Emerging Topics in Plant Bioinformatics and Omics Data Analysis

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Genetics, Genomics and Biotechnology".

Deadline for manuscript submissions: 28 February 2024 | Viewed by 2353

Special Issue Editors

Department of Chemical and Biological Sciences, Youngstown State University, Youngstown, OH 44555, USA
Interests: bioinformatics software and database development; DNA sequence and RNA-seq data analysis; alternative splicing; secretome and protein subcellular location prediction; gene and genome annotation and molecular evolutionary analysis
Basic Forestry and Proteomics Research Center, College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Interests: forest; long-read sequencing; genome; epitranscriptome; bioinformatics; Populus trichocarpa; Phyllostachys edulis; Dendrocalamus latiflorus Munro; post-transcriptional regulation
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Special Issue Information

Dear Colleagues,

The rapid accumulation of omics data in plants, including genomes, transcriptomes, proteomes, and metabolomes, requires intensive applications of bioinformatics and the development of a computational algorithm. This Special Issue is dedicated to plant bioinformatics with a focus on omics data analysis and data integration, including genome annotation and evolutionary analysis, the comparative genome-wide analysis of transcriptomes and proteomes, such as alternative splicing, protein subcellular location prediction and curation, etc. Papers in all areas of the applications and development of bioinformatics tools and databases resources are considered.

This Special Issue welcomes submissions of the following article types: methods, tools and database, original research, and reviews.

Prof. Dr. Xiangjia Min
Prof. Dr. Lianfeng Gu
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bioinformatics tools
  • databases
  • genomics
  • RNA-seq
  • transcriptome
  • metabolome
  • metagenomics
  • proteome
  • resources
  • alternative splicing
  • systems biology
  • molecular evolution
  • protein function
  • protein subcellular location

Published Papers (1 paper)

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Research

22 pages, 5595 KiB  
Article
CircPCBL: Identification of Plant CircRNAs with a CNN-BiGRU-GLT Model
Plants 2023, 12(8), 1652; https://doi.org/10.3390/plants12081652 - 14 Apr 2023
Cited by 1 | Viewed by 1467
Abstract
Circular RNAs (circRNAs), which are produced post-splicing of pre-mRNAs, are strongly linked to the emergence of several tumor types. The initial stage in conducting follow-up studies involves identifying circRNAs. Currently, animals are the primary target of most established circRNA recognition technologies. However, the [...] Read more.
Circular RNAs (circRNAs), which are produced post-splicing of pre-mRNAs, are strongly linked to the emergence of several tumor types. The initial stage in conducting follow-up studies involves identifying circRNAs. Currently, animals are the primary target of most established circRNA recognition technologies. However, the sequence features of plant circRNAs differ from those of animal circRNAs, making it impossible to detect plant circRNAs. For example, there are non-GT/AG splicing signals at circRNA junction sites and few reverse complementary sequences and repetitive elements in the flanking intron sequences of plant circRNAs. In addition, there have been few studies on circRNAs in plants, and thus it is urgent to create a plant-specific method for identifying circRNAs. In this study, we propose CircPCBL, a deep-learning approach that only uses raw sequences to distinguish between circRNAs found in plants and other lncRNAs. CircPCBL comprises two separate detectors: a CNN-BiGRU detector and a GLT detector. The CNN-BiGRU detector takes in the one-hot encoding of the RNA sequence as the input, while the GLT detector uses k-mer (k = 1 − 4) features. The output matrices of the two submodels are then concatenated and ultimately pass through a fully connected layer to produce the final output. To verify the generalization performance of the model, we evaluated CircPCBL using several datasets, and the results revealed that it had an F1 of 85.40% on the validation dataset composed of six different plants species and 85.88%, 75.87%, and 86.83% on the three cross-species independent test sets composed of Cucumis sativus, Populus trichocarpa, and Gossypium raimondii, respectively. With an accuracy of 90.9% and 90%, respectively, CircPCBL successfully predicted ten of the eleven circRNAs of experimentally reported Poncirus trifoliata and nine of the ten lncRNAs of rice on the real set. CircPCBL could potentially contribute to the identification of circRNAs in plants. In addition, it is remarkable that CircPCBL also achieved an average accuracy of 94.08% on the human datasets, which is also an excellent result, implying its potential application in animal datasets. Ultimately, CircPCBL is available as a web server, from which the data and source code can also be downloaded free of charge. Full article
(This article belongs to the Special Issue Emerging Topics in Plant Bioinformatics and Omics Data Analysis)
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