Bioinformatics Tools and Genomic Resources Available in Understanding the Structure and Function of Gossypium

Cotton is economically and evolutionarily important crop for its fiber. In order to improve fiber quality and yield, and to exploit the natural genetic potential inherent in genotypes, understanding genome structure and function of cultivated cotton is important. In order to achieve this, a functional understanding of bioinformatics resources such as databases, software solutions, and analysis tools is required. But currently, there are very few unified reports on bioinformatics tools and even fewer repositories to access cotton genomic information. Also, resourceful developers and bioinformatics scientists actively addressing complex genomic challenges in cotton genomes are much in need. The primary goal of this chapter is to provide a review of such tools and resources for analyzing the structure and function of the cotton genome with preferential emphasis on this complex and economically important plant species. This discourse begins with a description of concurrent advances in high‐throughput genome sequencing and bioinformatics analyses and focuses on four major sections covering bioinformatics tools and resources for analysis of: (1) genomes; (2) transcriptomes; (3) small RNAs; and (4) epigenomes. In each section, recent advances in cotton have been discussed. Cotton genome sequencing and annotation efforts are outlined within these sections. This review discusses the availability of genome information of both diploid and tetraploid species that have impelled cotton genome research into the post‐genomics era, opening new avenues for exploring regulatory mechanisms associated with fine‐tuning of gene expression of fiber‐related genes. Finally, the potential impacts of these rapid advances, especially the challenges in handling and analyzing the large datasets are discussed

Genome Editing in Plants: An Overview of Tools and Applications

The emergence of genome manipulation methods promises a real revolution in biotechnology and genetic engineering. Targeted editing of the genomes of living organisms not only permits investigations into the understanding of the fundamental basis of biological systems but also allows addressing a wide range of goals towards improving productivity and quality of crops. This includes the creation of plants with valuable compositional properties and with traits that confer resistance to various biotic and abiotic stresses. During the past few years, several novel genome editing systems have been developed; these include zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and clustered regularly interspaced short palindromic repeats/Cas9 (CRISPR/Cas9). These exciting new methods, briefly reviewed herein, have proved themselves as effective and reliable tools for the genetic improvement of plants

Cheminformatics and artificial intelligence for accelerating agrochemical discovery

The global cost-benefit analysis of pesticide use during the last 30 years has been characterized by a significant increase during the period from 1990 to 2007 followed by a decline. This observation can be attributed to several factors including, but not limited to, pest resistance, lack of novelty with respect to modes of action or classes of chemistry, and regulatory action. Due to current and projected increases of the global population, it is evident that the demand for food, and consequently, the usage of pesticides to improve yields will increase. Addressing these challenges and needs while promoting new crop protection agents through an increasingly stringent regulatory landscape requires the development and integration of infrastructures for innovative, cost- and time-effective discovery and development of novel and sustainable molecules. Significant advances in artificial intelligence (AI) and cheminformatics over the last two decades have improved the decision-making power of research scientists in the discovery of bioactive molecules. AI- and cheminformatics-driven molecule discovery offers the opportunity of moving experiments from the greenhouse to a virtual environment where thousands to billions of molecules can be investigated at a rapid pace, providing unbiased hypothesis for lead generation, optimization, and effective suggestions for compound synthesis and testing. To date, this is illustrated to a far lesser extent in the publicly available agrochemical research literature compared to drug discovery. In this review, we provide an overview of the crop protection discovery pipeline and how traditional, cheminformatics, and AI technologies can help to address the needs and challenges of agrochemical discovery towards rapidly developing novel and more sustainable products

RNA Interference for Functional Genomics and Improvement of Cotton (Gossypium sp.)

RNA interference (RNAi), is a powerful new technology in the discovery of genetic sequence functions, and has become a valuable tool for functional genomics of cotton (Gossypium ssp.). The rapid adoption of RNAi has replaced previous antisense technology. RNAi has aided in the discovery of function and biological roles of many key cotton genes involved in fiber development, fertility and somatic embryogenesis, resistance to important biotic and abiotic stresses, and oil and seed quality improvements as well as the key agronomic traits including yield and maturity. Here, we have comparatively reviewed seminal research efforts in previously used antisense approaches and currently applied breakthrough RNAi studies in cotton, analyzing developed RNAi methodologies, achievements, limitations, and future needs in functional characterizations of cotton genes. We also highlighted needed efforts in the development of RNAi-based cotton cultivars, and their safety and risk assessment, small and large-scale field trials, and commercialisation

GIS Applications in Agriculture

Technological innovations during the recent centuries have enabled us to significantly boost agricultural production to feed the rapidly increasing global population. While advances in digital technologies triggered the onset of the fourth revolution in agriculture, we also have several challenges such as limited cropland, diminishing water resources, and climate change, underscoring the need for unprecedented measures to achieve agricultural resilience to support the world population. Geographic information system (GIS), along with other partner technologies such as remote sensing, global positioning system, artificial intelligence, computational systems, and data analytics, has been playing a pivotal role in monitoring crops and in implementing optimal and targeted management practices towards improving crop productivity. Here we have reviewed the diverse applications of GIS in agriculture that cover the entire pipeline from land-use planning to crop-soil-yield monitoring to post-harvest operations. GIS, in combination with digital technologies and through new and emerging areas of applications, is enabling the realization of precision farming and sustainable food production goals

Wild Relatives of Maize, Rice, Cotton, and Soybean: Treasure Troves for Tolerance to Biotic and Abiotic Stresses

Global food demand is expected to nearly double by 2050 due to an increase in the world's population. The Green Revolution has played a key role in the past century by increasing agricultural productivity worldwide, however, limited availability and continued depletion of natural resources such as arable land and water will continue to pose a serious challenge for global food security in the coming decades. High yielding varieties with proven tolerance to biotic and abiotic stresses, superior nutritional profiles, and the ability to adapt to the changing environment are needed for continued agricultural sustainability. The narrow genetic base of modern cultivars is becoming a major bottleneck for crop improvement efforts and, therefore, the use of crop wild relatives (CWRs) is a promising approach to enhance genetic diversity of cultivated crops. This article provides a review of the efforts to date on the exploration of CWRs as a source of tolerance to multiple biotic and abiotic stresses in four global crops of importance; maize, rice, cotton, and soybean. In addition to the overview of the repertoire and geographical spread of CWRs in each of the respective crops, we have provided a comprehensive discussion on the morphological and/or genetic basis of the traits along with some examples, when available, of the research in the transfer of traits from CWRs to cultivated varieties. The emergence of modern molecular and genomic technologies has not only accelerated the pace of dissecting the genetics underlying the traits found in CWRs, but also enabled rapid and efficient trait transfer and genome manipulation. The potential and promise of these technologies has also been highlighted in this review

Molecular Characterization of Transgenic Events Using Next Generation Sequencing Approach.

Demand for the commercial use of genetically modified (GM) crops has been increasing in light of the projected growth of world population to nine billion by 2050. A prerequisite of paramount importance for regulatory submissions is the rigorous safety assessment of GM crops. One of the components of safety assessment is molecular characterization at DNA level which helps to determine the copy number, integrity and stability of a transgene; characterize the integration site within a host genome; and confirm the absence of vector DNA. Historically, molecular characterization has been carried out using Southern blot analysis coupled with Sanger sequencing. While this is a robust approach to characterize the transgenic crops, it is both time- and resource-consuming. The emergence of next-generation sequencing (NGS) technologies has provided highly sensitive and cost- and labor-effective alternative for molecular characterization compared to traditional Southern blot analysis. Herein, we have demonstrated the successful application of both whole genome sequencing and target capture sequencing approaches for the characterization of single and stacked transgenic events and compared the results and inferences with traditional method with respect to key criteria required for regulatory submissions