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Plant Bioinformatics: Applications and Databases
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Plant Bioinformatics: Applications and Databases

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 25037

Special Issue Editors

Prof. Dr. Ji Huang

E-Mail Website
Guest Editor
College of Agriculture, Nanjing Agricultural University, Nanjing, China
Interests: crop bioinformatics; molecular breeding; rice
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yufeng Wu

E-Mail Website
Guest Editor
Center for Bioinformatics, Nanjing Agricultural University, Nanjing 210095, China
Interests: epigenetics; rice genetics; bioinformatics

Special Issue Information

Dear Colleagues, 

With the increase in large-scale omics data, e.g., genome re-sequencing, RNA-seq, small RNA-seq, ChIP-seq, and degradome seq, the big data era for plant molecular biology has already begun. However, the utilization of such omics data is limited, and data mining is still a challenge for plant biologists who have limited experiences in bioinformatics. Thus, it is critical to establish the applications, local or online, and databases for plant biologists to increase data mining efficiency from big omics data. Although many plant databases and web applications have been established in recent years, they are still limited in number and usually not updated regularly, compared to databases and applications for humans and animals. Further, with the development of artificial intelligence (AI), AI techniques such as machine learning have been applied in bioinformatics predictions in plants. 

In this Special Issue, we encourage submissions regarding newly developed bioinformatics applications and databases in plants or reviews introducing and comparing existing applications or databases. Manuscript size is flexible, but at least two printed pages are required. Applications and databases should be freely accessible, and there is no login requirement.

Prof. Dr. Ji Huang
Prof. Dr. Yufeng Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Plants is an international peer-reviewed open access semimonthly journal published by MDPI.

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
  • application
  • database
  • omics data
  • data mining
  • machine learning

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Published Papers (6 papers)

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Research

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15 pages, 3695 KiB  
Article
LiDAR Platform for Acquisition of 3D Plant Phenotyping Database
by Manuel G. Forero, Harold F. Murcia, Dehyro Méndez and Juan Betancourt-Lozano
Plants 2022, 11(17), 2199; https://doi.org/10.3390/plants11172199 - 25 Aug 2022
Cited by 20 | Viewed by 3735
Abstract
Currently, there are no free databases of 3D point clouds and images for seedling phenotyping. Therefore, this paper describes a platform for seedling scanning using 3D Lidar with which a database was acquired for use in plant phenotyping research. In total, 362 maize [...] Read more.
Currently, there are no free databases of 3D point clouds and images for seedling phenotyping. Therefore, this paper describes a platform for seedling scanning using 3D Lidar with which a database was acquired for use in plant phenotyping research. In total, 362 maize seedlings were recorded using an RGB camera and a SICK LMS4121R-13000 laser scanner with angular resolutions of 45° and 0.5° respectively. The scanned plants are diverse, with seedling captures ranging from less than 10 cm to 40 cm, and ranging from 7 to 24 days after planting in different light conditions in an indoor setting. The point clouds were processed to remove noise and imperfections with a mean absolute precision error of 0.03 cm, synchronized with the images, and time-stamped. The database includes the raw and processed data and manually assigned stem and leaf labels. As an example of a database application, a Random Forest classifier was employed to identify seedling parts based on morphological descriptors, with an accuracy of 89.41%. Full article
(This article belongs to the Special Issue Plant Bioinformatics: Applications and Databases)
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12 pages, 3205 KiB  
Communication
Non-Heading Chinese Cabbage Database: An Open-Access Platform for the Genomics of Brassica campestris (syn. Brassica rapa) ssp. chinensis
by Zhidong Li, Ying Li, Tongkun Liu, Changwei Zhang, Dong Xiao and Xilin Hou
Plants 2022, 11(8), 1005; https://doi.org/10.3390/plants11081005 - 7 Apr 2022
Cited by 11 | Viewed by 2885
Abstract
The availability of a high-quality genome sequence of Brassica campestris ssp. chinensis NHCC001 has paved the way for deep mining of genome data. We used the B. campestris NHCC001 draft genome to develop a comprehensive database, known as the non-heading Chinese cabbage database, [...] Read more.
The availability of a high-quality genome sequence of Brassica campestris ssp. chinensis NHCC001 has paved the way for deep mining of genome data. We used the B. campestris NHCC001 draft genome to develop a comprehensive database, known as the non-heading Chinese cabbage database, which provides access to the B. campestris NHCC001 genome data. The database provides 127,347 SSR, from which 382,041 pairs of primers were designed. NHCCDB contains information on 105,360 genes, which were further classified into 63 transcription factor families. Furthermore, NHCCDB provides eight kinds of tools for biological or sequencing data analyses, including sequence alignment tools, functional genomics tools, comparative genomics tools, motif analysis tools, genome browser, primer design, and SSR analysis tools. In addition, eight kinds of graphs, including a box plot, Venn diagram, corrplot, Q-Q plot, Manhattan plot, seqLogo, volcano plot, and a heatmap, can be generated rapidly using NHCCDB. We have incorporated a search system for efficient mining of transcription factors and genes, along with an embedded data submit function in NHCCDB. We believe that the NHCCDB database will be a useful platform for non-heading Chinese cabbage research and breeding. Full article
(This article belongs to the Special Issue Plant Bioinformatics: Applications and Databases)
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20 pages, 51513 KiB  
Article
Using FIBexDB for In-Depth Analysis of Flax Lectin Gene Expression in Response to Fusarium oxysporum Infection
by Natalia Petrova and Natalia Mokshina
Plants 2022, 11(2), 163; https://doi.org/10.3390/plants11020163 - 7 Jan 2022
Cited by 6 | Viewed by 2173
Abstract
Plant proteins with lectin domains play an essential role in plant immunity modulation, but among a plurality of lectins recruited by plants, only a few members have been functionally characterized. For the analysis of flax lectin gene expression, we used FIBexDB, which includes [...] Read more.
Plant proteins with lectin domains play an essential role in plant immunity modulation, but among a plurality of lectins recruited by plants, only a few members have been functionally characterized. For the analysis of flax lectin gene expression, we used FIBexDB, which includes an efficient algorithm for flax gene expression analysis combining gene clustering and coexpression network analysis. We analyzed the lectin gene expression in various flax tissues, including root tips infected with Fusarium oxysporum. Two pools of lectin genes were revealed: downregulated and upregulated during the infection. Lectins with suppressed gene expression are associated with protein biosynthesis (Calreticulin family), cell wall biosynthesis (galactose-binding lectin family) and cytoskeleton functioning (Malectin family). Among the upregulated lectin genes were those encoding lectins from the Hevein, Nictaba, and GNA families. The main participants from each group are discussed. A list of lectin genes, the expression of which can determine the resistance of flax, is proposed, for example, the genes encoding amaranthins. We demonstrate that FIBexDB is an efficient tool both for the visualization of data, and for searching for the general patterns of lectin genes that may play an essential role in normal plant development and defense. Full article
(This article belongs to the Special Issue Plant Bioinformatics: Applications and Databases)
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14 pages, 1652 KiB  
Article
Development of SSR Databases Available for Both NGS and Capillary Electrophoresis in Apple, Pear and Tea
by Sogo Nishio, Miyuki Kunihisa, Fumiya Taniguchi, Hiromi Kajiya-Kanegae, Shigeki Moriya, Yukie Takeuchi and Yutaka Sawamura
Plants 2021, 10(12), 2796; https://doi.org/10.3390/plants10122796 - 17 Dec 2021
Cited by 7 | Viewed by 3726
Abstract
Developing new varieties in fruit and tea breeding programs is very costly and labor-intensive. Thus, establishing a variety discrimination system is important for protecting breeders’ rights and producers’ profits. Simple sequence repeat (SSR) databases that can be utilized for both next-generation sequencing (SSR-GBS) [...] Read more.
Developing new varieties in fruit and tea breeding programs is very costly and labor-intensive. Thus, establishing a variety discrimination system is important for protecting breeders’ rights and producers’ profits. Simple sequence repeat (SSR) databases that can be utilized for both next-generation sequencing (SSR-GBS) and polymerase chain reaction–capillary electrophoresis (PCR-CE) would be very useful in variety discrimination. In the present study, SSRs with tri-, tetra- and pentanucleotide repeats were examined in apple, pear and tea. Out of 37 SSRs that showed clear results in PCR-CE, 27 were suitable for SSR-GBS. Among the remaining markers, there was allele dropout for some markers that caused differences between the results of PCR-CE and SSR-GBS. For the selected 27 markers, the alleles detected by SSR-GBS were comparable to those detected by PCR-CE. Furthermore, we developed a computational pipeline for automated genotyping using SSR-GBS by setting a value “α” for each marker, a criterion whether a genotype is homozygous or heterozygous based on allele frequency. The set of 27 markers contains 10, 8 and 9 SSRs for apple, pear and tea, respectively, that are useful for both PCR-CE and SSR-GBS and suitable for automation. The databases help researchers discriminate varieties in various ways depending on sample size, markers and methods. Full article
(This article belongs to the Special Issue Plant Bioinformatics: Applications and Databases)
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Review

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21 pages, 1727 KiB  
Review
Gene Co-Expression Network Tools and Databases for Crop Improvement
by Rabiatul-Adawiah Zainal-Abidin, Sarahani Harun, Vinothienii Vengatharajuloo, Amin-Asyraf Tamizi and Nurul Hidayah Samsulrizal
Plants 2022, 11(13), 1625; https://doi.org/10.3390/plants11131625 - 21 Jun 2022
Cited by 14 | Viewed by 4807
Abstract
Transcriptomics has significantly grown as a functional genomics tool for understanding the expression of biological systems. The generated transcriptomics data can be utilised to produce a gene co-expression network that is one of the essential downstream omics data analyses. To date, several gene [...] Read more.
Transcriptomics has significantly grown as a functional genomics tool for understanding the expression of biological systems. The generated transcriptomics data can be utilised to produce a gene co-expression network that is one of the essential downstream omics data analyses. To date, several gene co-expression network databases that store correlation values, expression profiles, gene names and gene descriptions have been developed. Although these resources remain scattered across the Internet, such databases complement each other and support efficient growth in the functional genomics area. This review presents the features and the most recent gene co-expression network databases in crops and summarises the present status of the tools that are widely used for constructing the gene co-expression network. The highlights of gene co-expression network databases and the tools presented here will pave the way for a robust interpretation of biologically relevant information. With this effort, the researcher would be able to explore and utilise gene co-expression network databases for crops improvement. Full article
(This article belongs to the Special Issue Plant Bioinformatics: Applications and Databases)
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Other

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21 pages, 7495 KiB  
Technical Note
GXP: Analyze and Plot Plant Omics Data in Web Browsers
by Constantin Eiteneuer, David Velasco, Joseph Atemia, Dan Wang, Rainer Schwacke, Vanessa Wahl, Andrea Schrader, Julia J. Reimer, Sven Fahrner, Roland Pieruschka, Ulrich Schurr, Björn Usadel and Asis Hallab
Plants 2022, 11(6), 745; https://doi.org/10.3390/plants11060745 - 11 Mar 2022
Cited by 1 | Viewed by 4877
Abstract
Next-generation sequencing and metabolomics have become very cost and work efficient and are integrated into an ever-growing number of life science research projects. Typically, established software pipelines analyze raw data and produce quantitative data informing about gene expression or concentrations of metabolites. These [...] Read more.
Next-generation sequencing and metabolomics have become very cost and work efficient and are integrated into an ever-growing number of life science research projects. Typically, established software pipelines analyze raw data and produce quantitative data informing about gene expression or concentrations of metabolites. These results need to be visualized and further analyzed in order to support scientific hypothesis building and identification of underlying biological patterns. Some of these tools already exist, but require installation or manual programming. We developed “Gene Expression Plotter” (GXP), an RNAseq and Metabolomics data visualization and analysis tool entirely running in the user’s web browser, thus not needing any custom installation, manual programming or uploading of confidential data to third party servers. Consequently, upon receiving the bioinformatic raw data analysis of RNAseq or other omics results, GXP immediately enables the user to interact with the data according to biological questions by performing knowledge-driven, in-depth data analyses and candidate identification via visualization and data exploration. Thereby, GXP can support and accelerate complex interdisciplinary omics projects and downstream analyses. GXP offers an easy way to publish data, plots, and analysis results either as a simple exported file or as a custom website. GXP is freely available on GitHub (see introduction) Full article
(This article belongs to the Special Issue Plant Bioinformatics: Applications and Databases)
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