. .,PPGCC, Federal University of Pará, Augusto Corrêa, 01, Belém, BR, 66075-110, Brazil.
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Xu ZC, Xiao X, Qiu WR, Wang P, Fang XZ. iAI-DSAE: A Computational Method for Adenosine to Inosine Editing Site Prediction. LETT ORG CHEM 2019. [DOI: 10.2174/1570178615666181016112546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
As an important post-transcriptional modification, adenosine-to-inosine RNA editing generally occurs in both coding and noncoding RNA transcripts in which adenosines are converted to inosines. Accordingly, the diversification of the transcriptome can be resulted in by this modification. It is significant to accurately identify adenosine-to-inosine editing sites for further understanding their biological functions. Currently, the adenosine-to-inosine editing sites would be determined by experimental methods, unfortunately, it may be costly and time consuming. Furthermore, there are only a few existing computational prediction models in this field. Therefore, the work in this study is starting to develop other computational methods to address these problems. Given an uncharacterized RNA sequence that contains many adenosine resides, can we identify which one of them can be converted to inosine, and which one cannot? To deal with this problem, a novel predictor called iAI-DSAE is proposed in the current study. In fact, there are two key issues to address: one is ‘what feature extraction methods should be adopted to formulate the given sample sequence?’ The other is ‘what classification algorithms should be used to construct the classification model?’ For the former, a 540-dimensional feature vector is extracted to formulate the sample sequence by dinucleotide-based auto-cross covariance, pseudo dinucleotide composition, and nucleotide density methods. For the latter, we use the present more popular method i.e. deep spare autoencoder to construct the classification model. Generally, ACC and MCC are considered as the two of the most important performance indicators of a predictor. In this study, in comparison with those of predictor PAI, they are up 2.46% and 4.14%, respectively. The two other indicators, Sn and Sp, rise at certain degree also. This indicates that our predictor can be as an important complementary tool to identify adenosine-toinosine RNA editing sites. For the convenience of most experimental scientists, an easy-to-use web-server for identifying adenosine-to-inosine editing sites has been established at: http://www.jci-bioinfo.cn/iAI-DSAE, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. It is important to identify adenosine-to-inosine editing sites in RNA sequences for the intensive study on RNA function and the development of new medicine. In current study, a novel predictor, called iAI-DSAE, was proposed by using three feature extraction methods including dinucleotidebased auto-cross covariance, pseudo dinucleotide composition and nucleotide density. The jackknife test results of the iAI-DSAE predictor based on deep spare auto-encoder model show that our predictor is more stable and reliable. It has not escaped our notice that the methods proposed in the current paper can be used to solve many other problems in genome analysis.
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Affiliation(s)
- Zhao-Chun Xu
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403, China
| | - Xuan Xiao
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403, China
| | - Wang-Ren Qiu
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403, China
| | - Peng Wang
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403, China
| | - Xin-Zhu Fang
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403, China
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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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Notari DL, Molin A, Davanzo V, Picolotto D, Ribeiro HG, Silva SDAE. IntergenicDB: a database for intergenic sequences. Bioinformation 2014; 10:381-3. [PMID: 25097383 PMCID: PMC4110431 DOI: 10.6026/97320630010381] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2014] [Accepted: 05/24/2014] [Indexed: 12/01/2022] Open
Abstract
A whole genome contains not only coding regions, but also non-coding regions. These are located between the end of a given
coding region and the beginning of the following coding region. For this reason, the information about gene regulation process
underlies in intergenic regions. There is no easy way to obtain intergenic regions from current available databases. IntergenicDB
was developed to integrate data of intergenic regions and their gene related information from NCBI databases. The main goal of
INTERGENICDB is to offer friendly database for intergenic sequences of bacterial genomes.
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Affiliation(s)
- Daniel Luis Notari
- Centro de Computação e Tecnologia da Informação, Universidade de Caxias do Sul, Rua Francisco Getúlio Vargas, 1130 - CEP 95070-560 - Caxias do Sul, Rio Grande do Sul, Brasil ; Instituto de Biotecnologia, Universidade de Caxias do Sul, Rua Francisco Getúlio Vargas, 1130 - CEP 95070-560 - Caxias do Sul, Rio Grande do Sul, Brasil
| | - Aurione Molin
- Centro de Computação e Tecnologia da Informação, Universidade de Caxias do Sul, Rua Francisco Getúlio Vargas, 1130 - CEP 95070-560 - Caxias do Sul, Rio Grande do Sul, Brasil
| | - Vanessa Davanzo
- Centro de Computação e Tecnologia da Informação, Universidade de Caxias do Sul, Rua Francisco Getúlio Vargas, 1130 - CEP 95070-560 - Caxias do Sul, Rio Grande do Sul, Brasil
| | - Douglas Picolotto
- Centro de Computação e Tecnologia da Informação, Universidade de Caxias do Sul, Rua Francisco Getúlio Vargas, 1130 - CEP 95070-560 - Caxias do Sul, Rio Grande do Sul, Brasil
| | - Helena Graziottin Ribeiro
- Centro de Computação e Tecnologia da Informação, Universidade de Caxias do Sul, Rua Francisco Getúlio Vargas, 1130 - CEP 95070-560 - Caxias do Sul, Rio Grande do Sul, Brasil
| | - Scheila de Avila E Silva
- Instituto de Biotecnologia, Universidade de Caxias do Sul, Rua Francisco Getúlio Vargas, 1130 - CEP 95070-560 - Caxias do Sul, Rio Grande do Sul, Brasil
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Vinga S. Information theory applications for biological sequence analysis. Brief Bioinform 2014; 15:376-89. [PMID: 24058049 PMCID: PMC7109941 DOI: 10.1093/bib/bbt068] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2013] [Accepted: 08/17/2013] [Indexed: 01/13/2023] Open
Abstract
Information theory (IT) addresses the analysis of communication systems and has been widely applied in molecular biology. In particular, alignment-free sequence analysis and comparison greatly benefited from concepts derived from IT, such as entropy and mutual information. This review covers several aspects of IT applications, ranging from genome global analysis and comparison, including block-entropy estimation and resolution-free metrics based on iterative maps, to local analysis, comprising the classification of motifs, prediction of transcription factor binding sites and sequence characterization based on linguistic complexity and entropic profiles. IT has also been applied to high-level correlations that combine DNA, RNA or protein features with sequence-independent properties, such as gene mapping and phenotype analysis, and has also provided models based on communication systems theory to describe information transmission channels at the cell level and also during evolutionary processes. While not exhaustive, this review attempts to categorize existing methods and to indicate their relation with broader transversal topics such as genomic signatures, data compression and complexity, time series analysis and phylogenetic classification, providing a resource for future developments in this promising area.
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Affiliation(s)
- Susana Vinga
- IDMEC, Instituto Superior Técnico - Universidade de Lisboa (IST-UL), Av. Rovisco Pais, 1049-001 Lisboa, Portugal. Tel.: +351-218419504; Fax: +351-218498097;
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Batista MV, Ferreira TA, Freitas AC, Balbino VQ. An entropy-based approach for the identification of phylogenetically informative genomic regions of Papillomavirus. INFECTION GENETICS AND EVOLUTION 2011; 11:2026-33. [DOI: 10.1016/j.meegid.2011.09.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2011] [Revised: 09/09/2011] [Accepted: 09/14/2011] [Indexed: 11/17/2022]
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Delgado-Soler L, Toral R, Tomás MS, Rubio-Martinez J. RED: A Set of Molecular Descriptors Based on Rényi Entropy. J Chem Inf Model 2009; 49:2457-68. [PMID: 19842710 DOI: 10.1021/ci900275w] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Laura Delgado-Soler
- Departament de Química Física, Universitat de Barcelona (UB) and the Institut de Recerca en Química Teòrica i Computacional (IQTCUB), Martí i Franqués 1, E-08028 Barcelona, Spain, Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC), CSIC-UIB, Campus UIB, 07122-Palma de Mallorca, Spain, and Secció Matemàtiques ETSAB, Universitat Politècnica de Catalunya (UPC), ETS d’Arquitectura, Avinguda Diagonal 649, E-08028 Barcelona, Spain
| | - Raul Toral
- Departament de Química Física, Universitat de Barcelona (UB) and the Institut de Recerca en Química Teòrica i Computacional (IQTCUB), Martí i Franqués 1, E-08028 Barcelona, Spain, Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC), CSIC-UIB, Campus UIB, 07122-Palma de Mallorca, Spain, and Secció Matemàtiques ETSAB, Universitat Politècnica de Catalunya (UPC), ETS d’Arquitectura, Avinguda Diagonal 649, E-08028 Barcelona, Spain
| | - M. Santos Tomás
- Departament de Química Física, Universitat de Barcelona (UB) and the Institut de Recerca en Química Teòrica i Computacional (IQTCUB), Martí i Franqués 1, E-08028 Barcelona, Spain, Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC), CSIC-UIB, Campus UIB, 07122-Palma de Mallorca, Spain, and Secció Matemàtiques ETSAB, Universitat Politècnica de Catalunya (UPC), ETS d’Arquitectura, Avinguda Diagonal 649, E-08028 Barcelona, Spain
| | - Jaime Rubio-Martinez
- Departament de Química Física, Universitat de Barcelona (UB) and the Institut de Recerca en Química Teòrica i Computacional (IQTCUB), Martí i Franqués 1, E-08028 Barcelona, Spain, Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC), CSIC-UIB, Campus UIB, 07122-Palma de Mallorca, Spain, and Secció Matemàtiques ETSAB, Universitat Politècnica de Catalunya (UPC), ETS d’Arquitectura, Avinguda Diagonal 649, E-08028 Barcelona, Spain
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Giancarlo R, Scaturro D, Utro F. Textual data compression in computational biology: a synopsis. Bioinformatics 2009; 25:1575-86. [DOI: 10.1093/bioinformatics/btp117] [Citation(s) in RCA: 63] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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Perera A, Vallverdu M, Claria F, Soria JM, Caminal P. DNA binding site characterization by means of Rényi entropy measures on nucleotide transitions. IEEE Trans Nanobioscience 2008; 7:133-41. [PMID: 18556261 DOI: 10.1109/tnb.2008.2000744] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this work, parametric information-theory measures for the characterization of binding sites in DNA are extended with the use of transitional probabilities on the sequence. We propose the use of parametric uncertainty measures such as Rényi entropies obtained from the transition probabilities for the study of the binding sites, in addition to nucleotide frequency-based Rényi measures. Results are reported in this work comparing transition frequencies (i.e., dinucleotides) and base frequencies for Shannon and parametric Rényi entropies for a number of binding sites found in E. Coli, lambda and T7 organisms. We observe that the information provided by both approaches is not redundant. Furthermore, under the presence of noise in the binding site matrix we observe overall improved robustness of nucleotide transition-based algorithms when compared with nucleotide frequency-based method.
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Affiliation(s)
- A Perera
- Centre for Biomedical Engineering Research, Technical University of Catalonia, Barcelona, Spain.
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Perera A, Vallverdu M, Claria F, Soria JM, Caminal P. DNA binding sites characterization by means of Rényi entropy measures on nucleotide transitions. CONFERENCE PROCEEDINGS : ... ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL CONFERENCE 2008; 2006:5783-6. [PMID: 17946719 DOI: 10.1109/iembs.2006.260482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In this work, parametric information-theory measures for the characterization of binding sites in DNA are extended with the use of transitional probabilities on the sequence. We propose the use of parametric uncertainty measure such as Renyi entropies obtained from the transition probabilities for the study of the binding sites, in addition to nucleotide frequency based Renyi measures. Results are reported in this manuscript comparing transition frequencies (i.e. dinucelotides) and base frequencies for Shannon and parametric Renyi for a number of binding sites found in E. Coli, lambda and T7 organisms. We observe that, for the evaluated datasets, the information provided by both approaches is not redundant, as they evolve differently under increasing Renyi orders.
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Affiliation(s)
- Alexandre Perera
- Centre for Biomed. Eng. Res., Tech. Univ. of Catalonia, Barcelona, Spain.
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Pandey SP, Krishnamachari A. Computational analysis of plant RNA Pol-II promoters. Biosystems 2005; 83:38-50. [PMID: 16236422 DOI: 10.1016/j.biosystems.2005.09.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2005] [Revised: 09/10/2005] [Accepted: 09/10/2005] [Indexed: 11/18/2022]
Abstract
Plant promoters have not yet been thoroughly analyzed in terms of their structural and sequence dependent properties like curvature, periodicity and information content and our present study is an attempt in that direction. Results were compared with E. coli and yeast data to get some insight into the promoter organization. Promoters having the TATA box (TATA(+)) and those lacking the same (TATA(-)) were also analyzed separately. It was found that plant promoters have marked differences for all these properties when compared to E. coli and yeast. Bias for A+T was observed in promoters of all the three groups. Compared to E. coli and yeast, plant promoters showed intermediate values for A+T content as well as curvature. Analysis showed that curvature of core promoters is more pronounced than non-promoters. Information theoretic analysis of plant promoters reveal high information content at certain consensus regions such as -30 (TATA box) and +1 transcription start site (TSS); and have moderate values at other positions as well. This factor was taken into account while developing weight matrices. For certain threshold values, these weight matrices could pick up all true positives, and reduce false positives to a great extent in a test set. A new multi-parameterized prediction strategy has been proposed that uses a combination of sequence composition, curvature and position weight matrices for identification of plant promoters. This strategy was tested and validated with experimentally known promoter sequences. Our study is novel in using in silico approaches to study the sequence dependent properties of plant RNA Pol-II promoters and their prediction, and important as there is no dedicated promoter search tool for plants.
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Affiliation(s)
- S P Pandey
- Bioinformatics Centre, School of Information Technology, Jawaharlal Nehru University, Hall No. 6, Lecture Halls Complex, New Delhi 110067, India
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Wachowiak MP, Smolíková R, Tourassi GD, Elmaghraby AS. Estimation of generalized entropies with sample spacing. Pattern Anal Appl 2005. [DOI: 10.1007/s10044-005-0247-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Vinga S, Almeida JS. Rényi continuous entropy of DNA sequences. J Theor Biol 2004; 231:377-88. [PMID: 15501469 DOI: 10.1016/j.jtbi.2004.06.030] [Citation(s) in RCA: 53] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2004] [Accepted: 06/30/2004] [Indexed: 11/20/2022]
Abstract
Entropy measures of DNA sequences estimate their randomness or, inversely, their repeatability. L-block Shannon discrete entropy accounts for the empirical distribution of all length-L words and has convergence problems for finite sequences. A new entropy measure that extends Shannon's formalism is proposed. Renyi's quadratic entropy calculated with Parzen window density estimation method applied to CGR/USM continuous maps of DNA sequences constitute a novel technique to evaluate sequence global randomness without some of the former method drawbacks. The asymptotic behaviour of this new measure was analytically deduced and the calculation of entropies for several synthetic and experimental biological sequences was performed. The results obtained were compared with the distributions of the null model of randomness obtained by simulation. The biological sequences have shown a different p-value according to the kernel resolution of Parzen's method, which might indicate an unknown level of organization of their patterns. This new technique can be very useful in the study of DNA sequence complexity and provide additional tools for DNA entropy estimation. The main MATLAB applications developed and additional material are available at the webpage . Specialized functions can be obtained from the authors.
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Affiliation(s)
- Susana Vinga
- Biomathematics Group, Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa, R. Qta. Grande 6, 2780-156 Oeiras, Portugal.
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