Bioinformatics Toolkit for Plant Studies

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 (31 August 2023) | Viewed by 7044

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Guest Editor
1. Department of Biological Sciences, University of North Texas, Denton, TX 76203, USA
2. BioDiscovery Institute, University of North Texas, Denton, TX 76203, USA
3. Department of Mathematics, University of North Texas, Denton, TX 76203, USA
Interests: plants bioinformatics; computational genomics, genome evolution, pathogenomics, metagenomics; gene prediction, structural variation detection, disease gene identification
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Special Issue Information

Dear Colleagues,

Advances in high-throughput technologies, including next-generation sequencing, have revolutionized the field of plant biology, similar to other life sciences fields. These developments were driven by remarkable advances in the field of bioinformatics. Similar to other disciplines, Big Data is becoming increasingly important in plant biology. A significant amount of omics data, including those of genomics, transcriptomics, proteomics, metabolomics, methylomics, interactomics, and phenomics, are now available in various databases across the globe, which has presented opportunities to interrogate these data and gain new insights into plants at the molecular, physiological, and systems levels. A number of bioinformatics tools have already been developed, with many more in the works, to analyze and interpret these data and facilitate a system-level understanding of the processes within plants. These include tools for identifying genes and characterizing their functions in plants; deciphering genotypes or genetic variations, as well as genome-wide associations, that underlie versatile phenotypes of plants; quantifying gene expression under different conditions including in response to biotic and abiotic stresses; elucidating protein–protein, protein–DNA, and protein–metabolite interactions; uncovering molecular networks and signaling mechanisms; and performing large-scale imaging data analysis to advance plant phenomics. Examining plant data at different levels using bioinformatics tools is enabling the development of next-generation technologies to engineer plants for a variety of purposes, including rendering them resilient to climatic changes and breeding crops for a sustainable planet. This Special Issue will highlight current and new bioinformatics tools, both standalone and pipeline based, for plant data analysis and interpretation.

Dr. Rajeev K. Azad
Guest Editor

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Published Papers (4 papers)

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16 pages, 1006 KiB  
Article
Benchmarking RNA-Seq Aligners at Base-Level and Junction Base-Level Resolution Using the Arabidopsis thaliana Genome
by Tallon Coxe, David J. Burks, Utkarsh Singh, Ron Mittler and Rajeev K. Azad
Plants 2024, 13(5), 582; https://doi.org/10.3390/plants13050582 - 21 Feb 2024
Viewed by 1185
Abstract
The utmost goal of selecting an RNA-Seq alignment software is to perform accurate alignments with a robust algorithm, which is capable of detecting the various intricacies underlying read-mapping procedures and beyond. Most alignment software tools are typically pre-tuned with human or prokaryotic data, [...] Read more.
The utmost goal of selecting an RNA-Seq alignment software is to perform accurate alignments with a robust algorithm, which is capable of detecting the various intricacies underlying read-mapping procedures and beyond. Most alignment software tools are typically pre-tuned with human or prokaryotic data, and therefore may not be suitable for applications to other organisms, such as plants. The rapidly growing plant RNA-Seq databases call for the assessment of the alignment tools on curated plant data, which will aid the calibration of these tools for applications to plant transcriptomic data. We therefore focused here on benchmarking RNA-Seq read alignment tools, using simulated data derived from the model organism Arabidopsis thaliana. We assessed the performance of five popular RNA-Seq alignment tools that are currently available, based on their usage (citation count). By introducing annotated single nucleotide polymorphisms (SNPs) from The Arabidopsis Information Resource (TAIR), we recorded alignment accuracy at both base-level and junction base-level resolutions for each alignment tool. In addition to assessing the performance of the alignment tools at their default settings, accuracies were also recorded by varying the values of numerous parameters, including the confidence threshold and the level of SNP introduction. The performances of the aligners were found consistent under various testing conditions at the base-level accuracy; however, the junction base-level assessment produced varying results depending upon the applied algorithm. At the read base-level assessment, the overall performance of the aligner STAR was superior to other aligners, with the overall accuracy reaching over 90% under different test conditions. On the other hand, at the junction base-level assessment, SubRead emerged as the most promising aligner, with an overall accuracy over 80% under most test conditions. Full article
(This article belongs to the Special Issue Bioinformatics Toolkit for Plant Studies)
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10 pages, 2489 KiB  
Communication
Gene Coexpression Analysis Identifies Genes Associated with Chlorophyll Content and Relative Water Content in Pearl Millet
by Harshraj Shinde, Ambika Dudhate, Atul Sathe, Neha Paserkar, Sopan Ganpatrao Wagh and Ulhas Sopanrao Kadam
Plants 2023, 12(6), 1412; https://doi.org/10.3390/plants12061412 - 22 Mar 2023
Cited by 1 | Viewed by 1830
Abstract
Pearl millet is a significant crop that is tolerant to abiotic stresses and is a staple food of arid regions. However, its underlying mechanisms of stress tolerance are not fully understood. Plant survival is regulated by the ability to perceive a stress signal [...] Read more.
Pearl millet is a significant crop that is tolerant to abiotic stresses and is a staple food of arid regions. However, its underlying mechanisms of stress tolerance are not fully understood. Plant survival is regulated by the ability to perceive a stress signal and induce appropriate physiological changes. Here, we screened for genes regulating physiological changes such as chlorophyll content (CC) and relative water content (RWC) in response to abiotic stress by using “weighted gene coexpression network analysis” (WGCNA) and clustering changes in physiological traits, i.e., CC and RWC associated with gene expression. Genes’ correlations with traits were defined in the form of modules, and different color names were used to denote a particular module. Modules are groups of genes with similar patterns of expression, which also tend to be functionally related and co-regulated. In WGCNA, the dark green module (7082 genes) showed a significant positive correlation with CC, and the black (1393 genes) module was negatively correlated with CC and RWC. Analysis of the module positively correlated with CC highlighted ribosome synthesis and plant hormone signaling as the most significant pathways. Potassium transporter 8 and monothiol glutaredoxin were reported as the topmost hub genes in the dark green module. In Clust analysis, 2987 genes were found to display a correlation with increasing CC and RWC. Furthermore, the pathway analysis of these clusters identified the ribosome and thermogenesis as positive regulators of RWC and CC, respectively. Our study provides novel insights into the molecular mechanisms regulating CC and RWC in pearl millet. Full article
(This article belongs to the Special Issue Bioinformatics Toolkit for Plant Studies)
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12 pages, 3818 KiB  
Article
The Enhanced Affinity of WRKY Reinforces Drought Tolerance in Solanum lycopersicum L.: An Innovative Bioinformatics Study
by Sandip Debnath, Achal Kant, Pradipta Bhowmick, Ayushman Malakar, Shampa Purkaystha, Binod Kumar Jena, Gaurav Mudgal, Mehdi Rahimi, Md Mostofa Uddin Helal, Rakibul Hasan, Jen-Tsung Chen and Faizul Azam
Plants 2023, 12(4), 762; https://doi.org/10.3390/plants12040762 - 8 Feb 2023
Cited by 9 | Viewed by 2189
Abstract
In the scenario of global climate change, understanding how plants respond to drought is critical for developing future crops that face restricted water resources. This present study focuses on the role of WRKY transcription factors on drought tolerance in tomato, Solanum lycopersicum L., [...] Read more.
In the scenario of global climate change, understanding how plants respond to drought is critical for developing future crops that face restricted water resources. This present study focuses on the role of WRKY transcription factors on drought tolerance in tomato, Solanum lycopersicum L., which is a significant vegetable crop. WRKY transcription factors are a group of proteins that regulate a wild range of growth and developmental processes in plants such as seed germination and dormancy and the stress response. These transcription factors are defined by the presence of a DNA-binding domain, namely, the WRKY domain. It is well-known that WRKY transcription factors can interact with a variety of proteins and therefore control downstream activities. It aims to simulate the effect of curcumin, a bioactive compound with regulatory capacity, on the protein–protein interaction events by WRKY transcription factors with an emphasis on drought stress. It was found that curcumin binds to WRKY with an energy of −11.43 kcal/mol with inhibitory concentration (Ki) 0.12 mM and has the potential to improve fruit quality and reinforce drought tolerance of S. lycopersicum, according to the results based on bioinformatics tools. The root means square deviation (RMSD) of the C-α, the backbone of 2AYD with ligand coupled complex, displayed a very stable structure with just a little variation of 1.89 Å. MD simulation trajectory of Cα atoms of 2AYD bound to Curcumin revealed more un-ordered orientation in PC1 and PC10 modes and more toward negative correlation from the initial 400 frames during PCA. Establishing the binding energies of the ligand–target interaction is essential in order to characterize the compound’s binding affinity to the drought transcription factor. We think we have identified a phyto-agent called curcumin that has the potential to enhance the drought tolerance. Compared to the part of the mismatch repair-base technique that can be used to fix drought related genes, curcumin performed better in a drop-in crop yield over time, and it was suggested that curcumin is a potential candidate factor for improving drought tolerance in tomatoes, and it needs future validation by experiments in laboratory and field. Full article
(This article belongs to the Special Issue Bioinformatics Toolkit for Plant Studies)
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15 pages, 991 KiB  
Protocol
Integrated Systems Biology Pipeline to Compare Co-Expression Networks in Plants and Elucidate Differential Regulators
by Nilesh Kumar and M. Shahid Mukhtar
Plants 2023, 12(20), 3618; https://doi.org/10.3390/plants12203618 - 19 Oct 2023
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Abstract
To identify sets of genes that exhibit similar expression characteristics, co-expression networks were constructed from transcriptome datasets that were obtained from plant samples at various stages of growth and development or treated with diverse biotic, abiotic, and other environmental stresses. In addition, co-expression [...] Read more.
To identify sets of genes that exhibit similar expression characteristics, co-expression networks were constructed from transcriptome datasets that were obtained from plant samples at various stages of growth and development or treated with diverse biotic, abiotic, and other environmental stresses. In addition, co-expression network analysis can provide deeper insights into gene regulation when combined with transcriptomics. The coordination and integration of all these complex networks to deduce gene regulation are major challenges for plant biologists. Python and R have emerged as major tools for managing complex scientific data over the past decade. In this study, we describe a reproducible protocol POTFUL (pant co-expression transcription factor regulators), implemented in Python 3, for integrating co-expression and transcription factor target protein networks to infer gene regulation. Full article
(This article belongs to the Special Issue Bioinformatics Toolkit for Plant Studies)
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