The genome and population genomics of allopolyploid Coffea arabica reveal the diversification history of modern coffee cultivars.

Coffea arabica, an allotetraploid hybrid of Coffea eugenioides and Coffea canephora, is the source of approximately 60% of coffee products worldwide, and its cultivated accessions have undergone several population bottlenecks. We present chromosome-level assemblies of a di-haploid C. arabica accession and modern representatives of its diploid progenitors, C. eugenioides and C. canephora. The three species exhibit largely conserved genome structures between diploid parents and descendant subgenomes, with no obvious global subgenome dominance. We find evidence for a founding polyploidy event 350,000-610,000 years ago, followed by several pre-domestication bottlenecks, resulting in narrow genetic variation. A split between wild accessions and cultivar progenitors occurred ~30.5 thousand years ago, followed by a period of migration between the two populations. Analysis of modern varieties, including lines historically introgressed with C. canephora, highlights their breeding histories and loci that may contribute to pathogen resistance, laying the groundwork for future genomics-based breeding of C. arabica.

cirCodAn: A GHMM-based tool for accurate prediction of coding regions in circRNA.

Studies focusing on characterizing circRNAs with the potential to translate into peptides are quickly advancing. It is helping to elucidate the roles played by circRNAs in several biological processes, especially in the emergence and development of diseases. While various tools are accessible for predicting coding regions within linear sequences, none have demonstrated accurate open reading frame detection in circular sequences, such as circRNAs. Here, we present cirCodAn, a novel tool designed to predict coding regions in circRNAs. We evaluated the performance of cirCodAn using datasets of circRNAs with strong translation evidence and showed that cirCodAn outperformed the other tools available to perform a similar task. Our findings demonstrate the applicability of cirCodAn to identify coding regions in circRNAs, which reveals the potential of use of cirCodAn in future research focusing on elucidating the biological roles of circRNAs and their encoded proteins. cirCodAn is freely available at https://github.com/denilsonfbar/cirCodAn.

Genome mining of metabolic gene clusters in the Rubiaceae family.

The Rubiaceae plant family, comprising 3 subfamilies and over 13,000 species, is known for producing significant bioactive compounds such as caffeine and monoterpene indole alkaloids. Despite an increase in available genomes from the Rubiaceae family over the past decade, a systematic analysis of the metabolic gene clusters (MGCs) encoded by these genomes has been lacking. In this study, we aim to identify and analyze metabolic gene clusters within complete Rubiaceae genomes through a comparative analysis of eight species. Applying two bioinformatics pipelines, we identified 2372 candidate MGCs, organized into 549 gene cluster families (GCFs). To enhance the reliability of these findings, we developed coexpression networks and conducted orthology analyses. Using genomic data from Solanum lycopersicum (Solanaceae) for comparative purposes, we provided a detailed view of predicted metabolic enzymes, pathways, and coexpression networks. We bring some examples of MGCs and GCFs involved in biological pathways of terpenes, saccharides and alkaloids. Such insights lay the groundwork for discovering new compounds and associated MGCs within the Rubiaceae family, with potential implications in developing more robust crop species and expanding the understanding of plant metabolism. This large-scale exploration also provides a new perspective on the evolution and structure-function relationship of these clusters, offering opportunities for the highly efficient utilization of these unique metabolites. The outcome of this study contributes to a broader comprehension of the biosynthetic pathways, elucidating multiple aspects of specialized metabolism and offering innovative avenues for biotechnological applications.

Identification of Novel Genes and Proteoforms in Angiostrongylus costaricensis through a Proteogenomic Approach.

RNA sequencing (RNA-Seq) and mass-spectrometry-based proteomics data are often integrated in proteogenomic studies to assist in the prediction of eukaryote genome features, such as genes, splicing, single-nucleotide (SNVs), and single-amino-acid variants (SAAVs). Most genomes of parasite nematodes are draft versions that lack transcript- and protein-level information and whose gene annotations rely only on computational predictions. Angiostrongylus costaricensis is a roundworm species that causes an intestinal inflammatory disease, known as abdominal angiostrongyliasis (AA). Currently, there is no drug available that acts directly on this parasite, mostly due to the sparse understanding of its molecular characteristics. The available genome of A. costaricensis, specific to the Costa Rica strain, is a draft version that is not supported by transcript- or protein-level evidence. This study used RNA-Seq and MS/MS data to perform an in-depth annotation of the A. costaricensis genome. Our prediction improved the reference annotation with (a) novel coding and non-coding genes; (b) pieces of evidence of alternative splicing generating new proteoforms; and (c) a list of SNVs between the Brazilian (Crissiumal) and the Costa Rica strain. To the best of our knowledge, this is the first time that a multi-omics approach has been used to improve the genome annotation of A. costaricensis. We hope this improved genome annotation can assist in the future development of drugs, kits, and vaccines to treat, diagnose, and prevent AA caused by either the Brazil strain (Crissiumal) or the Costa Rica strain.

MicroRNA Databases 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.

Computational Analysis of Transposable Elements and CircRNAs in Plants.

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.

TERL: classification of transposable elements by convolutional neural networks.

Transposable elements (TEs) are the most represented sequences occurring in eukaryotic genomes. Few methods provide the classification of these sequences into deeper levels, such as superfamily level, which could provide useful and detailed information about these sequences. Most methods that classify TE sequences use handcrafted features such as k-mers and homology-based search, which could be inefficient for classifying non-homologous sequences. Here we propose an approach, called transposable elements pepresentation learner (TERL), that preprocesses and transforms one-dimensional sequences into two-dimensional space data (i.e., image-like data of the sequences) and apply it to deep convolutional neural networks. This classification method tries to learn the best representation of the input data to classify it correctly. We have conducted six experiments to test the performance of TERL against other methods. Our approach obtained macro mean accuracies and F1-score of 96.4% and 85.8% for superfamilies and 95.7% and 91.5% for the order sequences from RepBase, respectively. We have also obtained macro mean accuracies and F1-score of 95.0% and 70.6% for sequences from seven databases into superfamily level and 89.3% and 73.9% for the order level, respectively. We surpassed accuracy, recall and specificity obtained by other methods on the experiment with the classification of order level sequences from seven databases and surpassed by far the time elapsed of any other method for all experiments. Therefore, TERL can learn how to predict any hierarchical level of the TEs classification system and is about 20 times and three orders of magnitude faster than TEclass and PASTEC, respectively https://github.com/muriloHoracio/TERL. Contact:murilocruz@alunos.utfpr.edu.br.

A Practical Guide on Computational Tools and Databases for Transposable Elements in Plants.

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.

An Atlas of Plant Transposable Elements.

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).

mirtronDB: a mirtron knowledge base.

MOTIVATION: Mirtrons arise from short introns with atypical cleavage by using the splicing mechanism. In the current literature, there is no repository centralizing and organizing the data available to the public. To fill this gap, we developed mirtronDB, the first knowledge database dedicated to mirtron, and it is available at http://mirtrondb.cp.utfpr.edu.br/. MirtronDB currently contains a total of 1407 mirtron precursors and 2426 mirtron mature sequences in 18 species. RESULTS: Through a user-friendly interface, users can now browse and search mirtrons by organism, organism group, type and name. MirtronDB is a specialized resource that provides free and user-friendly access to knowledge on mirtron data. AVAILABILITY AND IMPLEMENTATION: MirtronDB is available at http://mirtrondb.cp.utfpr.edu.br/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Noncoding RNAs Databases: Current Status and Trends.

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.

Pattern recognition analysis on long noncoding RNAs: a tool for prediction in plants.

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.

Structural analysis of a novel N-carbamoyl-d-amino acid amidohydrolase from a Brazilian Bradyrhizobium japonicum strain: In silico insights by molecular modelling, docking and molecular dynamics.

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 ß-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 ß-lactam antibiotics such as penicillins, cephalosporins and amoxicillin.

PlaNC-TE: a comprehensive knowledgebase of non-coding RNAs and transposable elements in plants.

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.

Functional annotation and distribution overview of RNA families in 27 Streptococcus agalactiae genomes.

BACKGROUND: Streptococcus agalactiae, also known as Group B Streptococcus (GBS), is a Gram-positive bacterium that colonizes the gastrointestinal and genitourinary tract of humans. This bacterium has also been isolated from various animals, such as fish and cattle. Non-coding RNAs (ncRNAs) can act as regulators of gene expression in bacteria, such as Streptococcus pneumoniae and Streptococcus pyogenes. However, little is known about the genomic distribution of ncRNAs and RNA families in S. agalactiae. RESULTS: Comparative genome analysis of 27 S. agalactiae strains showed more than 5 thousand genomic regions identified and classified as Core, Exclusive, and Shared genome sequences. We identified 27 to 89 RNA families per genome distributed over these regions, from these, 25 were in Core regions while Shared and Exclusive regions showed variations amongst strains. We propose that the amount and type of ncRNA present in each genome can provide a pattern to contribute in the identification of the clonal types. CONCLUSIONS: The identification of RNA families provides an insight over ncRNAs, sRNAs and ribozymes function, that can be further explored as targets for antibiotic development or studied in gene regulation of cellular processes. RNA families could be considered as markers to determine infection capabilities of different strains. Lastly, pan-genome analysis of GBS including the full range of functional transcripts provides a broader approach in the understanding of this pathogen.

Follow up of a robust meta-signature to identify Zika virus infection in Aedes aegypti: another brick in the wall.

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.

ceRNAs in plants: computational approaches and associated challenges for target mimic research.

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.

Follow up of a robust meta-signature to identify Zika virus infection in Aedes aegypti: another brick in the wall

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.

Circulating mRNAs and miRNAs as candidate markers for the diagnosis and prognosis of prostate cancer.

Circulating nucleic acids are found in free form in body fluids and may serve as minimally invasive tools for cancer diagnosis and prognosis. Only a few studies have investigated the potential application of circulating mRNAs and microRNAs (miRNAs) in prostate cancer (PCa). The Cancer Genome Atlas (TCGA) database was used for an in silico analysis to identify circulating mRNA and miRNA as potential markers of PCa. A total of 2,267 genes and 49 miRNAs were differentially expressed between normal and tumor samples. The prediction analyses of target genes and integrative analysis of mRNA and miRNA expression revealed eleven genes and eight miRNAs which were validated by RT-qPCR in plasma samples from 102 untreated PCa patients and 50 cancer-free individuals. Two genes, OR51E2 and SIM2, and two miRNAs, miR-200c and miR-200b, showed significant association with PCa. Expression levels of these transcripts distinguished PCa patients from controls (67% sensitivity and 75% specificity). PCa patients and controls with prostate-specific antigen (PSA) ≤ 4.0 ng/mL were discriminated based on OR51E2 and SIM2 expression levels. The miR-200c expression showed association with Gleason score and miR-200b, with bone metastasis, bilateral tumor, and PSA > 10.0 ng/mL. The combination of circulating mRNA and miRNA was useful for the diagnosis and prognosis of PCa.

Meta-Analysis of Aedes aegypti Expression Datasets: Comparing Virus Infection and Blood-Fed Transcriptomes to Identify Markers of Virus Presence.

The mosquito Aedes aegypti (L.) is vector of several arboviruses including dengue, yellow fever, chikungunya, and more recently zika. Previous transcriptomic studies have been performed to elucidate altered pathways in response to viral infection. However, the intrinsic coupling between alimentation and infection were unappreciated in these studies. Feeding is required for the initial mosquito contact with the virus and these events are highly dependent. Addressing this relationship, we reinterrogated datasets of virus-infected mosquitoes with two different diet schemes (fed and unfed mosquitoes), evaluating the metabolic cross-talk during both processes. We constructed coexpression networks with the differentially expressed genes of these comparison: virus-infected versus blood-fed mosquitoes and virus-infected versus unfed mosquitoes. Our analysis identified one module with 110 genes that correlated with infection status (representing ~0.7% of the A. aegypti genome). Furthermore, we performed a machine-learning approach and summarized the infection status using only four genes (AAEL012128, AAEL014210, AAEL002477, and AAEL005350). While three of the four genes were annotated as hypothetical proteins, AAEL012128 gene is a membrane amino acid transporter correlated with viral envelope binding. This gene alone is able to discriminate all infected samples and thus should have a key role to discriminate viral infection in the A. aegypti mosquito. Moreover, validation using external datasets found this gene as differentially expressed in four transcriptomic experiments. Therefore, these genes may serve as a proxy of viral infection in the mosquito and the others 106 identified genes provides a framework to future studies.