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Drought and blast disease are the two major abiotic and biotic factors that severely reduce grain-yield (GY) of rice in rainfed-upland. Reproductive stage drought stress (RSDS) along with blast account for > 80% yield loss in rice. Identification of molecular markers for QTLs/genes (GY under RSDS and blast resistance) has opened up new avenues for developing new rice varieties with enhanced resistant/tolerant to these stresses. The current study focussed on introgression of QTL for GY under RSDS (qDTY12.1) and blast resistance gene Pi9 into an upland rice variety Nagina-22 (N-22). Foreground selection was carried out using previously reported markers corresponding to the QTL/gene. Background selection was carried out using 48 uniformly distributed SSRs. Finally, 27 BC2F3 lines (BLs), homozygous for qDTY12.1 and Pi9 were identified with background recovery of 87.5- 93.75%. These BLs were categorised into “tall” and “short” groups based on their plant height. Screening for blast resistance showed that all the BLs were highly resistant to blast [Standard Evaluation System (SES): 2–3], unlike the recurrent parent N-22 (SES = 6), which was susceptible. Similarly, these BLs were significantly tolerant to drought in comparison to N-22 (average yield advantage of 705kg/ha over N-22). Study of genetic parameters showed that five traits, namely, GY, DTF, PH, FGPP and SF% recorded high H2 values (> 60%) along with high GAM values (> 20%) (stress and non-stress), thereby strongly indicating the effectiveness of direct selection of these traits under contrasting moisture regimes. Correlation analysis indicated that DTF recorded positive and negative correlation with GY under non-stress and drought, respectively. Additionally, GY was positively correlated with PH, FGPP and SF%. Thus, plants having high FGPP, high SF% with short DTF and moderate PH may be selected for higher grain yield under RSDS. Thus, based on these criteria, the “short” BLs were found to be most suitable for farmer’s field and further selected for varietal trials.

Rice response to abiotic stresses is generally understood through comparison of gene expression and gene mapping between contrasting tolerant and susceptible genotypes. Machine learning uses large datasets and trains the data through building efficient models for pattern identification and feature importance (gene) prediction. This work explores the potential of using machine learning tools in identifying gene expression models common to different abiotic stress response in rice. For this, 146 rice microarray gene expression samples comprised of drought (57), salinity (35), cold (33) and heat (20) were categorized into two classes namely control, stress and analyzed using supervised (Random forest) and unsupervised (Cluster) machine learning algorithms. The best random forest trained model showed an accuracy of ~ 79% in the classification analysis with an ROC curve area of 0.7963. Besides, genes involved in ABA pathway, flowering, and secondary metabolites (Two PP2Cs, three expressed proteins, Flowering Promoter Factor-like, Terpene Synthase, Gamete EXpressed) were identified as common set of genes for multiple abiotic stress response in rice. Moreover, validation of these eight genes through q-PCR under vegetative stage drought stress identified intrinsic drought tolerance mechanism and drought responsiveness in drought tolerant and rainfed upland, aerobic, and irrigated rice varieties.

Rice sheath blight (ShB) disease, caused by the fungal pathogen Rhizoctonia solani AG1-IA, is one of the devastating diseases and causes severe yield losses all over the world. No completely resistant germplasm is known till now, and as a result, the progress in resistance breeding is unsatisfactory. Basic studies to identify candidate genes, QTLs, and to better understand the host-pathogen interaction are also scanty. In this study, we report the identification of a new ShB-tolerant rice germplasm, CR 1014. Further, we investigated the basis of tolerance by exploring the disease responsive differentially expressed transcriptome and comparing them with that of a susceptible variety, Swarna-Sub1. A total of 815 and 551 genes were found to be differentially regulated in CR 1014 and Swarna-Sub1, respectively, at two different time points. The result shows that the ability to upregulate genes for glycosyl hydrolase, secondary metabolite biosynthesis, cytoskeleton and membrane integrity, the glycolytic pathway, and maintaining photosynthesis make CR 1014 a superior performer in resisting the ShB pathogen. We discuss several putative candidate genes for ShB resistance. The present study, for the first time, revealed the basis of ShB tolerance in the germplasm CR1014 and should prove to be particularly valuable in understanding molecular response to ShB infection. The knowledge could be utilized to devise strategies to manage the disease better.

Rice is an important source of calorie for the growing world population. Its productivity, however is affected by climatic adversities, pest attacks, diseases of bacterial, viral and fungal origin and many other threats. Developing cultivars that are high yielding and stress resilient seems a better solution to tackle global food security issues. This study investigates the potential resistance of 24 rice cultivars against Xanthomonas oryzae pv. Oryzae (Xoo) infection that causes bacterial leaf blight disease and submergence stress. Bacterial leaf blight (BLB) resistance genes (Xa4, xa5, xa13, Xa21, Xa38) and submergence tolerance (Sub1) gene specific markers were used to determine the allelic status of genotypes. The results displayed presence of Xa4 resistance allele (78.95%), xa5 (15.79%) but xa13 and Sub1 tolerance allele were not found in any genotype. However, a new allele for Xa21 (84.21%) and Xa38 (10.52%) were identified in several genotypes. Phenotypic screening for both stress conditions was done to record the cultivars response. None of the genotypes showed resistance against Xoo, although varieties viz., Tapaswini and Konark showed moderate susceptibility. Likewise, survival percentage of genotypes under submergence stress varied from 0 to 100%. Tolerant checks FR13A (100%) and Swarna Sub1 (97.78%) exhibited high survival rate, whereas among genotypes, Gayatri (57.78%) recorded high survivability even though it lacked Sub1 tolerant its genetic background. A total of six trait specific STS and two SSR markers generated an average of 2.38 allele per locus. Polymorphism information content (PIC) value ranged from 0.08 to 0.42 with an average of 0.20. Structure analysis categorized 24 genotypes into two sub-populations, which was in correspondence with Nei’s genetic distance-based NJ tree and principal co-ordinate analysis (PCoA). Swarna Sub1 could be differentiated clearly from BLB resistant check, IRBB60 and other 22 genotypes without having Sub1 gene. Analysis of molecular variance (AMOVA) revealed more genetic variation within population than among population. Principal component analysis (PCA) showed that 9 morphological traits collectively explained 76.126% of total variation among all the genotypes studied. The information from this study would be useful in future breeding programs for pyramiding trait specific genes into high yielding cultivars that fall behind with respect to stress resilience. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12298-021-00951-1.

Seed vigor is the key performance parameter of good quality seed. A panel was prepared by shortlisting genotypes from all the phenotypic groups representing seedling growth parameters from a total of 278 germplasm lines. A wide variation was observed for the traits in the population. The panel was classified into four genetic structure groups. Fixation indices indicated the existence of linkage disequilibrium in the population. A moderate to high level of diversity parameters was assessed using 143 SSR markers. Principal component, coordinate, neighbor-joining tree and cluster analyses showed subpopulations with a fair degree of correspondence with the growth parameters. Marker–trait association analysis detected eight novel QTLs, namely qAGR4.1, qAGR6.1, qAGR6.2 and qAGR8.1 for absolute growth rate (AGR); qRSG6.1, qRSG7.1 and qRSG8.1 for relative shoot growth (RSG); and qRGR11.1 for relative growth rate (RGR), as analyzed by GLM and MLM. The reported QTL for germination rate (GR), qGR4-1, was validated in this population. Additionally, QTLs present on chromosome 6 controlling RSG and AGR at 221 cM and RSG and AGR on chromosome 8 at 27 cM were detected as genetic hotspots for the parameters. The QTLs identified in the study will be useful for improvement of the seed vigor trait in rice.

Rice varieties are generally bred for higher yield but may possess genomic regions conferring tolerance to abiotic stresses. Climate change driven heat stress during reproductive stage of the crop affects spikelet fertility and yield. Though genetic regions associated with heat stress tolerance have been identified in rice, but response of rice varieties and allelic phenotypic effect favoring spikelet fertility during heat stress has not been comprehensively studied. Hence, the present study aimed at assessing the response of 198 rice varieties during the dry season (2016) followed by validation of selected 67 varieties in the second dry season (2017) through staggered sowing for high temperature. The analysis showed mean spikelet sterility of 21.82% and 33.81% for the first and second sowing, respectively. Further, average difference in spikelet sterility for unit increase in maximum temperature during the flowering period was observed to be 7.93%. Employment of nine heat stress associated markers for genetic analysis identified four sub-populations in the 67 varieties inferred through neighbor-joining phylogenetic tree and sub-structure analysis. Marker-trait association analysis showed two markers namely RM205, RM242 were significantly associated with spikelet sterility with phenotypic variance (R2) of 7.7% and 6.0%, respectively. Allelic phenotypic effect of favorable alleles for both the markers reduced spikelet sterility by 14.49% compared to mean spikelet sterility (33.81%). Furthermore, four rice varieties showed spikelet sterility

This article comments on:Nisler J, Kopečný D, Pěkná Z, Končitíková R, Koprna R, Murvanidze N, Werbrouck SPO, Havlíček L, De Diego N, Wimmer Z, Briozzo P, Moréra S, Zalabák D, Spíchal L, Strnad M. 2021. Diphenylurea-derived cytokinin oxidase/dehydrogenase inhibitors for biotechnology and agriculture. Journal of Experimental Botany 72, 355–370.

Rice grain yield is drastically reduced under low light especially in kharif (wet) season due to cloudy weather during most part of crop growth. Therefore, 50–60% of yield penalty was observed. To overcome this problem, identification of low light tolerant rice genotypes with a high buffering capacity trait such as photosynthetic rate has to be developed. Sedoheptulose-1,7 bisphosphatase, a light-regulated enzyme, plays pivotal role in the Calvin cycle by regenerating the substrate (RuBP) for RuBisCo and therefore, indirectly regulates the influx of CO2 for this crucial process. We found a potential role of SBPase expression and activity in low light tolerant and susceptible rice genotypes by analyzing its influence on net photosynthetic rate and biomass. We observed a significant relationship of yield with photosynthesis, SBPase expression and activity especially under low light conditions. Two tolerant and two susceptible rice genotypes were used for the present study. Tolerant genotypes exhibited significant but least reduction compared to susceptible genotypes in the expression and activity of SBPase, which was also manifested in its photosynthetic rate and finally in the grain yield under low light. However, susceptible genotypes showed significant reduction in SBPase activity along with photosynthesis and grain yield suggesting that tracking the expression and activity of SBPase could form a simple and reliable method to identify the low light tolerant rice cultivars. The data were analyzed using the Indostat 7.5, Tukey–Kramer method through Microsoft Excel 2019 and PAST4.0 software. The significant association of SBPase activity with the grain yield, net assimilation rate, electron transfer rate, biomass and grain weight were observed under low light stress. These traits should be considered while selecting and breeding for low light tolerant cultivars. Thus, SBPase plays a major role in the low light tolerance mechanism in rice.

Rice serves as one of the essential staple food for half of the global human population. However, due to rapid human population growth, there is an increase in food demand across the globe. Thus, to lessen the gap between food demand and supply, there is an urgent requirement for grain yield enhancement in various important cereals crops, including rice. In the present study, the authors attempted to characterize haplotypes and single nucleotide polymorphisms associated with Gn1a for high grain number formation in rice plants. Result obtained reveals that high grain number gene sequences are under balancing selection and four high grain number specific missense SNPs decreases the stability of Gn1a. Earlier studies have also suggested that decreases Gn1a expression causes cytokinin accretion in inflorescence meristems, which in turn led to increase in grain yield. Hence, these four SNPs may be utilized for increasing grain yield in rice plants.

Continuous rise in the human population has resulted in an upsurge in food demand, which in turn demand grain yield enhancement of cereal crops, including rice. Rice yield is estimated via the number of tillers, grain number per panicles, and the number of spikes present per panicle. Marker-assisted selection (MAS) serve as one of the best ways to introduce QTLs/gene associated with yield in the rice plant. MAS has also been employed effectively in dissecting several other complex agricultural traits, for instance, drought, cold tolerance, salinity, etc. in rice plants. Thus, in this review, authors attempted to collect information about various genes/QTLs associated with high yield, including grain number, in rice and how different scheme of MAS can be employed to introduce them in rice (Oryza sativa L.) plant, which in turn will enhance rice yield. Information obtained to date suggest that, numerous QTLs, e.g., Gn1a, Dep1, associated with grain number and yield-related traits, have been identified either via mapping or cloning approaches. These QTLs have been successfully introduced into rice plants using various schemes of MAS for grain yield enhancement in rice. However, sometimes, MAS does not perform well in breeding, which might be due to lack of resources, skilled labors, reliable markers, and high costs associated with MAS. Thus, by overcoming these problems, we can enhance the application of MAS in plant breeding, which, in turn, may help us in increasing yield, which subsequently may help in bridging the gap between demand and supply of food for the continuously growing population.

Seed germination plays cardinal roles in seedling establishment and their successive growth. However, seed germination is retarded by far-red (FR) enrichment under low light stress, and the inhibitory signalling mechanism remains ambiguous. Our results indicated that low light treatment, both in the open and growth chamber conditions, inhibits rice seed germination by decreasing the gibberellin (GA) contents. To explore the mechanism of GA-deficiency under low light stress, differential expression profiling of GA-anabolic, -catabolic, ABA -anabolic, -catabolic, and SLR1 was investigated, revealing that expression of ABA- anabolic, GA-catabolic genes and SLR1 was upregulated with a simultaneous downregulation of ABA-catabolic and GA-anabolic genes under low light treatment. These results suggested that FR-induced GA inadequacy is resulted by upregulation of SLR1 and GA-catabolism genes consequently increase DELLA that further subsided GA-responses in the germinating rice seeds. Moreover, we provided evidence that FR-induced GA inadequacy demotes rice seed germination by decreasing amylase activity, eventually decreasing the carbohydrate solubilization in the germinating seeds. Finally, we suggest that under low light stress, due to a retarded conversion of phytochrome A to their bioactive form, the ABA-catabolic genes were eventually upregulated with a simultaneous downregulation of GA-anabolic genes. Consequently, a lower GA pool fails to leverage the GA-dependent DELLA degradation, further shutting down the expected GA responses that reduce germination efficiency under FR-enriched light. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12298-022-01167-7.

Phosphorus (P) flow in agricultural land depends on the P taken off from harvested product, its losses through runoff and fertilizer applied to balance the removed P. Phytic acid (PA), the major storage form of phosphorus (P) in cereal grains is a key anti-nutrient for human and non-ruminants leads to eutrophication of waterways. As the natural non-renewable P reserves are limited, enhancing P use efficiency is needed for field crops. SULTR-like phosphorus distribution transporter (SPDT) is a novel rice transporter transfer P to the grain. Any alteration in transporter gene reduce grain P with concomitant rise in the leaves. A low PA (3.0 g/kg) rice Khira was identified where a single nucleotide mutation in LOC_Os06g05160 gene encoding SPDT showed low P transportation to grain. An amino acid change was detected as Valine-330 to Alanine at the 3' end of fifth exon. Highest expression of SPDT was observed in node I of rice as compared to low PA genotype. The mutation in SPDT could significantly affect P and PA accumulation in the grains with increased mineral bioavailability. PRACTICAL APPLICATIONS: Excessive P application in crop leads to higher production cost as well as rapid depletion of limited rock phosphate. Alteration of P transporter function in the rice lower PA and total P accumulation in the grains with increased mineral bioavailability. The re-distributed P in the straw can be applied as manure to the rice field. Thus, less P will be removed from the field, result in the decreased requirement for P fertilizer.

High grain number is positively correlated with grain yield in rice, but it is compromised because of poor filling of basal spikelets in dense panicle bearing numerous spikelets. The phenomenon that turns the basal spikelets of compact panicle sterile in rice is largely unknown. In order to understand the factor(s) that possibly determines such spikelet sterility in compact panicle cultivars, QTLs and candidate genes were identified for spikelet fertility and associated traits like panicle compactness, and ethylene production that significantly influences the grain filling using recombinant inbred lines developed from a cross between indica rice cultivars, PDK Shriram (compact, high spikelet number) and Heera (lax, low spikelet number). Novel QTLs, qSFP1.1, qSFP3.1, and qSFP6.1 for spikelet fertility percentage; qIGS3.2 and qIGS4.1 for panicle compactness; and qETH1.2, qETH3.1, and qETH4.1 for ethylene production were consistently identified in both kharif seasons of 2017 and 2018. The comparative expression analysis of candidate genes like ERF3, AP2-like ethylene-responsive transcription factor, EREBP, GBSS1, E3 ubiquitin-protein ligase GW2, and LRR receptor-like serine/threonine-protein kinase ERL1 associated with identified QTLs revealed their role in poor grain filling of basal spikelets in a dense panicle. These candidate genes thus could be important for improving grain filling in compact-panicle rice cultivars through biotechnological interventions.

The introgression of wild chromosomal segments into popular rice varieties is one of the potential approaches for developing varieties for drought stress condition. Sixteen genotypes, including nine indica, two tropical japonica and five chromosome segments substitution lines (CSSLs) with different levels of tolerance/susceptibility to drought stress, were selected for diversity study. Sixty-three microsatellite markers were utilized for assessing genetic diversity. A total of 95 alleles were amplified, and out of them, 60 were polymorphic. Six unique alleles, amplified by the microsatellite loci RM276, RM472, RM488, RM537, RM541 and RM28089, were identified in six genotypes, namely FR13A, Brahamanakhi, RUF44, Swarna-sub1, Brahamanakhi and Satyabhama. The highest genetic similarity was found among CSSLs. Polymorphism information content (PIC) value varied from 0 to 1.00 with an average of 0.66 per locus. Twenty-eight microsatellites were found to be polymorphic, which could be used in marker-assisted selection programme. All the sixteen genotypes were grouped into two major clusters at genetic similarity of 0.64. In the cluster I, five CSSLs identified as diverse genotypes had wild ancestor segments responsible for drought tolerance, and hence they could be utilized as potential donors. The popular Indian varieties, Swarna-sub1 and IR64-sub1, could be used as recurrent parents in the future breeding program for developing varieties for abiotic stresses such as submergence and drought. Keywords: Genetic diversity, Marker-assisted selection, Microsatellite marker, Rice, Drought stress, Submergence, Chromosome segment substitution line

Rice serves as the major food for almost half of the world population. Because of its origin in the tropical and subtropical area, rice is more sensitive towards cold stress. Three homologs of DREB1, namely DREB1A, DREB1B and DREB1C are induced Queryduring cold stress and after binding with GCC-box in the promoter region of the target gene, they enhance cold tolerance in rice plants. Though the majority of DREBs bind GCC-box, the degree of activation varies among DREBs. The protein encoded via these three transcription factors contains a common domain, namely AP2/ERF. In silico method was utilised to predict 3D structure of each AP2/ERF domain. The molecular dynamic analysis suggests, under the normal environmental condition, in each AP2/ERF domain, a positive correlation exists between β-strands and the movement of C-α is constrained. However, during cold stress, when AP2/ERF domain binds with GCC-box present in the promoter region of the target gene, mean pressure of each three AP2/ERF domain gets lowered and final potential energy increases. A positive correlation between β-strands gets disrupted and C-α experiences random movement suggesting enhanced activity of DREB1A, DREB1B and DREB1C during cold stress and enhancement of cold tolerance in plants. Further, MM/PBSA calculations for protein–DNA affinities reveal that, due to lack of α2 in DREB1C, the binding affinity of GCC-box with AP2/ERF domain of DREB1A > DREB1B > DREB1C. Thus, due to a better binding affinity with GCC-box, DREB1A and DREB1B can be utilised in near future for increasing cold tolerance of rice plant and increasing yield.

Lack of appropriate donors, non-utilization of high throughput phenotyping and genotyping platforms with high genotype × environment interaction restrained identification of robust QTLs for grain protein content (GPC) in rice. In the present investigation a BC3F4 mapping population was developed using grain protein donor, ARC10075 and high-yielding cultivar Naveen and 190 lines were genotyped using 40 K Affimetrix custom SNP array with the objective to identify stable QTLs for protein content. Three of the identified QTLs, one for GPC (qGPC1.1) and the other two for single grain protein content (qSGPC2.1, qSGPC7.1) were stable over the environments explaining 13%, 14% and 7.8% of the phenotypic variances, respectively. Stability and repeatability of these additive QTLs were supported by the synergistic additive effects of multi-environmental-QTLs. One epistatic-QTL, independent of the main effect QTL was detected over the environment for SGPC. A few functional genes governing seed storage protein were hypothesised inside these identified QTLs. The qGPC1.1 was validated by NIR Spectroscopy-based high throughput phenotyping in BC3F5 population. Higher glutelin content was estimated in high-protein lines with the introgression of qGPC1.1 in telomeric region of short arm of chromosome 1. This was supported by the postulation of probable candidate gene inside this QTL region encoding glutelin family proteins.

High grain number is positively correlated with grain yield in rice, but it is compromised because of poor filling of basal spikelets in dense panicle bearing numerous spikelets. The phenomenon that turns the basal spikelets of compact panicle sterile in rice is largely unknown. In order to understand the factor(s) that possibly determines such spikelet sterility in compact panicle cultivars, QTLs and candidate genes were identified for spikelet fertility percentage, panicle compactness and ethylene production that significantly influence the grain filling using recombinant inbred lines developed from a cross between indica rice cultivars, PDK Shriram (compact, high spikelet number) and Heera (lax, low spikelet number). Novel QTLs, qSFP1.1, qSFP3.1 and qSFP6.1 for spikelet fertility percentage; qIGS3.2 and qIGS4.1 for panicle compactness; and qETH1.2, qETH3.1 and qETH4.1 for ethylene production were consistently identified in both kharif seasons of 2017 and 2018. The comparative expression analysis of candidate genes like ERF3, AP2-like ethylene-responsive transcription factor, EREBP, GBSS1, E3 ubiquitin-protein ligase GW2, and LRR receptor-like serine/threonine-protein kinase ERL1 associated with identified QTLs revealed their role in poor grain filling of basal spikelets in dense panicle. These candidate genes thus could be important for improving grain filling in compact-panicle rice cultivars through biotechnological interventions.

Recent advancements in technologies have led to the accumulation of a vast number of biological sequences and structures. However, storing and analyzing them are a grand challenge to biologists. Recently developed bioinformatic approaches serve as a key solution to solve these problems. Today, bioinformatics is used in various applications ranging from biofuel production to the drug discovery process. However, as most of the biological data and databases have distinct information and file formats, analyzing them requires knowledge from diverse fields, like molecular biology, mathematics, and computer sciences. Hence, the majority of the biologists end up using only basic bioinformatic tools. For solving complex biological problems, they continuously have to depend on expertise. Thus, there is an urgent requirement to teach biologists the necessary computer and mathematics skills during undergraduate and graduate studies. Even extensive initiatives also need to start to standardize the format of biological databases and ontology, which, in the near future, will help biologists to work independently and will make bioinformatics a more coherent discipline of biology.

Rice is considered as a model crop plant to study phylogeny and genetic diversity. By improving the various sequencing techniques of whole genome it is possible to find out the region of both nuclear and mitochondrial and chloroplast genomes that are mainly involved in evolution. In rice evolution, many genes present in the chloroplast and mitochondrial genome play a role in plant evolution. As compared to the mitochondrial genome, the chloroplast genome contains a highly conserved sequence that makes researchers find out the genetic variation that led to a new evolution in the plant. The mitochondrial genome contains genes that are mainly involved in cytoplasmic male sterility (CMS) in rice. Due to the decoding of whole genome it is possible to detect genes and improvement of crops through genetic engineering and editing or repairing. Various stress resistances, such as herbicide resistance plants, are developed through the decoding of extrachromosomal DNA (eccDNA) in rice.

Rice is considered as a model plant to map the complete genome and identify the genes in the genome for various traits. Recent advance in molecular biology and statistical analysis techniques could boost the plant breeder to gain knowledge on rice genome and find out the function and expression of rice genes. Near the genes, several quantitative trait loci (QTLs) have been mapped and identified with genetic markers to find out its location in chromosome. With the popularity of computational approach, it is easy to find out the QTLs position in the genome and introgress these QTLs into the popular rice genotypes to study the expression and its effect with trait used in MAS breeding program. Several models and QTL mapping methods are used to detect the QTL position between marker interval. QTL mapping is a highly efficient way of determining genetic interactions in plant disease resistance. The QTL mapping was carried in F2, RILs, NILs, backcrossing populations, and double haploids (DH) in rice to study the performance of respective QTLs and its genetic basis of expression in grain yield and other traits as well as other parameters of yield like biotic and disease resistance. Various studies have been reported for QTL mapping and its function in phenotypic variation found in rice. This fine mapping and detection of QTL position provide a new way to improve the rice plant against various theft of decreasing yield.

De novo evolution of genes was used as an important approach in the rice breeding program. The identification of the novel genes in the rice genome regulates economic traits such as grain size, weight, and others linked to grain yield or quality. Specific novel genes, also called orphan genes, were used to detect these orphan genes and their expression pattern in rice development. This certain evolution of genes helps to study the expression pattern of the plant genome in various stresses. Due to the newly discovered key functions of orphan genes, their study may lead to the understanding of environmental stress, its impact, adaptability, and evolution of novel genes and proteins, as well as crop productivity.

Long non-coding RNA (lncRNA) is the largest non-protein and functional RNA. The majority of lncRNAs are functionally uncharacterized. Thus, researchers are employing both experimental as well as computational approaches to characterize unknown lncRNAs. Recently, the majority of lncRNAs have been characterized using transcriptome sequencing datasets under different conditions. The information on these transcripts was mainly restricted to genomic loci and expression patterns. This discrepancy in lncRNAs’ functional understanding has largely been due to the lack of methods to classify basic genome-scale bio-molecular interactions and resources that systematically archive these interactions. Additionally, experimental methods are time-consuming and cost-effective. To overcome this, various bioinformatics tools have been developed to predict lncRNA. Thus, in this chapter, the authors attempted to understand the underlying principle of various computational approaches to detect lnRNAs. Information obtained reveals that lncRNA may be annotated by employing its coding potential and sequence conservation, folding algorithms, and interactions. Additionally, several databases have also been developed to help researchers detect lncRNAs from the large genomic dataset. Though the result obtained from these tools and databases is useful, a systematic integrative and metadata analysis is also required to understand diverse lncRNA regulatory mechanisms of action at various levels. Further attempts would be made to annotate their features, which is beneficial in understanding the underlying cell biology.

Over the past 10 years, gene set analysis has been the first option for studying gene expression and gene interaction for gaining insights into the fundamental dynamic biology of disease/traits. It, therefore, reduces the complexity of traditional statistical research and increases the illustrating strength of the outcomes achieved. Although approaches to gene set analysis are commonly utilized in gene expression analytics, the statistical framework and steps generally employed in these methods have not yet been thoroughly explored, restricting their usefulness. Thus, in this chapter, the authors include an outlined statistical framework and steps for the analysis of gene set used for various genome studies, ranging from microarrays, RNA sequencing, and the analysis of genomic widespread association results. The drawbacks of these approaches and strengths have also been addressed depending on their separate components such as their gene score, null hypotheses, and essential evaluation methods. The authors believe that a standardized approach for testing the methods of gene set analysis can also be used for correcting the lack of agreement on the method of preference for a specific experiment. The benchmark expression data sets will reflect actual expression data characteristics and prevent oversimplifying conclusions, such as naturally distributed data with zero or constant gene-gene correlations. In the near future, information present in this chapter will be highly useful for underlying process with any disease or trait in more comprehensive way.

Genome editing is the main goal in improving rice productivity by inducing gene-specific mutations to modify the gene function. Due to the advances in the biotechnology approach, it is possible to make the gene manipulation that could give effective results against various biotic and abiotic stresses and grain quality. Nowadays, population increases day by day; therefore, it is a big challenge for developing countries. However, it is important to release new crop varieties with effective function, and which will minimize the food shortage and food security problem. The existing rice genome sequences have made it possible to identify genetic variations and insert mutagenesis to improve rice traits. Various methods and techniques are available for genome editing to modify the gene through inducing mutagenesis or by gene silencing. Zinc finger nucleases (ZFNs), transcriptional activator-like nucleases (TALENs), and newly identified clustered regularly interspaced repeats (CRISPR)/CRISPR associated protein 9 (Cas9)-based genome editing methods are more widely used in rice breeding programs. Among them CRISPR/Cas9 is the most reliable tool in genome editing due to its simple targeting method for improving grain quality and quantity. Thus, the application of CRISPR/Cas9 system introduces a double-strand break in DNA to edit the gene of interest at a specific position. Additionally, it may be utilized in the near future to develop disease and herbicide resistance, as well as to improve rice production, quality, and quantitative characteristics.

All cellular structures are heterogeneous. Thus, investigating true cell heterogeneity is highly required to further understand cellular connectivity and accountability within a disease or normal conditions. Because of its rapidly decreasing costs, the Next-Generation (NGS) sequence is widely used to analyze various biological data. However, these approaches may fail to provide detailed insight into cells’ true heterogeneity. Recently developed single-cell RNA sequencing (scRNA-seq) technology tries to tackle these bulk NGS issues by linking transcriptomic, epigenomic, proteomic, and molecular sequences to a specific cell. Thus, in this chapter, the author addresses the process involved, relative strengths, possible uses, and limitations of scRNA-seq techniques methods. Information obtained revealed that cell isolation methods may be broadly divided into two categories centered on different principles. The first category centered on physical characteristics, while the second category mainly focuses on cell’ features and primarily involves affinity approaches. After obtaining raw data, the general approach for analyzing ScRNA-Seq data are pre-processing, batch effect correction, normalization, dimensionality reduction, feature selection, cell type identification, differential expression analysis, and rebuilding cell hierarchy. scRNA-seq technologies are continuously being employed to unmask various biological processes ranging from epigenetic regulation to biomarker identification. However, scRNA-seq technologies do have difficulties like cumbersome activity and high detection costs that restrict technology promotion. Hence, there is an urgent requirement to develop more robust tools so that, in the near future, the technology for single-cell sequencing will be streamlined and is more efficient.

Rice is one of the world’s most important agricultural crops and a model plant that is widely studied. The completion of the complete rice (Oryza sativa) genome sequence through high-throughput experimental platforms has resulted in a huge amount of data being generated and specialized databases, and bioinformatic tools for data processing, analysis, efficient organization, and visualization have been developed. In this chapter, we address a set of biological databases that host rice-specific sequence, genetic variation, gene expression, pathways, and gene–interactome data from various genomic and proteomic sources, and aid in data analysis and visualization.

The epigenetic study is now most widely used in plant research to identify the mutation as well as modifications present at the genome level. Modern advanced technologies enable genome-wide DNA and histone modifications to be assessed, which in turn help us to understand how they regulate the functions, and this might also be an aid in classifying plant regulation mechanisms on a higher level than the nucleotide sequence. Rice is an important food grain and is an ideal example for monocotyledons because it has a smaller genome size and a completely sequenced, well-annotated genome. Computational methods and bioinformatics tools are used in epigenomics studies, particularly during experimental design, data analysis, hypothesis confirmation, and results interpretation, due to the large quantity of data produced by high-throughput sequencing. High-throughput sequencing approaches are widely used to identify such modifications in rice that have an effect on growth, development as well as biotic and abiotic stress in the genome region. DNA methylation is regulated in rice by several regulators that are involved in both adaptation and agronomic efficiency. This, in turn, indicates that a detailed study of epigenetic regulators in rice may benefit development. Considering the above information in the present chapter, the author made an attempt to discuss various epigenetic modifications that occur in rice plant and their mode of expression function in a different area.