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The correct annotation of non-coding RNAs, especially long non-coding RNAs (lncRNAs), is still an important critial challenge in genome analyses. One crucial issue in lncRNA transcript annotation is the transcriptome resource that supports lncRNA loci. Long-read technologies now bring the potential to improve the quality of transcriptome annotation. Consequently, long non-coding RNAs (lncRNA) are probably the most benefited class of transcripts that would have improved annotation using this novel technology. However, there is a gap regarding benchmarking studies that highlighted if the direct use of lncRNA predictors in long-reads makes more precise identification of these transcripts. Considering that these lncRNA tools were not trained with these reads, we want to address: how is the performance of these tools? Are they also able to efficiently identify lncRNAs? We could provide evidence of where and how to make potential better approaches for the lncRNA annotation by understanding these issues. Keywords: Non-coding RNAs, high-throughput sequencing technologies, coding, methods, benchmarking, tools, NGS, transcripts

Mature microRNAs (miRNAs) are short RNA sequences about 18-24 nucleotide long, which provide the recognition key within RISC for the posttranscriptional regulation of target RNAs. Considering the canonical pathway, mature miRNAs are produced via a multistep process. Their transcription (pri-miRNAs) and first processing step via the microprocessor complex (pre-miRNAs) occur in the nucleus. Then they are exported into the cytosol, processed again by Dicer (dsRNA) and finally a single strand (mature miRNA) is incorporated into RISC (miRISC). The sequence of the incorporated miRNA provides the function of RNA target recognition via hybridization. Following binding of the target, the mRNA is either degraded or translation is inhibited, which ultimately leads to less protein production. Conversely, it has been shown that binding within the 5' UTR of the mRNA can lead to an increase in protein product. Regulation of homeostasis is very important for a cell; therefore, all steps in the miRNA-based regulation pathway, from transcription to the incorporation of the mature miRNA into RISC, are under tight control. While much research effort has been exerted in this area, the knowledgebase is not sufficient for accurately modelling miRNA regulation computationally. The computational prediction of miRNAs is, however, necessary because it is not feasible to investigate all possible pairs of a miRNA and its target, let alone miRNAs and their targets. We here point out open challenges important for computational modelling or for our general understanding of miRNA-based regulation and show how their investigation is beneficial. It is our hope that this collection of challenges will lead to their resolution in the near future.

In this era of big data, sets of methodologies and strategies are designed to extract knowledge from huge volumes of data. However, the cost of where and how to get this information accurately and quickly is extremely important, given the diversity of genomes and the different ways of representing that information. Among the huge set of information and relationships that the genome carries, there are sequences called miRNAs (microRNAs). These sequences were described in the 1990s and are mainly involved in mechanisms of regulation and gene expression. Having this in mind, this chapter focuses on exploring the available literature and providing useful and practical guidance on the miRNA database and tools topic. For that, we organized and present this text in two ways: (a) the update reviews and articles, which best summarize and discuss the theme; and (b) our update investigation on miRNA literature and portals about databases and tools. Finally, we present the main challenge and a possible solution to improve resources and tools.

In this era of big data, sets of methodologies and strategies are designed to extract knowledge from huge volumes of data. However, the cost of where and how to get this information accurately and quickly is extremely important, given the diversity of genomes and the different ways of representing that information. Among the huge set of information and relationships that the genome carries, there are sequences called miRNAs (microRNAs). These sequences were described in the 1990s and are mainly involved in mechanisms of regulation and gene expression. Having this in mind, this chapter focuses on exploring the available literature and providing useful and practical guidance on the miRNA database and tools topic. For that, we organized and present this text in two ways: (a) the update reviews and articles, which best summarize and discuss the theme; and (b) our update investigation on miRNA literature and portals about databases and tools. Finally, we present the main challenge and a possible solution to improve resources and tools.

This chapter provides two main contributions: (1) a description of computational tools and databases used to identify and analyze transposable elements (TEs) and circRNAs in plants; and (2) data analysis on public TE and circRNA data. Our goal is to highlight the primary information available in the literature on circular noncoding RNAs and transposable elements in plants. The exploratory analysis performed on publicly available circRNA and TEs data help discuss four sequence features. Finally, we investigate the association on circRNAs:TE in plants in the model organism Arabidopsis thaliana.

In the age of big data, obtaining precise information about the research topic of interesting is extremely important. Keeping this in mind, this chapter focuses on providing a practical knowledge guide about computational tools and databases of transposable elements (TE) in plants. For that, we organize and present this text in three sections: (1) a discussion about tools and databases on this theme; (2) hands-on of how to use a few of them; (3) an exploratory data analysis on public TE data. Finally, we are going deep to present the main challenges and possible solutions to improve resources and tools.

In the age of big data, obtaining precise information about the research topic of interesting is extremely important. Keeping this in mind, this chapter focuses on providing a practical knowledge guide about computational tools and databases of transposable elements (TE) in plants. For that, we organize and present this text in three sections: (1) a discussion about tools and databases on this theme; (2) hands-on of how to use a few of them; (3) an exploratory data analysis on public TE data. Finally, we are going deep to present the main challenges and possible solutions to improve resources and tools.

Made available in DSpace on 2022-04-28T19:50:06Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-01-01 Advances in genomic sequencing have recently offered vast opportunities for biological exploration, unraveling the evolution and improving our understanding of Earth biodiversity. Due to distinct plant species characteristics in terms of genome size, ploidy and heterozygosity, transposable elements (TEs) are common characteristics of many genomes. TEs are ubiquitous and dispersed repetitive DNA sequences that frequently impact the evolution and composition of the genome, mainly due to their redundancy and rearrangements. For this study, we provided an atlas of TE data by employing an easy-to-use portal ( APTE website ). To our knowledge, this is the most extensive and standardized analysis of TEs in plant genomes. We evaluated 67 plant genomes assembled at chromosome scale, recovering a total of 49,802,023 TE records, representing a total of 47,992,091,043 (~47,62%) base pairs (bp) of the total genomic space. We observed that new types of TEs were identified and annotated compared to other data repositories. By establishing a standardized catalog of TE annotation on 67 genomes, new hypotheses, exploration of TE data and their influences on the genomes may allow a better understanding of their function and processes. All original code and an example of how we developed the TE annotation strategy is available on GitHub ( Extended data). Department of Computer Science; Bioinformatics and Pattern Recognition Group, Graduation Program in Bioinformatics, Federal University of Technology - Paraná (UTFPR), Cornélio Procópio, Paraná, 86300000, Brazil Departament of Agricultural and Environmental Biotechnology School of Agricultural and Veterinary Sciences São Paulo State University (UNESP) Institut de Recherche pour le Développement IRD University of Montpellier Department of Electronics and Automatization Universidad Autónoma de Manizales Group of Genomics and Transcriptomes in Plants Institute of Biosciences São Paulo State University (UNESP) Departament of Agricultural and Environmental Biotechnology School of Agricultural and Veterinary Sciences São Paulo State University (UNESP) Group of Genomics and Transcriptomes in Plants Institute of Biosciences São Paulo State University (UNESP)

This chapter provides two main contributions: (1) a description of computational tools and databases used to identify and analyze transposable elements (TEs) and circRNAs in plants; and (2) data analysis on public TE and circRNA data. Our goal is to highlight the primary information available in the literature on circular noncoding RNAs and transposable elements in plants. The exploratory analysis performed on publicly available circRNA and TEs data help discuss four sequence features. Finally, we investigate the association on circRNAs:TE in plants in the model organism Arabidopsis thaliana.

This article has as its main objective the evaluation of the differences between long non-coding RNAs of plants and humans. Long non-coding RNAs are also known as lncRNAs. The lncRNAS belong to the class of RNAs that do not encode proteins and are related to several biological functions, such as chromatin modifications, post-transcriptional regulation and mainly in the different development processes of diseases such as cancer. In this work, we want to verify the existence of differences in lncRNAs in plants and humans using state-of-the-art approaches to identify lncRNAs. The main reason for the study is that there are differences between the miNAs (small ncRNAs) of plants and humans, whether in biological or computational characteristics, for lncRNAs it is still an open question. To answer this question, this paper proposes to show the results of two ncRNAS prediction tools, trained with humans, and which are widely used for lncRNA prediction: CPC2 and CPAT. We will also show results from tools used to predict lncRNAS in plants, which are trained with plant data: the RNAplonc, the PlncPRO tool that contains two versions, one for monocot and one for dicot and the LGC tool that was trained with plants and humans. The results of tools trained with human data will also be displayed: PLEK, CPPRED and PredLnc-GFStack. These eight tools were applied in two sets of tests, one composed of eight species of plants (Amborella trichopoda, Brachypodium distachyon, Citrus sinensis, Manihot esculenta, Ricinus communis, Solanum tuberosum, Sorghum bicolor, Zea mays) and the other composed of human lncRNAS.

As consequence of the various genomic sequencing projects, an increasing volume of biological sequence data is being produced. Although machine learning algorithms have been successfully applied to a large number of genomic sequence-related problems, the results are largely affected by the type and number of features extracted. This effect has motivated new algorithms and pipeline proposals, mainly involving feature extraction problems, in which extracting significant discriminatory information from a biological set is challenging. Considering this, our work proposes a new study of feature extraction approaches based on mathematical features (numerical mapping with Fourier, entropy and complex networks). As a case study, we analyze long non-coding RNA sequences. Moreover, we separated this work into three studies. First, we assessed our proposal with the most addressed problem in our review, e.g. lncRNA and mRNA; second, we also validate the mathematical features in different classification problems, to predict the class of lncRNA, e.g. circular RNAs sequences; third, we analyze its robustness in scenarios with imbalanced data. The experimental results demonstrated three main contributions: first, an in-depth study of several mathematical features; second, a new feature extraction pipeline; and third, its high performance and robustness for distinct RNA sequence classification. Availability:https://github.com/Bonidia/FeatureExtraction_BiologicalSequences

Made available in DSpace on 2021-06-25T11:23:13Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-09-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Coffea canephoragrains are highly traded commodities worldwide. Non-coding RNAs (ncRNAs) are transcriptional products involved in genome regulation, environmental responses, and plant development. There is not an extensive genome-wide analysis that uncovers the ncRNA portion of theC. canephoragenome. This study aimed to provide a curated characterization of six ncRNA classes in theCoffea canephoragenome. For this purpose, we employed a combination of similarity-based and structural-based computational approaches with stringent curation. Candidate ncRNA loci had expression evidence analyzed using sRNA-seq libraries. We identified 7455 ncRNA loci (6976 with transcriptional evidence) in theC. canephoragenome. This comprised of total 115 snRNAs, 1031 snoRNAs, 92 miRNA precursors, 602 tRNAs, 72 rRNAs, and 5064 lncRNAs. For miRNAs, we identified 159 putative high-confidence targets. This study was the most extensive genomic catalog of curated ncRNAs in the Coffea genus. This data might help elaborating more robust hypotheses in future comparative genomic studies as well as gene regulation and genome dynamics, helping to understand the molecular basis of domestication, environmental adaptation, resistance to pests and diseases, and coffee productivity. Univ Tecnol Fed Parana, Dept Comp Sci, BR-86300000 Cornelio Procopio, Brazil Sao Paulo State Univ, Grp Genom & Transcriptomes Plants, BR-13506900 Rio Claro, SP, Brazil Univ Montpellier, Inst Rech Dev IRD, F-34394 Montpellier, France Sao Paulo State Univ, Grp Genom & Transcriptomes Plants, BR-13506900 Rio Claro, SP, Brazil CAPES: 001

The number of available biological sequences has increased significantly in recent years due to various genomic sequencing projects, creating a huge volume of data. Consequently, new computational methods are needed to analyze and extract information from these sequences. Machine learning methods have shown broad applicability in computational biology and bioinformatics. The utilization of machine learning methods has helped to extract relevant information from various biological datasets. However, there are still several obstacles that motivate new algorithms and pipeline proposals, mainly involving feature extraction problems, in which extracting significant discriminatory information from a biological set is challenging. Considering this, our work proposes to study and analyze a feature extraction pipeline based on mathematical models (Numerical Mapping, Fourier, Entropy, and Complex Networks). As a case study, we analyze Long Non-Coding RNA sequences. Moreover, we divided this work into two studies, e.g., (I) we assessed our proposal with the most addressed problem in our review, e.g., lncRNA vs. mRNA; (II) we tested its generalization on different classification problems, e.g., circRNA vs. lncRNA. The experimental results demonstrated three main contributions: (1) An in-depth study of several mathematical models; (2) a new feature extraction pipeline and (3) its generalization and robustness for distinct biological sequence classification.

Made available in DSpace on 2020-12-10T20:02:24Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-06-03 Brazilian National Science Fund (Conselho Nacional de Desenvolvimento Cientifico and Tecnologico) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) BackgroundMost of our understanding on the social behavior and genomics of bees and other social insects is centered on the Western honey bee, Apis mellifera. The genus Apis, however, is a highly derived branch comprising less than a dozen species, four of which genomically characterized. In contrast, for the equally highly eusocial, yet taxonomically and biologically more diverse Meliponini, a full genome sequence was so far available for a single Melipona species only. We present here the genome sequence of Frieseomelitta varia, a stingless bee that has, as a peculiarity, a completely sterile worker caste.ResultsThe assembly of 243,974,526 high quality Illumina reads resulted in a predicted assembled genome size of 275Mb composed of 2173 scaffolds. A BUSCO analysis for the 10,526 predicted genes showed that these represent 96.6% of the expected hymenopteran orthologs. We also predicted 169,371 repetitive genomic components, 2083 putative transposable elements, and 1946 genes for non-coding RNAs, largely long non-coding RNAs. The mitochondrial genome comprises 15,144bp, encoding 13 proteins, 22 tRNAs and 2 rRNAs. We observed considerable rearrangement in the mitochondrial gene order compared to other bees. For an in-depth analysis of genes related to social biology, we manually checked the annotations for 533 automatically predicted gene models, including 127 genes related to reproductive processes, 104 to development, and 174 immunity-related genes. We also performed specific searches for genes containing transcription factor domains and genes related to neurogenesis and chemosensory communication.ConclusionsThe total genome size for F. varia is similar to the sequenced genomes of other bees. Using specific prediction methods, we identified a large number of repetitive genome components and long non-coding RNAs, which could provide the molecular basis for gene regulatory plasticity, including worker reproduction. The remarkable reshuffling in gene order in the mitochondrial genome suggests that stingless bees may be a hotspot for mtDNA evolution. Hence, while being just the second stingless bee genome sequenced, we expect that subsequent targeting of a selected set of species from this diverse clade of highly eusocial bees will reveal relevant evolutionary signals and trends related to eusociality in these important pollinators. Univ Sao Paulo, Fac Med Ribeirao Preto, Dept Genet, Ribeirao Preto, SP, Brazil Univ Fed Alfenas, Inst Ciencias Biomed, Dept Biol Celular & Desenvolvimento, Alfenas, MG, Brazil Univ Fed Vales Jequitinhonha & Mucuri, Fac Ciencias Biol & Saude, Dept Ciencias Biol, Diamantina, MG, Brazil Univ Fed Sao Carlos, Ctr Ciencias Biol & Saude, Dept Genet & Evolucao, Sao Carlos, SP, Brazil Univ Tecnol Fed Parana, Cornelio Procopio, PR, Brazil Univ Sao Paulo, Fac Med Ribeirao Preto, Dept Biol Celular & Mol & Bioagentes Patogen, Av Bandeirantes 3900, BR-14049900 Ribeirao Preto, SP, Brazil Univ, Estadual Paulista Julio de Mesquita Filho, Fac Ciencias Agr & Vet, Dept Tecnol, Jaboticabal, SP, Brazil Univ Sao Paulo, Fac Filosofia Ciencias & Letras Ribeirao Preto, Dept Biol, Ribeirao Preto, SP, Brazil Univ, Estadual Paulista Julio de Mesquita Filho, Fac Ciencias Agr & Vet, Dept Tecnol, Jaboticabal, SP, Brazil Brazilian National Science Fund (Conselho Nacional de Desenvolvimento Cientifico and Tecnologico): 454103/2014-0 FAPESP: 2015/06657-0 CAPES: 001

Made available in DSpace on 2021-06-25T10:26:04Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-01-01 Machine learning algorithms have been applied to numerous transcript datasets to identify Long non-coding RNAs (lncRNAs). Nevertheless, before these algorithms are applied to RNA data, features must be extracted from the original sequences. As many of these features can be redundant or irrelevant, the predictive performance of the algorithms can be improved by performing feature selection. However, the most current approaches usually select features independently, ignoring possible relations. In this paper, we propose a new model, which identifies the best subsets, removing unnecessary, irrelevant, and redundant predictive features, taking the importance of their co-occurrence into account. The proposed model is based on decomposing solutions and is called k-rounds of decomposition features. In this model, the least relevant features are suppressed according to their contribution to a classification task. To evaluate our proposal, we extract from 5 plant species datasets, a set of features based on sequence structures, using GC content, k-mer (1-6), sequence length, and Open Reading Frame. Next, we apply 5 metaheuristics approaches (Genetic Algorithm, (μ +λ) Evolutionary Algorithm, Artificial Bee Colony, Ant Colony Optimization, and Particle Swarm Optimization) to select the best feature subsets. The main contribution of this work was to include in each metaheuristic a decomposition model that uses round and voting scheme. To investigate its relevance, we select the REPTree classifier to assess the predictive capacity of each subset of features selected in 8 plant species.We identified that the inclusion of the proposed decomposition model significantly reduces the dimensions of the datasets and improves predictive performance, regardless of the metaheuristic. Furthermore, the resulting pipeline has been compared with five approaches in the literature, for lncRNA, when it also showed superior predictive performance. Finally, this study generated a new pipeline to find a minimum number of features in lncRNAs and biological sequences. Department of Computer Science Bioinformatics Graduate Program Federal University of Technology-Paraná (UTFPR) Institute of Mathematics and Computer Sciences University of São Paulo (USP) Universidade Nove de Julho (UNINOVE) Department of Botany Institute of Biosciences São Paulo State University (UNESP) Department of Botany Institute of Biosciences São Paulo State University (UNESP)

Due to a large number of features in bioinformatics data, and its effect on predictive performance, there is an increasing need for tools able to select the most relevant features. For Long non-coding RNAs (lncRNAs) data, key regulators of several biological processes and diseases, many researchers have reported the harmful influence of the high amount of input features. Although there are many feature selection techniques, most of them select the attributes independently, not considering how they related to each other. In this paper, we propose a metaheuristic approach for feature selection of Plants lncRNAs that take this relation into account. For such, we investigate the use of two widely studied metaheuristic algorithms, such as (µ + λ)EA and Ant Colony Optimization. Thus, we build a training and test set of transcript data from 13 plant species (Arabidopsis, Cucumis, Glycine, Populus, Oryza, Amborella, Brachypodium, Citrus, Manihot, Ricinus, Solanum, Sorghum, and Zea), divided into lncRNA samples and protein-coding genes (mRNA). The proposed approach was compared with known techniques for classification of lncRNAs data (RNAplonc, CPC, CPC2, CNCI, PLEK). The experimental and statistical results demonstrated the metaheuristics efficiency for dimensionality reduction in the feature selection problem of lncRNAs, providing competitive classification performance.

MotivationMirtrons are originated from short introns with atypical cleavage from the miRNA canonical pathway by using the splicing mechanism. Several studies describe mirtrons in chordates, invertebrates and plants but in the current literature there is no repository that centralizes and organizes these public and available data. To fill this gap, we created the first knowledge database dedicated to mirtron, called mirtronDB, available at http://mirtrondb.cp.utfpr.edu.br/. MirtronDB has a total of 1,407 mirtron precursors and 2,426 mirtron mature sequences in 18 species.ResultsThrough a user-friendly interface, users can browse and search mirtrons by organism, organism group, type and name. MirtronDB is a specialized resource to explore mirtrons and their regulations, providing free, user-friendly access to knowledge on mirtron data.AvailabilityMirtronDB is available at http://mirtrondb.cp.utfpr.edu.br/.Contactpaschoal@utfpr.edu.brSupplementary informationSupplementary data are available.

Made available in DSpace on 2018-12-11T16:42:09Z (GMT). No. of bitstreams: 0 Previous issue date: 2016-05-01 Transposable elements (TEs) comprise a major fraction of many plant genomes and are known to drive their organization and evolution. Several studies show that these repetitive elements have a prominent role in shaping noncoding regions of the genome such as microRNA (miRNA) loci, which are components of post-transcriptional regulation mechanisms. Although some studies have reported initial formation of miRNA loci from TE sequences, especially in model plants, the approaches that were used did not employ systems that would allow results to be delivered by a user-friendly database. In this study, we identified 152 precursor miRNAs overlapping TEs in 10 plant species. PlanTE-MIR DB was designed to assemble this data and deliver it to the scientific community interested in miRNA origin, evolution, and regulation pathways. Users can browse the database through a web interface and search for entries using various parameters. This resource is cross-referenced with repetitive element (Repbase Update) and miRNA (miRBase) repositories, where sequences can be checked for further analysis. All data in PlanTE-MIR DB are publicly available for download in several file formats to facilitate their understanding and use. The database is hosted at http://bioinfo-tool.cp.utfpr.edu.br/plantemirdb/. Graduation Program in Genetics and Molecular Biology Universidade Estadual de Londrina Bioinformatics Laboratory Universidade Tecnológica Federal do Paraná UTFPR Department of Botany Instituto de Biociências Universidade Estadual Paulista UNESP Department of Botany Instituto de Biociências Universidade Estadual Paulista UNESP

One of the most important resources for researchers of noncoding RNAs is the information available in public databases spread over the internet. However, the effective exploration of this data can represent a daunting task, given the large amount of databases available and the variety of stored data. This chapter describes a classification of databases based on information source, type of RNA, source organisms, data formats, and the mechanisms for information retrieval, detailing the relevance of each of these classifications and its usability by researchers. This classification is used to update a 2012 review, indexing now more than 229 public databases. This review will include an assessment of the new trends for ncRNA research based on the information that is being offered by the databases. Additionally, we will expand the previous analysis focusing on the usability and application of these databases in pathogen and disease research. Finally, this chapter will analyze how currently available database schemas can help the development of new and improved web resources.

One of the most important resources for researchers of noncoding RNAs is the information available in public databases spread over the internet. However, the effective exploration of this data can represent a daunting task, given the large amount of databases available and the variety of stored data. This chapter describes a classification of databases based on information source, type of RNA, source organisms, data formats, and the mechanisms for information retrieval, detailing the relevance of each of these classifications and its usability by researchers. This classification is used to update a 2012 review, indexing now more than 229 public databases. This review will include an assessment of the new trends for ncRNA research based on the information that is being offered by the databases. Additionally, we will expand the previous analysis focusing on the usability and application of these databases in pathogen and disease research. Finally, this chapter will analyze how currently available database schemas can help the development of new and improved web resources.

Made available in DSpace on 2019-10-06T16:00:29Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-01-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) In this work we performed several in silico analyses to describe the relevant structural aspects of an enzyme N-Carbamoyl-D-amino acid amidohydrolase (D-NCAase) encoded on the genome of the Brazilian strain CPAC 15 (=SEMIA 5079) of Bradyrhizobium japonicum, a nonpathogenic species belonging to the order Rhizobiales. D-NCAase has wide applications particularly in the pharmaceutical industry, since it catalyzes the production of D-amino acids such as D-p-hydroxyphenylglycine (D-HPG), an intermediate in the synthesis of β-lactam antibiotics. We applied a homology modelling approach and 50 ns of molecular dynamics simulations to predict the structure and the intersubunit interactions of this novel D-NCAase. Also, in order to evaluate the substrate binding site, the model was subjected to 50 ns of molecular dynamics simulations in the presence of N-Carbamoyl-d-p-hydroxyphenylglycine (Cp-HPG) (a D-NCAase canonical substrate) and water-protein/water-substrate interactions analyses were performed. Overall, the structural analysis and the molecular dynamics simulations suggest that D-NCAase of B. japonicum CPAC-15 has a homodimeric structure in solution. Here, we also examined the substrate specificity of the catalytic site of our model and the interactions with water molecules into the active binding site were comprehensively discussed. Also, these simulations showed that the amino acids Lys123, His125, Pro127, Cys172, Asp174 and Arg176 are responsible for recognition of ligand in the active binding site through several chemical associations, such as hydrogen bonds and hydrophobic interactions. Our results show a favourable environment for a reaction of hydrolysis that transforms N-Carbamoyl-d-p-hydroxyphenylglycine (Cp-HPG) into the active compound D-p-hydroxyphenylglycine (D-HPG). This work envisage the use of D-NCAase from the Brazilian Bradyrhizobium japonicum strain CPAC-15 (=SEMIA 5079) for the industrial production of D-HPG, an important intermediate for semi-synthesis of β-lactam antibiotics such as penicillins, cephalosporins and amoxicillin. Laboratório Nacional de Computação Científica, Petrópolis Centro Multiusuário de Inovação Biomolecular Departamento de Física Universidade Estadual Paulista (UNESP), São José do Rio Preto Federal University of Technology – Paraná, Avenida Alberto Carazzai, 1640, Cornélio Procópio Universidade Federal da Paraíba Centro de Informática, Rua dos Escoteiros, S/N Embrapa Soja, Cx. Postal 231 Centro Multiusuário de Inovação Biomolecular Departamento de Física Universidade Estadual Paulista (UNESP), São José do Rio Preto CAPES: 23038.010041/2013–13 CNPq: 307713/2016–4

Made available in DSpace on 2019-10-04T12:33:56Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-01-01 Brazilian Coffee Research Consortium Fundacao Araucaria Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Coffea arabica L. is an important agricultural commodity, accounting for 60% of traded coffee worldwide. Nitrogen (N) is a macronutrient that is usually limiting to plant yield; however, molecular mechanisms of plant acclimation to N limitation remain largely unknown in tropical woody crops. In this study, we investigated the transcriptome of coffee roots under N starvation, analyzing poly-A+ libraries and small RNAs. We also evaluated the concentration of selected amino acids and N-source preferences in roots. Ammonium was preferentially taken up over nitrate, and asparagine and glutamate were the most abundant amino acids observed in coffee roots. We obtained 34,654 assembled contigs by mRNA sequencing, and validated the transcriptional profile of 12 genes by RT-qPCR. Illumina small RNA sequencing yielded 8,524,332 non-redundant reads, resulting in the identification of 86 microRNA families targeting 253 genes. The transcriptional pattern of eight miRNA families was also validated. To our knowledge, this is the first catalog of differentially regulated amino acids, N sources, mRNAs, and sRNAs in Arabica coffee roots. Inst Agron Parana, Lab Biotecnol Vegetal, BR-86047902 Londrina, Brazil Univ Oeste Paulista, Rodovia Raposo Tavares Km 572, BR-19067175 Presidente Prudente, Brazil Univ Sao Paulo, Ctr Energia Nucl Agr, BR-13400970 Piracicaba, Brazil Univ Fed Minas Gerais, Inst Ciencias Biol, Dept Bot, BR-31270901 Belo Horizonte, MG, Brazil Univ Tecnol Fed Parana, Programa Posgrad Bioinformat, BR-86300000 Cornelio Procopio, Brazil Univ Estadual Paulista, Inst Biociencias Rio Claro, Dept Bot, BR-13506900 Rio Claro, Brazil Univ Paranaense, Lab Biol Mol, BR-87502210 Umuarama, Brazil Univ Estadual Londrina, Programa Posgrad Genet & Biol Mol, BR-86057970 Londrina, Brazil Inst Butantan, Lab Especial Toxinol Aplicada, BR-05503900 Sao Paulo, Brazil Univ Estadual Campinas, Inst Biol, Dept Biol Vegetal, BR-13083970 Campinas, SP, Brazil COFFEEADAPT, UMR IPME, IRD, BP 64501, F-34394 Montpellier 5, France Univ Estadual Campinas, Life Sci Core Facil LaCTAD, BR-13083886 Campinas, SP, Brazil Embrapa Cafe, BR-70770901 Brasilia, DF, Brazil Univ Estadual Paulista, Inst Biociencias Rio Claro, Dept Bot, BR-13506900 Rio Claro, Brazil CAPES: PVE 084/2013

Made available in DSpace on 2018-11-26T17:55:27Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-09-13 Universidade Tecnologica Federal do Parana Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Pro-Reitoria de Pesquisa e Pos-Graduacao (PROPPG-UTFPR) Transposable elements (TEs) play an essential role in the genetic variability of eukaryotic species. In plants, they may comprise up to 90% of the total genome. Non-coding RNAs (ncRNAs) are known to control gene expression and regulation. Although the relationship between ncRNAs and TEs is known, obtaining the organized data for sequenced genomes is not straightforward. In this study, we describe the PlaNC-TE :(http//planc-te.cp.utfpr.edu.br), a user-friendly portal harboring a knowledgebase created by integrating and analysing plant ncRNA-TE data. We identified a total of 14 350 overlaps between ncRNAs and TEs in 40 plant genomes. The database allows users to browse, search and download all ncRNA and TE data analysed. Overall, PlaNC-TE not only organizes data and provides insights about the relationship between ncRNA and TEs in plants but also helps improve genome annotation strategies. Moreover, this is the first database to provide resources to broadly investigate functions and mechanisms involving TEs and ncRNAs in plants. Univ Tecnol Fed Parana, Bioinformat Grad Program PPGBIOINFO, Dept Comp Sci, 1640 Ave Alberto Carazzai, BR-86300000 Cornelio Procopio, PR, Brazil Univ Sao Paulo, Ribeirao Preto Med Sch, Dept Biochem & Immunol, 3900 Bandeirantes Ave, BR-14049900 Monte Alegre do Sul, SP, Brazil UNESP, Sao Paulo State Univ, Inst Biosci, Dept Bot, Av 24-A 1515, BR-13506900 Rio Claro, SP, Brazil UNESP, Sao Paulo State Univ, Inst Biosci, Dept Bot, Av 24-A 1515, BR-13506900 Rio Claro, SP, Brazil FAPESP: 2017/03052-2 CNPq: 309642/2015-9

The mosquito Aedes aegypti is the main vector of several arthropod-borne diseases that have global impacts. In a previous meta-analysis, our group identified a vector gene set containing 110 genes strongly associated with infections of dengue, West Nile and yellow fever viruses. Of these 110 genes, four genes allowed a highly accurate classification of infected status. More recently, a new study of Ae. aegypti infected with Zika virus (ZIKV) was published, providing new data to investigate whether this "infection" gene set is also altered during a ZIKV infection. Our hypothesis is that the infection-associated signature may also serve as a proxy to classify the ZIKV infection in the vector. Raw data associated with the NCBI/BioProject were downloaded and re-analysed. A total of 18 paired-end replicates corresponding to three ZIKV-infected samples and three controls were included in this study. The nMDS technique with a logistic regression was used to obtain the probabilities of belonging to a given class. Thus, to compare both gene sets, we used the area under the curve and performed a comparison using the bootstrap method. Our meta-signature was able to separate the infected mosquitoes from the controls with good predictive power to classify the Zika-infected mosquitoes.

Made available in DSpace on 2019-10-06T15:46:52Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-03-25 MOTIVATION: Long noncoding RNAs (lncRNAs) correspond to a eukaryotic noncoding RNA class that gained great attention in the past years as a higher layer of regulation for gene expression in cells. There is, however, a lack of specific computational approaches to reliably predict lncRNA in plants, which contrast the variety of prediction tools available for mammalian lncRNAs. This distinction is not that obvious, given that biological features and mechanisms generating lncRNAs in the cell are likely different between animals and plants. Considering this, we present a machine learning analysis and a classifier approach called RNAplonc (https://github.com/TatianneNegri/RNAplonc/) to identify lncRNAs in plants. RESULTS: Our feature selection analysis considered 5468 features, and it used only 16 features to robustly identify lncRNA with the REPTree algorithm. That was the base to create the model and train it with lncRNA and mRNA data from five plant species (thale cress, cucumber, soybean, poplar and Asian rice). After an extensive comparison with other tools largely used in plants (CPC, CPC2, CPAT and PLncPRO), we found that RNAplonc produced more reliable lncRNA predictions from plant transcripts with 87.5% of the best result in eight tests in eight species from the GreeNC database and four independent studies in monocotyledonous (Brachypodium) and eudicotyledonous (Populus and Gossypium) species. Department of Computer Science Bioinformatics Graduate Program (PPGBIOINFO) Federal University of Technology - Paraná UTFPR Brazil and Informatics and Knowledge Management Graduate Program Universidade Nove de Julho, Campus Cornélio Informatics and Knowledge Management Graduate Program Universidade Nove de Julho Department of Computer Science Bioinformatics Graduate Program (PPGBIOINFO) Federal University of Technology - Paraná UTFPR, Campus Cornélio Department of Computer Science Bioinformatics Graduate Program (PPGBIOINFO) Federal University of Technology - Paraná UTFPR Brazil and Department of Botany Institute of Biosciences São Paulo State University UNESP, Campus Cornélio Department of Computer Science Bioinformatics Graduate Program (PPGBIOINFO) Federal University of Technology - Paraná UTFPR Brazil and Department of Botany Institute of Biosciences São Paulo State University UNESP, Campus Cornélio

Large-scale transcriptome and methylome data analyses obtained by high-throughput technologies have been enabling the identification of novel imprinted genes. We investigated genome-wide DNA methylation patterns in multiple human tissues, using a high-resolution microarray to uncover hemimethylated CpGs located in promoters overlapping CpG islands, aiming to identify novel candidate imprinted genes. Using our approach, we recovered ~30% of the known human imprinted genes, and a further 168 candidates were identified, 61 of which with at least three hemimethylated CpGs shared by more than two tissue types. Thirty-four of these candidate genes are members of the protocadherin cluster on 5q31.3; in mice, protocadherin genes have non-imprinted random monoallelic expression, which might also be the case in humans. Among the remaining 27 genes, ZNF331 was recently validated as an imprinted gene, and six of them have been reported as candidates, supporting our prediction. Five candidates (CCDC166, ARC, PLEC, TONSL, and VPS28) map to 8q24.3, and might constitute a novel imprinted cluster. Additionally, we performed a comprehensive compilation of known human and mice imprinted genes from literature and databases, and a comparison among high-throughput imprinting studies in humans. The screening for hemimethylated CpGs shared by multiple human tissues, together with the extensive review, appears to be a useful approach to reveal candidate imprinted genes.

Made available in DSpace on 2019-10-06T15:18:04Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-05-30 Fondo Nacional de Desarrollo Científico y Tecnológico Consejo Nacional de Ciencia y Tecnología Deutscher Akademischer Austauschdienst The competing endogenous RNA hypothesis has gained increasing attention as a potential global regulatory mechanism of microRNAs (miRNAs), and as a powerful tool to predict the function of many noncoding RNAs, including miRNAs themselves. Most studies have been focused on animals, although target mimic (TMs) discovery as well as important computational and experimental advances has been developed in plants over the past decade. Thus, our contribution summarizes recent progresses in computational approaches for research of miRNA:TM interactions. We divided this article in three main contributions. First, a general overview of research on TMs in plants is presented with practical descriptions of the available literature, tools, data, databases and computational reports. Second, we describe a common protocol for the computational and experimental analyses of TM. Third, we provide a bioinformatics approach for the prediction of TM motifs potentially cross-targeting both members within the same or from different miRNA families, based on the identification of consensus miRNA-binding sites from known TMs across sequenced genomes, transcriptomes and known miRNAs. This computational approach is promising because, in contrast to animals, miRNA families in plants are large with identical or similar members, several of which are also highly conserved. From the three consensus TM motifs found with our approach: MIM166, MIM171 and MIM159/319, the last one has found strong support on the recent experimental work by Reichel and Millar [Specificity of plant microRNA TMs: cross-targeting of mir159 and mir319. J Plant Physiol 2015;180:45-8]. Finally, we stress the discussion on the major computational and associated experimental challenges that have to be faced in future ceRNA studies. UTFPR - CÂMPUS CORNELIO PROCOPIO, 1640 Avenue Alberto Carazzai Bioinformatics Group Department of Computer Science and Interdisciplinary Center for Bioinformatics University Leipzig, Härtelstraße 16-18 Interdisciplinary Center for Bioinformatics University of Leipzig Department of Botany Institute of Biosciences São Paulo State University (UNESP) Department of Botany Institute of Biosciences São Paulo State University (UNESP) Fondo Nacional de Desarrollo Científico y Tecnológico: 00454505/2014-0 Consejo Nacional de Ciencia y Tecnología: 185993 Deutscher Akademischer Austauschdienst: 57314018

Coffee is one of the most important commodities in the world, and its production relies mainly on two species, Coffea arabica and Coffea canephora. Although there are diverse transcriptome datasets available for coffee trees, few research groups have exploited the potential knowledge contained in these data, especially with respect to fruit and seed development. Here, we present a comparative analysis of the transcriptomes of Coffea arabica and Coffea canephora with a focus on fruit development using publicly available expressed sequence tags (ESTs). Most of the fruit and seed EST data has been obtained from C. canephora. Therefore, we performed a fruit EST analysis of the 5 developmental stages of this species (18, 22, 30, 42, and 46 weeks after flowering) comprising 29,009 sequences. We compared C. canephora fruit ESTs to reference unigenes of C. canephora (7710 contigs and 8955 singletons) and C. arabica (15,656 contigs and 16,351 singletons). Additional analyses included functional annotation based on Gene Onthology, as well as an annotation using PlantCyc, a curated plant protein database. The Coffee Bean EST (CoffeebEST) is a public database available at http://bioinfo-02.cp.utfpr.edu.br/. This database represents an additional resource for the coffee scientific community, offering a user-friendly collection of information for non-specialists in coffee molecular biology to support experimental research on comparative and functional genomics.