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Padilla-Padilla EA, De la Rosa C, Aragón W, Ávila-Sandoval AK, Torres M, Dorantes-Acosta AE, Arteaga-Vázquez MA, Formey D, Serrano M. Identification of Arabidopsis thaliana small RNAs responsive to the fungal pathogen Botrytis cinerea at an early stage of interaction. PLoS One 2024; 19:e0304790. [PMID: 38875250 PMCID: PMC11178217 DOI: 10.1371/journal.pone.0304790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 05/19/2024] [Indexed: 06/16/2024] Open
Abstract
In plants, small RNAs (sRNAs), mainly microRNAs (miRNAs) and small interfering RNAs (siRNAs), have been described as key regulators of plant development, growth, and abiotic and biotic responses. Despite reports indicating the involvement of certain sRNAs in regulating the interaction between Botrytis cinerea (a major necrotrophic fungal phytopathogen) and host plants, there remains a lack of analysis regarding the potential regulatory roles of plant sRNAs during early stages of the interaction despite early immune responses observed then during infection. We present the first transcriptome-wide analysis of small RNA expression on the early interaction between the necrotrophic fungus Botrytis cinerea and the model plant Arabidopsis thaliana. We found that evolutionary conserved A. thaliana miRNAs were the sRNAs that accumulated the most in the presence of B. cinerea. The upregulation of miR167, miR159 and miR319 was of particular interest because these, together with their target transcripts, are involved in the fine regulation of the plant hormone signaling pathways. We also describe that miR173, which triggers the production of secondary siRNAs from TAS1 and TAS2 loci, as well as secondary siRNAs derived from these loci, is upregulated in response to B. cinerea. Thus, at an early stage of the interaction there are transcriptional changes of sRNA-guided silencing pathway genes and of a subset of sRNAs that targeted genes from the PPR gene superfamily, and these may be important mechanisms regulating the interaction between A. thaliana and B. cinerea. This work provides the basis for a better understanding of the regulation mediated by sRNAs during early B. cinerea-plant interaction and may help in the development of more effective strategies for its control.
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Affiliation(s)
- Emir Alejandro Padilla-Padilla
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
- Posgrado en Ciencias Biológicas, Unidad de Posgrado, Ciudad Universitaria, Coyoacán, Ciudad de México
| | - Carlos De la Rosa
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Wendy Aragón
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
- Instituto de Biociencias, Universidad Autónoma de Chiapas, Chiapas, México
| | - Ana Karen Ávila-Sandoval
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Martha Torres
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Ana Elena Dorantes-Acosta
- Instituto de Biotecnología y Ecología Aplicada (INBIOTECA), Universidad Veracruzana, Xalapa, Veracruz, México
| | - Mario A Arteaga-Vázquez
- Instituto de Biotecnología y Ecología Aplicada (INBIOTECA), Universidad Veracruzana, Xalapa, Veracruz, México
| | - Damien Formey
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Mario Serrano
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
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2
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Aina O, Bakare OO, Fadaka AO, Keyster M, Klein A. Plant biomarkers as early detection tools in stress management in food crops: a review. PLANTA 2024; 259:60. [PMID: 38311674 PMCID: PMC10838863 DOI: 10.1007/s00425-024-04333-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 01/07/2024] [Indexed: 02/06/2024]
Abstract
MAIN CONCLUSION Plant Biomarkers are objective indicators of a plant's cellular state in response to abiotic and biotic stress factors. They can be explored in crop breeding and engineering to produce stress-tolerant crop species. Global food production safely and sustainably remains a top priority to feed the ever-growing human population, expected to reach 10 billion by 2050. However, abiotic and biotic stress factors negatively impact food production systems, causing between 70 and 100% reduction in crop yield. Understanding the plant stress responses is critical for developing novel crops that can adapt better to various adverse environmental conditions. Using plant biomarkers as measurable indicators of a plant's cellular response to external stimuli could serve as early warning signals to detect stresses before severe damage occurs. Plant biomarkers have received considerable attention in the last decade as pre-stress indicators for various economically important food crops. This review discusses some biomarkers associated with abiotic and biotic stress conditions and highlights their importance in developing stress-resilient crops. In addition, we highlighted some factors influencing the expression of biomarkers in crop plants under stress. The information presented in this review would educate plant researchers, breeders, and agronomists on the significance of plant biomarkers in stress biology research, which is essential for improving plant growth and yield toward sustainable food production.
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Affiliation(s)
- Omolola Aina
- Plant Omics Laboratory, Department of Biotechnology, University of the Western Cape, Robert Sobukwe Road, Bellville, 7530, South Africa
| | - Olalekan O Bakare
- Department of Biochemistry, Faculty of Basic Medical Sciences, Olabisi Onabanjo University, Sagamu, 121001, Nigeria
- Environmental Biotechnology Laboratory, Department of Biotechnology, University of the Western Cape, Robert Sobukwe Road, Bellville, 7530, South Africa
| | - Adewale O Fadaka
- Plant Omics Laboratory, Department of Biotechnology, University of the Western Cape, Robert Sobukwe Road, Bellville, 7530, South Africa
| | - Marshall Keyster
- Environmental Biotechnology Laboratory, Department of Biotechnology, University of the Western Cape, Robert Sobukwe Road, Bellville, 7530, South Africa
| | - Ashwil Klein
- Plant Omics Laboratory, Department of Biotechnology, University of the Western Cape, Robert Sobukwe Road, Bellville, 7530, South Africa.
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3
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Mustafin RN, Khusnutdinova E. Perspective for Studying the Relationship of miRNAs with Transposable Elements. Curr Issues Mol Biol 2023; 45:3122-3145. [PMID: 37185728 PMCID: PMC10136691 DOI: 10.3390/cimb45040204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 03/07/2023] [Accepted: 04/03/2023] [Indexed: 05/17/2023] Open
Abstract
Transposable elements are important sources of miRNA, long non-coding RNAs genes, and their targets in the composition of protein-coding genes in plants and animals. Therefore, the detection of expression levels of specific non-coding RNAs in various tissues and cells in normal and pathological conditions may indicate a programmed pattern of transposable elements' activation. This reflects the species-specific composition and distribution of transposable elements in genomes, which underlie gene regulation in every cell division, including during aging. TEs' expression is also regulated by epigenetic factors (DNA methylation, histone modifications), SIRT6, cytidine deaminases APOBEC3, APOBEC1, and other catalytic proteins, such as ERCC, TREX1, RB1, HELLS, and MEGP2. In evolution, protein-coding genes and their regulatory elements are derived from transposons. As part of non-coding regions and introns of genes, they are sensors for transcriptional and post-transcriptional control of expression, using miRNAs and long non-coding RNAs, that arose from transposable elements in evolution. Methods (Orbld, ncRNAclassifier) and databases have been created for determining the occurrence of miRNAs from transposable elements in plants (PlanTE-MIR DB, PlaNC-TE), which can be used to design epigenetic gene networks in ontogenesis. Based on the data accumulated in the scientific literature, the presence of 467 transposon-derived miRNA genes in the human genome has been reliably established. It was proposed to create an updated and controlled online bioinformatics database of miRNAs derived from transposable elements in healthy individuals, as well as expression changes of these miRNAs during aging and various diseases, such as cancer and difficult-to-treat diseases. The use of the information obtained can open new horizons in the management of tissue and organ differentiation to aging slow down. In addition, the created database could become the basis for clarifying the mechanisms of pathogenesis of various diseases (imbalance in the activity of transposable elements, reflected in changes in the expression of miRNAs) and designing their targeted therapy using specific miRNAs as targets. This article provides examples of the detection of transposable elements-derived miRNAs involved in the development of specific malignant neoplasms, aging, and idiopathic pulmonary fibrosis.
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Affiliation(s)
- Rustam Nailevich Mustafin
- Department of Medical Genetics and Fundamental Medicine, Bashkir State Medical University, 450008 Ufa, Russia
| | - Elza Khusnutdinova
- Ufa Federal Research Centre, Institute of Biochemistry and Genetics, Russian Academy of Sciences, 450054 Ufa, Russia
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4
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Zhang T, Zhai J, Zhang X, Ling L, Li M, Xie S, Song M, Ma C. Interactive Web-based Annotation of Plant MicroRNAs with iwa-miRNA. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:557-567. [PMID: 34332120 PMCID: PMC9801042 DOI: 10.1016/j.gpb.2021.02.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 12/15/2020] [Accepted: 03/06/2021] [Indexed: 01/26/2023]
Abstract
MicroRNAs (miRNAs) are important regulators of gene expression. The large-scale detection and profiling of miRNAs have been accelerated with the development of high-throughput small RNA sequencing (sRNA-Seq) techniques and bioinformatics tools. However, generating high-quality comprehensive miRNA annotations remains challenging due to the intrinsic complexity of sRNA-Seq data and inherent limitations of existing miRNA prediction tools. Here, we present iwa-miRNA, a Galaxy-based framework that can facilitate miRNA annotation in plant species by combining computational analysis and manual curation. iwa-miRNA is specifically designed to generate a comprehensive list of miRNA candidates, bridging the gap between already annotated miRNAs provided by public miRNA databases and new predictions from sRNA-Seq datasets. It can also assist users in selecting promising miRNA candidates in an interactive mode, contributing to the accessibility and reproducibility of genome-wide miRNA annotation. iwa-miRNA is user-friendly and can be easily deployed as a web application for researchers without programming experience. With flexible, interactive, and easy-to-use features, iwa-miRNA is a valuable tool for the annotation of miRNAs in plant species with reference genomes. We also illustrate the application of iwa-miRNA for miRNA annotation using data from plant species with varying genomic complexity. The source codes and web server of iwa-miRNA are freely accessible at http://iwa-miRNA.omicstudio.cloud/.
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Affiliation(s)
- Ting Zhang
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling 712100, China,Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture and Rural Affairs, Northwest A&F University, Yangling 712100, China
| | - Jingjing Zhai
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling 712100, China,Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture and Rural Affairs, Northwest A&F University, Yangling 712100, China
| | - Xiaorong Zhang
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling 712100, China,Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture and Rural Affairs, Northwest A&F University, Yangling 712100, China
| | - Lei Ling
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling 712100, China,Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture and Rural Affairs, Northwest A&F University, Yangling 712100, China
| | - Menghan Li
- College of Plant Science, Tibet Agricultural and Animal Husbandry University, Linzhi 860006, China
| | - Shang Xie
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling 712100, China,Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture and Rural Affairs, Northwest A&F University, Yangling 712100, China
| | - Minggui Song
- College of Information Engineering, Northwest A&F University, Yangling 712100, China
| | - Chuang Ma
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling 712100, China,Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture and Rural Affairs, Northwest A&F University, Yangling 712100, China,Corresponding author.
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5
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Jyothsna S, Alagu M. Role of phasiRNAs in plant-pathogen interactions: molecular perspectives and bioinformatics tools. PHYSIOLOGY AND MOLECULAR BIOLOGY OF PLANTS : AN INTERNATIONAL JOURNAL OF FUNCTIONAL PLANT BIOLOGY 2022; 28:947-961. [PMID: 35722509 PMCID: PMC9203634 DOI: 10.1007/s12298-022-01189-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 05/01/2022] [Accepted: 05/18/2022] [Indexed: 05/03/2023]
Abstract
The genome of an organism is regulated in concert with the organized action of various genetic regulators at different hierarchical levels. Small non-coding RNAs are one of these regulators, among which microRNAs (miRNAs), a distinguished sRNA group with decisive functions in the development, growth and stress-responsive activities of both plants as well as animals, are keenly explored over a good number of years. Recent studies in plants revealed that apart from the silencing activity exhibited by miRNAs on their targets, miRNAs of specific size and structural features can direct the phasing pattern of their target loci to form phased secondary small interfering RNAs (phasiRNAs). These trigger-miRNAs were identified to target both coding and long non-coding RNAs that act as potent phasiRNA precursors or PHAS loci. The phasiRNAs produced thereby exhibit a role in enhancing further downstream regulation either on their own precursors or on those transcripts that are distinct from their genetic source of origin. Hence, these tiny regulators can stimulate an elaborative cascade of interacting RNA networks via cis and trans-regulatory mechanisms. Our review focuses on the comprehensive understanding of phasiRNAs and their trigger miRNAs, by giving much emphasis on their role in the regulation of plant defense responses, together with a summary of the computational tools available for the prediction of the same.
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Affiliation(s)
- S. Jyothsna
- Department of Genomic Science, Central University of Kerala, Periye, Kasaragod, Kerala 671316 India
| | - Manickavelu Alagu
- Department of Genomic Science, Central University of Kerala, Periye, Kasaragod, Kerala 671316 India
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6
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Guerra-Almeida D, Tschoeke DA, da-Fonseca RN. Understanding small ORF diversity through a comprehensive transcription feature classification. DNA Res 2021; 28:6317669. [PMID: 34240112 PMCID: PMC8435553 DOI: 10.1093/dnares/dsab007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Indexed: 11/13/2022] Open
Abstract
Small open reading frames (small ORFs/sORFs/smORFs) are potentially coding sequences smaller than 100 codons that have historically been considered junk DNA by gene prediction software and in annotation screening; however, the advent of next-generation sequencing has contributed to the deeper investigation of junk DNA regions and their transcription products, resulting in the emergence of smORFs as a new focus of interest in systems biology. Several smORF peptides were recently reported in noncanonical mRNAs as new players in numerous biological contexts; however, their relevance is still overlooked in coding potential analysis. Hence, this review proposes a smORF classification based on transcriptional features, discussing the most promising approaches to investigate smORFs based on their different characteristics. First, smORFs were divided into nonexpressed (intergenic) and expressed (genic) smORFs. Second, genic smORFs were classified as smORFs located in noncoding RNAs (ncRNAs) or canonical mRNAs. Finally, smORFs in ncRNAs were further subdivided into sequences located in small or long RNAs, whereas smORFs located in canonical mRNAs were subdivided into several specific classes depending on their localization along the gene. We hope that this review provides new insights into large-scale annotations and reinforces the role of smORFs as essential components of a hidden coding DNA world.
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Affiliation(s)
- Diego Guerra-Almeida
- Institute of Biodiversity and Sustainability, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Diogo Antonio Tschoeke
- Alberto Luiz Coimbra Institute of Graduate Studies and Engineering Research (COPPE), Biomedical Engineering Program, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Rodrigo Nunes- da-Fonseca
- Institute of Biodiversity and Sustainability, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.,National Institute of Science and Technology in Molecular Entomology, Rio de Janeiro, Brazil
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7
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Li Q, Liu G, Bao Y, Wu Y, You Q. Evaluation and application of tools for the identification of known microRNAs in plants. APPLICATIONS IN PLANT SCIENCES 2021; 9:e11414. [PMID: 33854848 PMCID: PMC8027368 DOI: 10.1002/aps3.11414] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 02/07/2021] [Indexed: 06/12/2023]
Abstract
MicroRNAs (miRNAs), endogenous non-coding RNA regulators, post-transcriptionally inhibit the expression of their target genes. Several tools have been developed for predicting annotated known miRNAs, but there is no consensus about how to select the most suitable method for any given species. In this study, eight miRNA prediction tools (mirnovo, miRPlant, miRDeep-P2, miRExpress, miRkwood, miRDeep2, miR-PREFeR, and sRNAbench) were selected for evaluation. High-throughput small RNA sequencing data from four plant species (including C3 and C4 species, and both monocots and dicots, i.e., Arabidopsis thaliana, Oryza sativa, Triticum aestivum, and Zea mays) were used for the analysis. The sensitivity, accuracy, area under the curve, consistency, duration, and RAM usage of the known miRNA predictions were evaluated for each tool. The miRNA annotations were obtained using miRBase and sRNAanno. Algorithms, such as random forest, BLAST, and receiver operating characteristic curves, were used to evaluate accuracy. Of the tools evaluated, sRNAbench was found to be the most accurate, miRDeep-P2 was the most sensitive, miRDeep-P2 was the fastest, and miRkwood had the highest memory usage. Due to its large genome size, only three tools were able to successfully predict known miRNAs in wheat (Triticum aestivum). Our results enable us to recommend the tool best suited to a variety of researcher needs, which we hope will reduce confusion and enhance future work.
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Affiliation(s)
- Qinglian Li
- Key Laboratory of Plant Functional Genomics of the Ministry of Education/Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Co‐Innovation Center for Modern Production Technology of Grain CropsCollege of AgricultureYangzhou UniversityYangzhou225009China
- Jiangsu Xuzhou Sweet Potato Research CenterXuzhou221131China
| | - Guanqing Liu
- Key Laboratory of Plant Functional Genomics of the Ministry of Education/Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Co‐Innovation Center for Modern Production Technology of Grain CropsCollege of AgricultureYangzhou UniversityYangzhou225009China
| | - Yu Bao
- Key Laboratory of Plant Functional Genomics of the Ministry of Education/Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Co‐Innovation Center for Modern Production Technology of Grain CropsCollege of AgricultureYangzhou UniversityYangzhou225009China
| | - Yuechao Wu
- Key Laboratory of Plant Functional Genomics of the Ministry of Education/Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Co‐Innovation Center for Modern Production Technology of Grain CropsCollege of AgricultureYangzhou UniversityYangzhou225009China
| | - Qi You
- Key Laboratory of Plant Functional Genomics of the Ministry of Education/Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Co‐Innovation Center for Modern Production Technology of Grain CropsCollege of AgricultureYangzhou UniversityYangzhou225009China
- State Key Laboratory of Cotton BiologyAnyangHenan455000China
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8
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Vivek AT, Kumar S. Computational methods for annotation of plant regulatory non-coding RNAs using RNA-seq. Brief Bioinform 2020; 22:6041165. [PMID: 33333550 DOI: 10.1093/bib/bbaa322] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 10/19/2020] [Accepted: 10/20/2020] [Indexed: 12/19/2022] Open
Abstract
Plant transcriptome encompasses numerous endogenous, regulatory non-coding RNAs (ncRNAs) that play a major biological role in regulating key physiological mechanisms. While studies have shown that ncRNAs are extremely diverse and ubiquitous, the functions of the vast majority of ncRNAs are still unknown. With ever-increasing ncRNAs under study, it is essential to identify, categorize and annotate these ncRNAs on a genome-wide scale. The use of high-throughput RNA sequencing (RNA-seq) technologies provides a broader picture of the non-coding component of transcriptome, enabling the comprehensive identification and annotation of all major ncRNAs across samples. However, the detection of known and emerging class of ncRNAs from RNA-seq data demands complex computational methods owing to their unique as well as similar characteristics. Here, we discuss major plant endogenous, regulatory ncRNAs in an RNA sample followed by computational strategies applied to discover each class of ncRNAs using RNA-seq. We also provide a collection of relevant software packages and databases to present a comprehensive bioinformatics toolbox for plant ncRNA researchers. We assume that the discussions in this review will provide a rationale for the discovery of all major categories of plant ncRNAs.
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Affiliation(s)
- A T Vivek
- National Institute of Plant Genome Research in New Delhi, India
| | - Shailesh Kumar
- National Institute of Plant Genome Research in New Delhi
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9
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Bugnon LA, Yones C, Milone DH, Stegmayer G. Genome-wide discovery of pre-miRNAs: comparison of recent approaches based on machine learning. Brief Bioinform 2020; 22:5894456. [PMID: 34020552 DOI: 10.1093/bib/bbaa184] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 07/13/2020] [Accepted: 07/18/2020] [Indexed: 01/12/2023] Open
Abstract
MOTIVATION The genome-wide discovery of microRNAs (miRNAs) involves identifying sequences having the highest chance of being a novel miRNA precursor (pre-miRNA), within all the possible sequences in a complete genome. The known pre-miRNAs are usually just a few in comparison to the millions of candidates that have to be analyzed. This is of particular interest in non-model species and recently sequenced genomes, where the challenge is to find potential pre-miRNAs only from the sequenced genome. The task is unfeasible without the help of computational methods, such as deep learning. However, it is still very difficult to find an accurate predictor, with a low false positive rate in this genome-wide context. Although there are many available tools, these have not been tested in realistic conditions, with sequences from whole genomes and the high class imbalance inherent to such data. RESULTS In this work, we review six recent methods for tackling this problem with machine learning. We compare the models in five genome-wide datasets: Arabidopsis thaliana, Caenorhabditis elegans, Anopheles gambiae, Drosophila melanogaster, Homo sapiens. The models have been designed for the pre-miRNAs prediction task, where there is a class of interest that is significantly underrepresented (the known pre-miRNAs) with respect to a very large number of unlabeled samples. It was found that for the smaller genomes and smaller imbalances, all methods perform in a similar way. However, for larger datasets such as the H. sapiens genome, it was found that deep learning approaches using raw information from the sequences reached the best scores, achieving low numbers of false positives. AVAILABILITY The source code to reproduce these results is in: http://sourceforge.net/projects/sourcesinc/files/gwmirna Additionally, the datasets are freely available in: https://sourceforge.net/projects/sourcesinc/files/mirdata.
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Affiliation(s)
- Leandro A Bugnon
- Research Institute for Signals, Systems and Computational Intelligence sinc(i), FICH/UNL-CONICET, Ciudad Universitaria, Santa Fe, Argentina
| | - Cristian Yones
- Research Institute for Signals, Systems and Computational Intelligence sinc(i), FICH/UNL-CONICET, Ciudad Universitaria, Santa Fe, Argentina
| | - Diego H Milone
- Research Institute for Signals, Systems and Computational Intelligence sinc(i), FICH/UNL-CONICET, Ciudad Universitaria, Santa Fe, Argentina
| | - Georgina Stegmayer
- Research Institute for Signals, Systems and Computational Intelligence sinc(i), FICH/UNL-CONICET, Ciudad Universitaria, Santa Fe, Argentina
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10
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PlantMirP-Rice: An Efficient Program for Rice Pre-miRNA Prediction. Genes (Basel) 2020; 11:genes11060662. [PMID: 32570706 PMCID: PMC7349308 DOI: 10.3390/genes11060662] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Revised: 06/13/2020] [Accepted: 06/15/2020] [Indexed: 11/16/2022] Open
Abstract
Rice microRNAs (miRNAs) are important post-transcriptional regulation factors and play vital roles in many biological processes, such as growth, development, and stress resistance. Identification of these molecules is the basis of dissecting their regulatory functions. Various machine learning techniques have been developed to identify precursor miRNAs (pre-miRNAs). However, no tool is implemented specifically for rice pre-miRNAs. This study aims at improving prediction performance of rice pre-miRNAs by constructing novel features with high discriminatory power and developing a training model with species-specific data. PlantMirP-rice, a stand-alone random forest-based miRNA prediction tool, achieves a promising accuracy of 93.48% based on independent (unseen) rice data. Comparisons with other competitive pre-miRNA prediction methods demonstrate that plantMirP-rice performs better than existing tools for rice and other plant pre-miRNA classification.
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11
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Ayachit G, Pandya H, Das J. miRDetect: A combinatorial approach for automated detection of novel miRNA precursors from plant EST data using homology and Random Forest classification. Genomics 2020; 112:3201-3206. [PMID: 32380232 DOI: 10.1016/j.ygeno.2020.05.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 04/25/2020] [Accepted: 05/02/2020] [Indexed: 01/23/2023]
Abstract
Identification of microRNAs from plants is a crucial step for understanding the mechanisms of pathways and regulation of genes. A number of tools have been developed for the detection of microRNAs from small RNA-seq data. However, there is a lack of pipeline for detection of miRNA from EST dataset even when a huge resource is publicly available and the method is known. Here we present miRDetect, a python implementation to detect novel miRNA precursors from plant EST data using homology and machine learning approach. 10-fold cross validation was applied to choose best classifier based on ROC, accuracy, MCC and F1-scores using 112 features. miRDetect achieved a classification accuracy of 93.35% on a Random Forest classifier and outperformed other precursor detection tools in terms of performance. The miRDetect pipeline aids in identifying novel plant precursors using a mixed approach and will be helpful to researchers with less informatics background.
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Affiliation(s)
- Garima Ayachit
- Department of Botany, Bioinformatics and Climate Change, Gujarat University, Ahmedabad, India
| | - Himanshu Pandya
- Department of Botany, Bioinformatics and Climate Change, Gujarat University, Ahmedabad, India.
| | - Jayashankar Das
- Centre for Genomics & Biomedical Informatics, IMS and SUM Hospital, Siksha "O" Anusandhan University (Deemed to be), K8, Kalinga Nagar, Bhubaneswar, Odisha 751003, India.
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12
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Vincent D, Rafiqi M, Job D. The Multiple Facets of Plant-Fungal Interactions Revealed Through Plant and Fungal Secretomics. FRONTIERS IN PLANT SCIENCE 2020; 10:1626. [PMID: 31969889 PMCID: PMC6960344 DOI: 10.3389/fpls.2019.01626] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 11/19/2019] [Indexed: 05/14/2023]
Abstract
The plant secretome is usually considered in the frame of proteomics, aiming at characterizing extracellular proteins, their biological roles and the mechanisms accounting for their secretion in the extracellular space. In this review, we aim to highlight recent results pertaining to secretion through the conventional and unconventional protein secretion pathways notably those involving plant exosomes or extracellular vesicles. Furthermore, plants are well known to actively secrete a large array of different molecules from polymers (e.g. extracellular RNA and DNA) to small compounds (e.g. ATP, phytochemicals, secondary metabolites, phytohormones). All of these play pivotal roles in plant-fungi (or oomycetes) interactions, both for beneficial (mycorrhizal fungi) and deleterious outcomes (pathogens) for the plant. For instance, recent work reveals that such secretion of small molecules by roots is of paramount importance to sculpt the rhizospheric microbiota. Our aim in this review is to extend the definition of the plant and fungal secretomes to a broader sense to better understand the functioning of the plant/microorganisms holobiont. Fundamental perspectives will be brought to light along with the novel tools that should support establishing an environment-friendly and sustainable agriculture.
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Affiliation(s)
- Delphine Vincent
- Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Maryam Rafiqi
- AgroBioSciences Program, Mohammed VI Polytechnic University (UM6P), Ben Guerir, Morocco
| | - Dominique Job
- CNRS/Université Claude Bernard Lyon 1/Institut National des Sciences Appliquées/Bayer CropScience Joint Laboratory (UMR 5240), Bayer CropScience, Lyon, France
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Raad J, Stegmayer G, Milone DH. Complexity measures of the mature miRNA for improving pre-miRNAs prediction. Bioinformatics 2019; 36:2319-2327. [DOI: 10.1093/bioinformatics/btz940] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 12/10/2019] [Accepted: 12/17/2019] [Indexed: 12/20/2022] Open
Abstract
AbstractMotivationThe discovery of microRNA (miRNA) in the last decade has certainly changed the understanding of gene regulation in the cell. Although a large number of algorithms with different features have been proposed, they still predict an impractical amount of false positives. Most of the proposed features are based on the structure of precursors of the miRNA only, not considering the important and relevant information contained in the mature miRNA. Such new kind of features could certainly improve the performance of the predictors of new miRNAs.ResultsThis paper presents three new features that are based on the sequence information contained in the mature miRNA. We will show how these new features, when used by a classical supervised machine learning approach as well as by more recent proposals based on deep learning, improve the prediction performance in a significant way. Moreover, several experimental conditions were defined and tested to evaluate the novel features impact in situations close to genome-wide analysis. The results show that the incorporation of new features based on the mature miRNA allows to improve the detection of new miRNAs independently of the classifier used.Availability and implementationhttps://sourceforge.net/projects/sourcesinc/files/cplxmirna/.Supplementary informationSupplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jonathan Raad
- Research Institute for Signals, Systems and Computational Intelligence sinc(i) (FICH-UNL/CONICET), Ciudad Universitaria, Santa Fe, Argentina
| | - Georgina Stegmayer
- Research Institute for Signals, Systems and Computational Intelligence sinc(i) (FICH-UNL/CONICET), Ciudad Universitaria, Santa Fe, Argentina
| | - Diego H Milone
- Research Institute for Signals, Systems and Computational Intelligence sinc(i) (FICH-UNL/CONICET), Ciudad Universitaria, Santa Fe, Argentina
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14
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Guigon I, Legrand S, Berthelot JF, Bini S, Lanselle D, Benmounah M, Touzet H. miRkwood: a tool for the reliable identification of microRNAs in plant genomes. BMC Genomics 2019; 20:532. [PMID: 31253093 PMCID: PMC6599362 DOI: 10.1186/s12864-019-5913-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 06/18/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulation of eukaryotic gene expression and are involved in many aspects of plant development. Although several prediction tools are available for metazoan genomes, the number of tools dedicated to plants is relatively limited. RESULTS Here, we present miRkwood, a user-friendly tool for the identification of miRNAs in plant genomes using small RNA sequencing data. Deep-sequencing data of Argonaute associated small RNAs showed that miRkwood is able to identify a large diversity of plant miRNAs and limits false positive predictions. Moreover, it outperforms current tools such as ShortStack and contrary to ShortStack, miRkwood provides a quality score allowing users to rank miRNA predictions. CONCLUSION miRkwood is a very efficient tool for the annotation of miRNAs in plant genomes. It is available as a web server, as a standalone version, as a docker image and as a Galaxy tool: http://bioinfo.cristal.univ-lille.fr/mirkwood.
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Affiliation(s)
| | - Sylvain Legrand
- University of Lille, CNRS, UMR 8198 - Evo-Eco-Paleo, F-59000 Lille, France
| | | | - Sébastien Bini
- University of Lille, CNRS, INRIA, UMR 9189 - CRIStAL, F-59000 Lille, France
| | - Delphine Lanselle
- University of Lille, CNRS, UMR 8198 - Evo-Eco-Paleo, F-59000 Lille, France
| | | | - Hélène Touzet
- University of Lille, CNRS, INRIA, UMR 9189 - CRIStAL, F-59000 Lille, France
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15
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Guigon I, Legrand S, Berthelot JF, Bini S, Lanselle D, Benmounah M, Touzet H. miRkwood: a tool for the reliable identification of microRNAs in plant genomes. BMC Genomics 2019. [PMID: 31253093 DOI: 10.1186/s12864-019-5913-9.31253093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/01/2023] Open
Abstract
BACKGROUND MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulation of eukaryotic gene expression and are involved in many aspects of plant development. Although several prediction tools are available for metazoan genomes, the number of tools dedicated to plants is relatively limited. RESULTS Here, we present miRkwood, a user-friendly tool for the identification of miRNAs in plant genomes using small RNA sequencing data. Deep-sequencing data of Argonaute associated small RNAs showed that miRkwood is able to identify a large diversity of plant miRNAs and limits false positive predictions. Moreover, it outperforms current tools such as ShortStack and contrary to ShortStack, miRkwood provides a quality score allowing users to rank miRNA predictions. CONCLUSION miRkwood is a very efficient tool for the annotation of miRNAs in plant genomes. It is available as a web server, as a standalone version, as a docker image and as a Galaxy tool: http://bioinfo.cristal.univ-lille.fr/mirkwood.
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Affiliation(s)
| | - Sylvain Legrand
- University of Lille, CNRS, UMR 8198 - Evo-Eco-Paleo, F-59000, Lille, France.
| | | | - Sébastien Bini
- University of Lille, CNRS, INRIA, UMR 9189 - CRIStAL, F-59000, Lille, France
| | - Delphine Lanselle
- University of Lille, CNRS, UMR 8198 - Evo-Eco-Paleo, F-59000, Lille, France
| | | | - Hélène Touzet
- University of Lille, CNRS, INRIA, UMR 9189 - CRIStAL, F-59000, Lille, France.
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Zhang T, Ju L, Zhai J, Song Y, Song J, Ma C. miRLocator: A Python Implementation and Web Server for Predicting miRNAs from Pre-miRNA Sequences. Methods Mol Biol 2019; 1932:89-97. [PMID: 30701493 DOI: 10.1007/978-1-4939-9042-9_6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
microRNAs (miRNAs) are short, noncoding regulatory RNAs derived from hairpin precursors (pre-miRNAs). In synergy with experimental approaches, computational approaches have become an invaluable tool for identifying miRNAs at the genome scale. We have recently reported a method called miRLocator, which applies machine learning algorithms to accurately predict the localization of most likely miRNAs within their pre-miRNAs. One major strength of miRLocator is the fact that the machine learning-based miRNA prediction model can be automatically trained using a set of miRNAs of particular interest, with informative features extracted from miRNA-miRNA* duplexes and the optimized ratio between positive and negative samples. Here, we present a detailed protocol for miRLocator that performs the training and prediction processes using a python implementation and web interface. The source codes, web interface, and manual documents are freely available to academic users at https://github.com/cma2015/miRLocator .
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Affiliation(s)
- Ting Zhang
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, China.,Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, Northwest A&F University, Yangling, China
| | - Lie Ju
- College of Information Engineering, Northwest A&F University, Yangling, China
| | - Jingjing Zhai
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, China.,Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, Northwest A&F University, Yangling, China
| | - Yujia Song
- College of Information Engineering, Northwest A&F University, Yangling, China
| | - Jie Song
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, China.,Biomass Energy Center for Arid and Semi-arid Lands, Northwest A&F University, Yangling, China
| | - Chuang Ma
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, China. .,Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, Northwest A&F University, Yangling, China. .,Biomass Energy Center for Arid and Semi-arid Lands, Northwest A&F University, Yangling, China.
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