Dataset Information

MepmiRDB: a medicinal plant microRNA database.

ABSTRACT: MicroRNAs (miRNAs) have been recognized as a key regulator in plant development and metabolism. Recent reports showed that the miRNAs of medicinal plants not only act as a critical modulator in secondary metabolism but also had a great potential of performing cross-kingdom gene regulation. Although several plant miRNA repositories have been publicly available, no miRNA database specific for medicinal plants has been reported to date. Here, we report the first version of MepmiRDB (medicinal plant microRNA database), which is freely accessible at http://mepmirdb.cn/mepmirdb/index.html. This database accommodates thousands of miRNA candidates belonging to 29 medicinal plant species. The miRNA information on sequences, expression patterns and regulatory networks has been included in the functional modules of the database. Specifically, the 'Sequence' module provides the sequences of the mature miRNAs and their precursors, and the structure information of the precursors. Moreover, the processing and small RNA accumulation signals on the miRNA precursors are also included in the 'Sequence' module. The organ/growth condition-specific expression information of the mature miRNAs has been stored in the 'Expression' module. The 'Interaction' module offers the information of the degradome-validated miRNA-target pairs of eight plant species. The 'Search' module enables users to search for the miRNAs by plant species and miRNA families, or by sequences. All data in this database are available for download. Taken together, the functional modules of MepmiRDB ensure its importance and timeliness for mechanistic and functional studies on the medicinal plant miRNAs.

SUBMITTER: Yu D

PROVIDER: S-EPMC6589547 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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MepmiRDB: a medicinal plant microRNA database.

Yu Dongliang D Lu Jiangjie J Shao Weishan W Ma Xiaoxia X Xie Tian T Ito Hidetaka H Wang Tingzhang T Xu Min M Wang Huizhong H Meng Yijun Y

Database : the journal of biological databases and curation 20190101

MicroRNAs (miRNAs) have been recognized as a key regulator in plant development and metabolism. Recent reports showed that the miRNAs of medicinal plants not only act as a critical modulator in secondary metabolism but also had a great potential of performing cross-kingdom gene regulation. Although several plant miRNA repositories have been publicly available, no miRNA database specific for medicinal plants has been reported to date. Here, we report the first version of MepmiRDB (medicinal plant ...[more]

PMID: 31231773

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Project description:BackgroundMicroRNAs (miRNAs) are identified in nearly all plants where they play important roles in development and stress responses by target mRNA cleavage or translation repression. MiRNAs exert their functions by sequence complementation with target genes and hence their targets can be predicted using bioinformatics algorithms. In the past two decades, microarray technology has been employed to study genes involved in important biological processes such as biotic response, abiotic response, and specific tissues and developmental stages, many of which are miRNA targets. Despite their value in assisting research work for plant biologists, miRNA target genes are difficult to access without pre-processing and assistance of necessary analytical and visualization tools because they are embedded in a large body of microarray data that are scattered around in public databases.DescriptionPlant MiRNA Target Expression Database (PMTED) is designed to retrieve and analyze expression profiles of miRNA targets represented in the plethora of existing microarray data that are manually curated. It provides a Basic Information query function for miRNAs and their target sequences, gene ontology, and differential expression profiles. It also provides searching and browsing functions for a global Meta-network among species, bioprocesses, conditions, and miRNAs, meta-terms curated from well annotated microarray experiments. Networks are displayed through a Cytoscape Web-based graphical interface. In addition to conserved miRNAs, PMTED provides a target prediction portal for user-defined novel miRNAs and corresponding target expression profile retrieval. Hypotheses that are suggested by miRNA-target networks should provide starting points for further experimental validation.ConclusionsPMTED exploits value-added microarray data to study the contextual significance of miRNA target genes and should assist functional investigation for both miRNAs and their targets. PMTED will be updated over time and is freely available for non-commercial use at http://pmted.agrinome.org.

Project description:Medicinal plants have been used to treat diseases since ancient times. Plants used as raw materials for herbal medicine are known as medicinal plants [2]. The U. S. Forest Service estimates that 40% of pharmaceutical drugs in the Western world are derived from plants [1]. Seven thousand medical compounds are derived from plants in the modern pharmacopeia. Herbal medicine combines traditional empirical knowledge with modern science [2]. A medicinal plant is considered an important source of prevention against various diseases [2]. The essential medicine component is extracted from different parts of the plants [8]. In underdeveloped countries, people use medicinal plants as a substitute for medicine. There are various species of plants in the world. Herbs are one of them, which are of different shapes, colors, and leaves [5]. It is difficult for ordinary people to recognize these species of herbs. People use more than 50000 plants in the world for medicinal purposes. There are 8000 medicinal plants in India with evidence of medicinal properties [7]. Automatic classification of these plant species is important because it requires intensive domain knowledge to manually classify the proper species. Machine learning techniques are extensively used in classifying medicinal plant species from photographs, which is challenging but intriguing to academics. Artificial Neural Network classifiers' effective performance depends on the quality of the image dataset [4]. This article represents a medicinal plant dataset: an image dataset of ten different Bangladeshi plant species. Images of medicinal plant leaves were from various gardens, including the Pharmacy Garden at Khwaja Yunus Ali University and the Khwaja Yunus Ali Medical College & Hospital in Sirajganj, Bangladesh. Images were collected by taking pictures with high-resolution mobile phone cameras. Ten medicinal species, 500 images per species are included in the data set, namely, Nayantara (Catharanthus roseus), Pathor kuchi (Kalanchoe pinnata), Gynura procumbens (Longevity spinach), Bohera (Terminalia bellirica), Haritaki (Terminalia chebula), Thankuni (Centella asiatica), Neem (Azadirachta indica), Tulsi (Ocimum tenniflorum), Lemon grass (Cymbopogon citratus), and Devil backbone (Euphorbia tithymaloides). This dataset will benefit researchers applying machine learning and computer vision algorithms in several ways. For example, training and evaluation of machine learning models with this well-curated high-quality dataset, development of new computer vision algorithms, automatic medicinal plant identification in the field of botany and pharmacology for drug discovery and conservation, and data augmentation. Overall, this medicinal plant image dataset can provide researchers in the field of machine learning and computer vision with a valuable resource to develop and evaluate algorithms for plant phenotyping, disease detection, plant identification, drug development, and other tasks related to medicinal plants.

Dataset Information

MepmiRDB: a medicinal plant microRNA database.

Publications

MepmiRDB: a medicinal plant microRNA database.

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