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17 Omics’ Approaches to Analysis of Stress Response Genes in Medicinal Plants 305
In the past 10 years, there has been an increase in knowledge and research in the
field of systems biology, which combines the massive amounts of data produced by
genomic technologies with computational models to help us understand the topology
and dynamical operations of the molecular systems supporting an organism
(Hammer et al.
2004). The discipline of systems biology research then moves on
with data-driven and hypothesis-driven forward techniques after a model is
projected. The benefit of this is that it brings together descriptive approaches based
on data integration and predictive approaches based on computation simulation and
helps biologists retrieve information from data collected using a variety of omics
technologies and synthesized network models as well as study their response and
pattern of growth in an easy and foretelling way. This method merges biological
research with computer science, mathematics, physics, and chemistry while
narrowing down inconsistencies betw een them. However, whether this field of
study is a dynamic modelling technique or a discipline of multidimensional data
analysis is still the subject of disagreement (Sumner et al.
2003a).
In order to achieve this, we provide an overview of the main bioinformatics data
resources available in support of plant research, specifically on abiotic stress
responses, in this chapter. These resources range from raw collections to complete
databases or platforms and are described in brief after a brief introduction of the main
technologies that are accompanying the production of massive molecular data. We
outline their main characteristics, their value, and a few bottlenecks that show the
need for coordination in the efforts as well as training in data and software handling,
for a suitable exploitation, to support the detection of key structural and functional
features to interpret molecular processes involved in plant responses to abiotic stress
conditions (Jha et al. 2019). We will also describe the open challenges to increase
and improve the opportunities offered by bioinformatics for innovative and effective
research in the field.
17.2 Plant Abiotic Stress: Omics Perspectives
Research conduct ed over the past 20 years has demon strated that a group of genes
that are dynamically up- and downregulated regulate the plant’s response to stress.
As a result, several researchers have used different “omics” methodologies to
acquire an integrated view of how plants react to different abiotic stresses (Agarwal
et al. 2014; Cusido et al. 2014). Omics methods have become crucial instruments for
targeting and interpreting the functioning of plant molecular systems, gaining
insights into biological networks, and advancing translational research (Burgos
et al. 2011). In order to understand how the plant’s biomolecule pool functions
and how it responds to changing environmental conditions, omics techniques
are used.
However genomics originally saw significant advancements, it eventually
became apparent that a more full understanding required an integrative strategy
that included the investigation of other omics levels, such as transcriptional,
proteomic, and metabolic profiles and their flux distributions (Vidal
2009). The
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expression and activity of any gene, along with its interacting partners and
regulators, may all be investigated at any time, thanks to technological
advancements in experimental protocols, data analysis, and visualization tools
(Sussman et al.
2009). Thus, studies on physiologically relevant patterns are now
predominantly “data and knowledge-driven” rather than merely “hypothesis-driven”
as a result of the development of omics-based techniques (Mousavi et al.
2016). Data
mining techniques have also been used to recreate the biomolecular networks for
each omics level as a result of advancements in computational biology.
306 D. Sharma
17.2.1 Plant Genomic Response to Abiotic Stress
A wide variety of abiotic stress responses are induced in plants as a result of
changing environmental conditions, which may impede growth because of altered
transport and metabolic activities. Ion imbalance and hyperosmotic stress are two
more stresses that are predominantly influenced by abiotic stress. Oxidative stress is
a byproduct of abiotic stress because it can lead to an increase in the synthesis and
accumulation of ROS, which is not at all beneficial and may be harmful to plants at
greater concentrations. A severe discrepancy in a cell division is brought about by
this oxidative stress, which leads to significant physiological complications (Burgos
et al.
2011). Apoptosis can occasionally result from excessive ROS production,
which harms biomolecules like proteins, carbohydrates, lipids, and DNA. Instead of
this, the beginning of stress can also activate some early sensors (most of which are
unknown), leading to the activation of the cytoplasmic calcium ions and protein
signaling pathways, which point the way to physi ological changes through the
expression of stress-responsive genes. It is crucial for plants to be exposed to various
environmental stresses in both in vivo (greenhouse, field) and in vitro (tissue
cultures) situations. These stresses include water stress, osmotic and salinit y stress,
temperature stress, wounding, light conditions, and atmospheric factors. Because it
initiates and directs the integration of stress effects and responses at several underly-
ing levels of organization over space and time, the plant response is uncommon and
complicated (Blum
1996).
17.2.1.1 The Impact of Drought Stress on Gene Expression
Numerous genes that are regulated by drought exposure in plants have been discov-
ered. A variety of methods, including gene expression patterns and transgenic plants,
are being used to gain a deeper understanding of the precise function of these genes
and their part in plants’ response to water scarcity. Numerous physiological factors
that shape a plant’s response to drought stress, including as root size and depth,
phenology, hydraulic conductivity, and the storage of reserves, are typically tied to
the plant’s structure and development and less stress-induced than constitutive (Jha
et al. 2019).
It has been hypothesized that VSP2 responds to hot temperatures, oxidative
stress, insects, aridity, salinity, and injury. When exposed to all 9 types of stress
(cold, osmotic stress, salt, drought, genotoxic stress, ultraviolet light, oxidative
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o
stress, wounding, and high temperature), 67 genes were observ ed to respond, but
RHL41 (At5g59820) only showed signs of being responsive to salinity, wounding,
heat, cold, oxidative stress, light stimulus, and chitin. Arabidopsis’ stress-responsive
genes have recently been found using cutting-edge microarray technology (Swindell
2006).
17 Omics’ Approaches to Analysis of Stress Response Genes in Medicinal Plants 307
17.2.1.2 Expression of Genes in Response to Salt Stress
There is ample evidence to support the idea that plants’ gene expression changes as a
result of salt exposure. Numerous salt-responsive genes have been discovered and
studied over the past ten years. From extremely high plants (Beta vulgaris)t
extremely low plants (Citrus spp.), salinity tolerance is present in a wide variety of
plants. The discovery of salt-tolerant genes in salt-tolerant organisms points the way
toward a better understanding of the mechanisms that set them apart from their salt-
sensitive counterparts (Rabbani et al.
2003). A number of different approaches have
been undertaken to identify genes whose expression is influenced by salinity in
plants. The identification and isolation of genes whose expression is affected by
salinity have been accomplished using a wide range of approaches.
The majority of these experimental designs include screening cDNA libraries that
were obtained from plant cells that have undergone salt treatmen t. One of the more
effective techniques for isolating salt-responsive cDNA clones from these libraries is
differential screening with probes produced from mRNA recovered from plant tissue
that has been both stressed by salt and has not been stressed by salt. To boost salt
tolerance, modern yeast mutants that are sensitive to salt use cDNAs that encode
proteins (Pulla et al. 2009). The transcriptome analysis also found unique stress-
inducible genes, revealing some variations between Arabidopsis and rice in terms of
their response to stress (Rabbani et al.
2003). The prediction toward the transcription
of a precursor protein of 307 amino acid residues may share high homology.
17.2.1.3 Nutritional Stress-Induced Gene Expression
The quantity of nutrients and minerals available obstructs normal plant growth and
development.
To develop high nutritional potency is therefore one of the most
important aims of plant breeding. Arabidopsis shoots and roots underwent transcrip-
tion analysis and microarray investigations to examine the relationship between
6172 genes and the macronutrients (nitrogen and phosphorus) and/or microelements
(iron or zinc) linked with P deficiency; almost 1835 genes, or almost 30% of these
genes, were reported to demonstrate up- or downregulation by twofold or more in the
first 72 h of phosphorus shortage. A greater number of genes are downregulated in
the root system (296 genes were specifically inhibited, and 141 were specifically
upregulated), but the opposite was true in the shoot system (617 downregulated and
488 upregulated genes) (Wu et al.
2003). It’s important to note that an adaptive
reprogramming unquestionably prefers downregulated genes to upregulated genes.
Transcription factors, macromolecule kinases, and phosphoinositide metabolism-
related enzymes are all included in expression and signal transduction pathways
(Gupta and Kaur
2005). For instance, immunological homologues of rice SAP
104 have been found in numerous species, including monocots and dicots. SAP
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104 is a 104-kDa macromolecule that accumulates in rice seedlings in response to
several abiotic stress situations. It is possible to predict that stressed plants will
require a robust protein turnover system that can break down stress-damaged and
environmentally regulated proteins.
308 D. Sharma
17.2.1.4 Expression of Genes in Response to Heavy Metal Stress
There are still a lot of opportunities to learn about the functional translated regions of
a plant genome, despite the fact that most of the advances in the study of gene
expression done at the mRNA levels have shared a depth understanding of plants’
response to heavy metals. Due to the carcinogenic nature of metals, several cellular
regulatory proteins or signaling proteins involved in cell development and apoptosis
were chosen as targets. The expression of genes involved in DNA repair, immune
system function, limiting the growth of injured cells and inducing apoptosis was
caused by some factor (Valko et al. 2005). According to numerous earlier
researchers, the majority of toxic stressors are brought on by exposure to heavy
metals, which alters gene expression by modulating the activities of genes including
NF-kB, AP-1, and p53.
As a result of elevated levels of copper and cadmium ions in the soil, plants under
heavy metal stress respond through various mitogen-activated protein kinase
(MAPK) pathways. For instance, it was discovered that the activation of four
different MAPKs—SIMK, MMK2, MMK3, and SAMK—occurred in lucerne
(Medicago sativa) seedlings when they were exposed to increased concentrations
of copper or cadmium ions. In eukaryotes, the synthesis of phytochelatins, which are
tiny metal-binding peptides that encode phytochelatin synthases, is induced by
environmental stress (Ortiz et al. 1992; Clemens et al. 1999). For the purpose of
enhancing tolerance for heavy metal stress, phytochelatins also sequestered
phytochelatin-metal complexes into the vacuoles. Cadmium tolerance is provided
by phytochelatin, which is mediated by the tonoplast-localized Abc2 transporter and
accumulates in vacuoles. In response to e xposure to the heavy metal cadmium (Cd),
the expression of the serine acetyltransferase (SAT) gene family was investigated in
Arabidopsis thaliana. When exposed to Cd, the leaf lamella, trichomes, root, and
stem cortex all showed increased expression of all SAT genes at various levels
(Varshney et al.
2009).
Structural Genomics
The term “structural genomics” refers to the high-throughput determination and
analysis of the three-dimensional structures of biological macromolecules, currently
focused mainly on specific protein domains. By determining prote in structures,
structural genomics is revolutionizing how we study and comprehend biological
systems by shedding light on an organism’s biology and life cycle at the molecular
level (Goldsmith-Fischman and Honig
2003). Molecular markers are necessary for
structural genomics because they can be used to map and tag genes of interest for
later use in crop breeding programs. The marker techniques can be divided into
groups called classes. Restrictions fragment length polymorphisms (RFLP) are one
among the first non-PCR-based approaches. Through hybridizing a labelled DNA
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probe to a southern blot of DNA that has been digested by restriction enzymes and
producing a differential DNA fragment profile, restriction fragment length polymor-
phism can detect DNA polymorphism (Agarwal et al. 2008).
17 Omics’ Approaches to Analysis of Stress Response Genes in Medicinal Plants 309
Quantitative trait loci (QTL)
mapping and genome-wide association studies
(GWAS) are methods that are used to comprehend and investigate the many qualities
in crops. A statistical technique called quantitative trait loci mapping helps to
connect complicated phenotypes and genotypes, which are two different types of
data. For mapping QTLs, molecular markers like SNPs and AFLPs are frequently
used, and these can subsequently be connected with observed phenotypic data.
GWAS, however, might discover variations linked to characteristics. Based on
SNPs in the sequence data, genome-wide association studies may also find
correlations between the genetic variants/phenotypes in a population of any species
(Wang and Qin 2017).
Functional Genomics
The term “functional genomics” refers to the creation of extensive experimental
methods to evaluate gene function. To find and isolate genes of interest, clone and
characterize those genes, and overexpress or knock out lines for functional trans-
genic investigations, a variety of biotechnological methods have been created
(Muthamilarasan et al.
2019). Prior to genome sequencing techniques, laborious
processes like suppression subtractive hybridization (SSH), expressed sequence tag
(EST), and cDNA-AFLP-sequencing were required to identify candidate genes.
With the development of NGS, these methods’ tediousness subsequently diminished
(Muthamilarasan and Prasad
2017).
The genes responsible for stress tolerance, disease resistance, and yield control
have been identified thanks to the availability of crop genome sequencing. Addi-
tionally, using genome editing technologies like the clustered regularly interspaced
short palindromic repeats (CRISPR/Cas9 system) and transcription activator-like
effector nuclease (TALEN), real genome engineering has been proposed to enhance
crops (Rinaldo and Ayliffe
2015). By introducing pest and disease resistance in
genetically modified crops, genome editing technologies without the insertion of
foreign DNA may increase yield. A bread wheat mildew resistance locus o (TaMlo)
mutant was created using the TALEN and CRISPR/Cas9 technologies (Huang et al.
2014). Similar to this, the SlMlo mutant was produced with tomato crop using the
same method (Nekrasov et al.
2017).
Bioinformatics is essential for supporting novel developments in plant improve-
ment. As a recent illustration, the CRISPR/Cas9 method of genome editing, which
can also be used for applications in abiotic stressors, heavily depends on readily
available genomics knowledge and the right development of bioinformatics tools.
For example, the web-based programs, E-CRISP, TIDE, CHOPCHOP, and CCTop,
have all been developed to predict and identify CRISPR/Cas9 sites for genome
editing (Doudna et al. 2014; Osakabe et al.
2016; Heigwer et al. 2014).
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310 D. Sharma
17.3 Transcriptomics
A clear grasp of how plants react to abiotic stressors is made possible through
transcriptome profiling research. Plant genomic resources have recently been
improved by transcriptome analysis supported by next-generation sequencing
(NGS), RNA-seq for sRNAs, and their signi ficance in genomics research. There
were discovered 5,364 sets of probes with variable expression (Ercolano et al.
2014).
Sixteen shared genes in four monocot plants—switch grass, rice, wheat, and
maize—have been discovered by studying transcriptomes under heat stress. They
can be used as valuable biomarkers for identifying genotypes of heat-sensitive plants
because the majority of them were linked to protein folding processes. In transgenic
rice plants created through homologous recombination, endogenous genomic
OsIRE1 (ER stress sensor/transducer) was replaced by missense alleles that were
deficient in ribonuclease activity. Wakasa and coworkers studied the expression
profiles in these transgenic rice plants using RNA sequencing. These findings point
the way toward uncovering important data regarding the ER stress response and the
identification of novel ER stress-related genes (Heigwer et al.
2014).
Through in situ RNA-seq (also known as in situ ligation), RNA is sequenced in
living cells or tissues, leading to novel developments in transcriptomics. Another
method, known as spatially resolved transcriptomics, uses spatial information to
detect gene expression in cells or tissues in order to provide an extensive molecular
description of biological processes in creatures (Burgess
2015). Foxtail millet and
sweet potato (Ipomoea batatas L.) crops have revealed tissue-specific expression in
response to abiotic and biotic stress by several RNA-seq analyses (Bonthala et al.
2016; Hittalmani et al. 2017). During the growth of the cowpea (Vigna unguiculata
L. Walp.), total RNAseq has revealed DE genes and SSR markers. In order to create
genic-SSR markers that can be coupled to phenotypic features associated with the
candidate genes, RNA-seq has therefore proven to be one of the superior
transcriptomics approaches. The identification of genes that are responsive to
drought stress has also been the subject of numerous research using the RNA-seq
approach in rice, maize, and rapeseed oil (Bhardwaj et al. 2015).
Transcriptomics is a potent method for accurately visualizing a target cell or
tissue as well as quantifying gene expression. In order to improve crop breeding,
transcriptomics correctly identifies the gene regulatory mechanism and candidate
genes that provide abiotic stress resistance in legumes (Afzal et al.
2020). Due to
cutting-edge technologies like high-throughput analysis, serial analysis of gene
expression (SAGE) and microarrays may be utilized to discover substantial
transcriptome information.
A comparative transcriptome approach has been used to document the transcrip-
tional changes in both drought-tolerant and drought-sensitive soybean cultivars
(Wang et al. 2018). Single-nucleotide polymorphisms (SNPs) markers that were
obtained from Sanger sequencing and Illumina’s GoldenGate method were used to
evaluate paternal polymorphism, functional and molecular genomic data association
in various genotypes of common beans that are susceptible to biotic and abiotic
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conditions such as heat, aluminum toxicity, low phosphorous, and drought (Blair
et al. 2013).
17 Omics’ Approaches to Analysis of Stress Response Genes in Medicinal Plants 311
17.4 Proteomics
Sequence, structural, functional, and expression proteomics are the four
subcategories of proteomics, a technique used to profile all expressed proteins in
an organism (Aranzana et al.
2019; Aizat and Hassan 2018 ). By employing high-
performance liquid chromatography, sequence proteomics may identify the amino
acid sequences that are typically recognized sequentially. In structural proteomics,
the structure of proteins is studied to determine their alleged activities. Numerous
methods, including computer-based modelling and experimental techniques like
nuclear magnetic resonance (NMR), crystallization, electron microscopy, and
X-ray diffraction of protein crystals, can be used to analyze structural proteomics
(Woolfson 2018; Sali et al. 2003). The activities of a protein are determined by
functional proteomics, and these functions are investigated using a variety of
techniques, including protein microarray profiling and yeast-one or yeast-two
hybrids (LaBaer and Ramachandran
2005).
The rapid development of plant proteome research, both at the sample and
genome-wide scales, has been facilitated by advances in protein extraction and
separation. The methods used in conventional proteomics are chromatography-
based and include affinity, size exclusion, and exchange chromatography. For the
study of particular proteins, however, western blotting and ELISA could be utilized.
Later, some more complex methods for the separation of proteins were developed
and used using gel-based techniques, including SDS-PAGE, two-dimensional gel
electrophoresis (2-DE), and two-dimensional differential gel electrophoresis
(2D-DIGE) (Nakagami et al. 2012). Protein microarrays/chips have been developed
for the simultaneous detection of small amounts of protein sample in order to do fast
protein expression analysis.
The iTRAQ approach in quantitative proteomics has shown the differential
expression of proteins in the potato crop under abiotic stress. Numerous stress-
responsive DEPs were recently discovered in two kinds of coconuts using a compar-
ative proteome analysis based on iTRAQ (Gu et al.
2015; Wang et al. 2020).
Additionally, iTRAQ-based proteomic research has shed fresh light on cotton crop
somatic embryogenesis. Therefore, quantitative proteomic investigations based on
iTRAQ are useful for protecting crops from abiotic stressors (Zhu et al.
2018). One
proteome investigation found 75 differentially expressed proteins (DEPs) in a
cultivar of chickpeas that is sensitive to dehydration using LC-MS/MS. The bulk
of DEPs functioned as ROS catabolic enzymes, cell signaling, gene transcription,
and molecular chaperons (Subba et al.
2013). DEPs connected to abiotic stress
responses in chickpea have also been discovered using comparative proteomics
and gene expression investigations utilizing 2-DE and LC-MS/MS. A significant
number of nodule proteins were discovered in Medicago spp. in response to drought
stress by using non-gel-based LC-MS/MS methods. (Arefian et al.
2019).
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312 D. Sharma
The ProteomicsDB, the PRoteomics IDEntifications (PRIDE) database,
PeptideAtlas, and the Global Proteome Machine Database (GPMDB) are actually
the four main public databases created for proteomics data. Unfortunately,
experiments on plant species are not included in PeptideAtlas. On the other hand,
PRIDE now includes 308 proteomics studies on various plant species, 72 of which
are concerned with the plants’ reactions to environmental stress. Although GPM DB
also has proteomics data from plant species, the web interface does not provide
searching for lists of data; thus we are unable to refer to the number of abiotic stress-
related investigations that are currently being conducted (Craig et al. 2004; Vizcaíno
et al.
2013).
Due to a lack of funding, Peptidome and Tranche, two other reference materials,
ceased to exist. This highlights the challenges associated with fostering and sustain-
ing data sharing in the proteomics sector. Despite only including studies on
Arabidopsis and maize, the Plant Proteomics Database includes a number of proteo-
mics experiments and tools for comparative and subcellular proteomics, metabolic
pathways, etc. It’s intriguing because it enables the search and retrieval of certain
proteins associated with the particular abiotic stress type, such as ozone, cold, and
drought (Slotta et al. 2009; Sun et al. 2009).
Public access is provided to more specialized databases, which are usually
centered on particular protein classes. ARAMEMNON among them enables the
discovery of a number of stress-related integral membrane proteins in monocots
(maize, banana, rice, and Brachypodium) and dicots (Arabidopsis, poplar, grape,
tomato, and muskmelon) (Kleffmann et al.
2006). The latter includes plant subcel-
lular proteome databases that have been generated at the plastid (Plprot;
AT_CHLORO) and cell wall (WallProtDB) levels, although they do not permit
one to perform ad hoc searches for abiotic stress-specific peptides. PMTs alterations,
which have been shown to play a fundamental role in the abiotic stress response, are
covered by the latter resources (San Clemente and Jamet
2015).
The analysis of modifications in stress-induced phosphorylated proteins in six
plant species (Arabidopsis, rapeseed, soybean, barrelclover, rice, and maize) is made
possible by the Plant Phosphorylation database (P3DB) (Yao et al.
2012). In addition
to retrieving data about kinase substrates, NetPhos 3.1 and PhosPhat, which were
first introduced in 1999 and 2007, respectively, and later implemented in 2004 and
2010, respectively, allow for the prediction of the pS, pT, and pY sites. They are also
connected with a number of related databases (Pfam and MapMan), which allow for
the inference of data regarding the localization of experimental and predicted
phosphorylation sites to known domains (Durek et al. 2010).
17.5 Metabolomics
Metabolomics has recently expanded plant stress studies’ horizons and has
established itself as a crucial tool for understanding the molecular processes that
underlie stress responses. When a plant is exposed to challenges, its metabolism is
altered in order to preserve metabolic homeostasis and produce chemicals that can
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withstand the shocks. Recent advancements in systems biology and metabolomics
have provided precise knowledge on the essential elements of metabolic pathways.
The accumulation of various metabolites, including oligosaccharides from the raffi-
nose family and amino acids like gamma amino butyrate (GABA), proline, and
tricarboxylic acid (TCA) cycle metabolites, which are known to be produced in
response to drought stress in plants, has been discovered by metabolom
ics studies of
A. thaliana plants under drought stress (Weckwerth and Kahl
2013)
.
17 Omics’ Approaches to Analysis of Stress Response Genes in Medicinal Plants 313
Mutant and transcriptome
approaches have been used to reveal ABA-dependent
transcriptional regulation, which is in charge of activating numerous stress-related
metabolic pathways (Urano et al.
2009). When treated to modest osmotic stress,
metabolite p rofiling of A. thaliana leaves has shown a temporal differentiation.
Proline, erythritol, and putrescine are examples of typical drought-responsive
genes that were expressed in mature leaves in correlation with the transcriptional
response and subsequently decreased in expanding leaves (Witt et al.
2012).
The generation of metabolic profiles for a specific plant sample requires the use of
separation and analytical techniques since metabolites have diverse chemical and
physical properties. Multiple analytical techniques have been empl oyed in plant
systems for determining metabolites including thin layer chromatography (TLC),
gas/liquid chromatography-mass spectrometry (GC/LC-MS), liquid
chromatography-electrochemistry-mass spectrometry (LCEC-MS), NMR, direct
infusion mass spectrometry (DIMS), Fourier-transform infrared (FT-IR), and capil-
lary electrophoresis liquid-chromatography mass spectrometry (CE-MS) (Wienkoop
et al. 2008). Plant metabolomics primarily use the CE-MS, GC-MS, LC-MS, and
NMR methodologies (Jogaiah et al. 2013). The approach’s selectivity, sensitivity,
speed, and accuracy are all important factors in these procedures. NMR is quick and
precise, but mass spectrometry methods (CE-MS, GC-MS, and LC-MS) are believed
to take longer but are still suitable for sensitive and precise analysis (Sumner et al.
2003b).
Despite not being specifically for abiotic stress and the plant field, useful and
frequently updated resources include the Human Meta bolome Database (HMD),
which is divided into four sections: DrugBank, T3DB, SMPDB, and FooDV. It
contains both polar and nonpolar compounds and is set up in metabocards that are
hyperlinked to other databases (KEGG, PubChem, MetaCyc, ChEBI, PDB, UniProt,
and GenBank); Metlin, additionally, has links and details for each of their 960,000
compounds, including their systematic name, structure, elemental formula, mass,
CAS number, other database link (KEGG, HMDB, PubChem, etc.), economic
viability, etc. Massbank, the first publicly accessible database of mass spectra of
known and unidentified compounds, offers a wide range of search options b ased on
the name, exact mass, formula, and type of MS platform as well as the type of MS
filter (MS, MS/MS, MS3, etc.) (Wishart et al. 2018; Guijas et al. 2018).
The Knapsack family database is located in the same environment, though it is
not specifically related to abiotic stress responses. In this database, metabolite data
(search according to the accurate mass, molecular formula, metabolite name, or MS
ionization mode) are closely linked to data on the species and the area in which they
have been found, as well as their biological activities, an aspect of interest for the
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study of many biological processes, including abiotic stress responses; as further
implementation, the 3D structure for each compound have been included in the
Knapsack DB using the Merck Molecular Force Field (MMFF94), providing novel
opportunity for the identification of new and potentially unexpected binding sites for
target proteins by docking studies or for the estimation of biological activities using
3D-QSAR (Horai et al.
2010; Nakamura et al. 2014
)
314 D. Sharma
17.6 Bioinformatics
Utilizing computational technologies to manage and examine biological data is
known as bioinformatics. The interdisciplinary area of bioinformatics includes
computer science, statistics, arithmetic, engineering, molecular biology, and bio-
technology. Biological questions can be interpreted using computational tools with
the aid of bioinformatics (Nakamura et al.
2013). Notably, the integration of omics
methods (i.e., genomes, trans criptomics, proteomics, and metabolomics) has
improved our comprehension of the molecular mechanisms underlying plant
responses to abiotic stress. The base for gathering data for plant abiotic stressors is
provided by bioinformatics, which integrates with these omics techniques. As a
result, bioinformatics is essential for data organization and mining in order to enable
various omics technologies (Raza et al. 2019). Additionally, bioinformatics analyses
data offered by such reliable technologies regarding the functioning system of genes.
By combining various omics technologies, bioinformatics also offers readily avail-
able resources for computational modelling and simulation analysis. The analysis of
the multi-omics techniques in agricultural science has been carried out using bioin-
formatics tools utilizing different software programs.
Additionally, the researchers studying the plants must have access to and be able
to process the generated high-throughput data. As a result, there exist numerous
databases and repositories online for the storing of biological data. Similar databases
include QlicRice , STIFDB2, RiceSRTFDB, PSPDB, miRBase, PNRD, DroughtDB,
PlantPReS, plant ontology, PtRFd, and Stress2TF for recording data regarding plant
stress (Ambrosino et al.
2018; Shameer et al. 2019).
Software for specific omics analyses, such as phenomics, proteomics, and
metabolomics, is available, and some of these programs a re also helpful for multi-
omics analyses. LemnaLauncher, BioLeaf, and EasyPCC are specifically utilized for
phenomics investigation of crops. Three significant software programs (i.e.,
STRING, SPPS, and ProteinProspector) offer crucial information for understanding
proteome function and relationships. Gromacs and PTools are important multi-
omics-based software for many crop species. GSDS, GAP4, and VISTA are avail-
able techniques for agricultural omics that can be used to analyze structural and
comparative genomes. Ionome-integrated metabolic component analysis in crop
species could also be performed using AMDIS and SIMCA-P 14.0 (Borkotoky
et al.
2013).
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