Showing papers by "Indian Agricultural Statistics Research Institute published in 2019"

RNAseq analysis reveals drought-responsive molecular pathways with candidate genes and putative molecular markers in root tissue of wheat

Abstract: Drought is one of the major impediments in wheat productivity. Traditional breeding and marker assisted QTL introgression had limited success. Available wheat genomic and RNA-seq data can decipher novel drought tolerance mechanisms with putative candidate gene and marker discovery. Drought is first sensed by root tissue but limited information is available about how roots respond to drought stress. In this view, two contrasting genotypes, namely, NI5439 41 (drought tolerant) and WL711 (drought susceptible) were used to generate ~78.2 GB data for the responses of wheat roots to drought. A total of 45139 DEGs, 13820 TF, 288 miRNAs, 640 pathways and 435829 putative markers were obtained. Study reveals use of such data in QTL to QTN refinement by analysis on two model drought-responsive QTLs on chromosome 3B in wheat roots possessing 18 differentially regulated genes with 190 sequence variants (173 SNPs and 17 InDels). Gene regulatory networks showed 69 hub-genes integrating ABA dependent and independent pathways controlling sensing of drought, root growth, uptake regulation, purine metabolism, thiamine metabolism and antibiotics pathways, stomatal closure and senescence. Eleven SSR markers were validated in a panel of 18 diverse wheat varieties. For effective future use of findings, web genomic resources were developed. We report RNA-Seq approach on wheat roots describing the drought response mechanisms under field drought conditions along with genomic resources, warranted in endeavour of wheat productivity.

Uncovering Genomic Regions Associated With 36 Agro-Morphological Traits in Indian Spring Wheat Using GWAS.

Abstract: Wheat genetic improvement by integration of advanced genomic technologies is one way of improving productivity. To facilitate the breeding of economically important traits in wheat, SNP loci and underlying candidate genes associated with the 36 agro-morphological traits were attempted in a diverse panel of 404 genotypes. Using Breeders’ 35K Axiom Array in a comprehensive genome-wide association study covering 4364.79 cM of the wheat genome and compressed mixed linear model, a total of 146 SNPs (-log10 P ≥ 4) were found associated with 23 traits out of 36 traits studied explaining 3.7-47.0% phenotypic variations. To reveal this a subset of 260 genotypes was characterized phenotypically for six quantitative traits [days to heading (DTH), days to maturity (DTM), plant height (PH), spike length (SL), awn length (Awn_L) and leaf length (Leaf_L)] under five environments. Gene annotations mined ~38 putative candidate genes which were confirmed using tissue and stage specific gene expression data from RNA Seq. We observed strong co-localized loci for four traits (glume pubescence, spike length, plant height and awn color) on chromosome 1B (24.64 cM) annotated five putative candidate genes. This study led to the discovery of hitherto unreported loci for some less explored traits (such as leaf sheath wax, awn attitude, glume pubescence) besides the refined chromosomal regions of known loci associated with the traits..This study provides valuable information of the genetic loci and their potential genes underlying the traits such as awn characters which are being considered as important contributors towards yield enhancement.

Deciphering Genotype-by- Environment Interaction for Targeting Test Environments and Rust Resistant Genotypes in Field Pea (Pisum sativum L.)

Abstract: Rust caused by Uromyces viciae-fabae is a major biotic constraint to field pea (Pisum sativum L.) cultivation worldwide. Deployment of host-pathogen interaction and resistant phenotype is a modest strategy for controlling this intricate disease. However, resistance against this pathogen is partial and influenced by environmental factors. Therefore, the magnitude of environmental and genotype-by-environment interaction was assessed to understand the dynamism of resistance and identification of durable resistant genotypes, as well as ideal testing locations for rust screening through multi-location and multi-year evaluation. Initial screening was conducted with 250 diverse genotypes at rust hot spots. A panel of 23 promising field pea genotypes extracted from initial evaluation was further assessed under inoculated conditions for rust disease for two consecutive years at six locations in India. Integration of GGE biplot analysis and multiple comparisons tests detected a higher proportion of variation in rust reaction due to environment (56.94%) as an interactive factor followed by genotype × environment interaction (35.02%), which justified the requisite of multi-year, and multi-location testing. Environmental component for disease reaction and dominance of cross over interaction (COI) were asserted by the inconsistent and non-repeatable genotypic response. The present study effectively allocated the testing locations into various categories considering their "repeatability" and "desirability index" over the years along with "discrimination power" and "representativeness." "Mega environment" identification helped in restructuring the ecological zonation and location of specific breeding. Detection of non-redundant testing locations would expedite optimal resource utilization in future. The computation of the confidence limit (CL) at 95% level through bootstrapping strengthened the accuracy of the GGE biplot and legitimated the precision of genotypes recommendation. Genotype, IPF-2014-16, KPMR-936 and IPF-2014-13 identified as "ideal" genotypes, which can be recommended for release and exploited in a resistance breeding program for the region confronting field pea rust.

Quantitative proteomic analysis reveals novel stress-associated active proteins (SAAPs) and pathways involved in modulating tolerance of wheat under terminal heat.

Abstract: Terminal heat stress has detrimental effect on the growth and yield of wheat. Very limited information is available on heat stress-associated active proteins (SAAPs) in wheat. Here, we have identified 159 protein groups with 4271 SAAPs in control (22 ± 3 °C) and HS-treated (38 °C, 2 h) wheat cvs. HD2985 and HD2329 using iTRAQ. We identified 3600 proteins to be upregulated and 5825 proteins to be downregulated in both the wheat cvs. under HS. We observed 60.3% of the common SAAPs showing upregulation in HD2985 (thermotolerant) and downregulation in HD2329 (thermosusceptible) under HS. GO analysis showed proton transport (molecular), photosynthesis (biological), and ATP binding (cellular) to be most altered under HS. Most of the SAAPs identified were observed to be chloroplast localized and involved in photosynthesis. Carboxylase enzyme was observed most abundant active enzymes in wheat under HS. An increase in the degradative isoenzymes (α/β-amylases) was observed, as compared to biosynthesis enzymes (ADP-glucophosphorylase, soluble starch synthase, etc.) under HS. Transcript profiling showed very high relative fold expression of HSP17, CDPK, Cu/Zn SOD, whereas downregulation of AGPase, SSS under HS. The identified SAAPs can be used for targeted protein-based precision wheat-breeding program for the development of ‘climate-smart’ wheat.

Development and validation of whole genome-wide and genic microsatellite markers in oil palm (Elaeis guineensis Jacq.): First microsatellite database (OpSatdb).

Abstract: The availability of large expressed sequence tag (EST) and whole genome databases of oil palm enabled the development of a data base of microsatellite markers. For this purpose, an EST database consisting of 40,979 EST sequences spanning 27 Mb and a chromosome-wise whole genome databases were downloaded. A total of 3,950 primer pairs were identified and developed from EST sequences. The tri and tetra nucleotide repeat motifs were most prevalent (each 24.75%) followed by di-nucleotide repeat motifs. Whole genome-wide analysis found a total of 245,654 SSR repeats across the 16 chromosomes of oil palm, of which 38,717 were compound microsatellite repeats. A web application, OpSatdb, the first microsatellite database of oil palm, was developed using the PHP and MySQL database ( https://ssr.icar.gov.in/index.php ). It is a simple and systematic web-based search engine for searching SSRs based on repeat motif type, repeat type, and primer details. High synteny was observed between oil palm and rice genomes. The mapping of ESTs having SSRs by Blast2GO resulted in the identification of 19.2% sequences with gene ontology (GO) annotations. Randomly, a set of ten genic SSRs and five genomic SSRs were used for validation and genetic diversity on 100 genotypes belonging to the world oil palm genetic resources. The grouping pattern was observed to be broadly in accordance with the geographical origin of the genotypes. The identified genic and genome-wide SSRs can be effectively useful for various genomic applications of oil palm, such as genetic diversity, linkage map construction, mapping of QTLs, marker-assisted selection, and comparative population studies.

Supercritical fluid extraction of β-carotene from ripe bitter melon pericarp

Abstract: Study ascertained the recovery of β-carotene from enzyme-treated (enzyme load of 167 U/g) pericarp of ripe bitter melon using supercritical fluid extraction (SFE) technique. Effect of different pressure (ranged from 150–450 bar), carbon dioxide (CO2) flow rates (ranged from 15 to 55 ml/min), temperatures (from 50 to 90 °C), and extraction periods (from 45–225 minutes) were observed on the extraction efficiency of β-carotene. Results showed that extraction pressure (X1) among extraction parameters had the most significant (p < 0.05) effect on extraction efficiency of the β-carotene followed by allowed extraction time (X4), CO2 flow rate (X2) and the temperature of the extraction (X3). The maximum yield of 90.12% of β-carotene from lyophilized enzymatic pretreated ripe bitter melon pericarp was achieved at the pressure of approx. 390 bar, flow rate of 35 mL/min, temperature at 70 °C and extraction time of 190 min, respectively. Based on the accelerated storage study the 70% retention shelf life of the β-carotene into extract was estimated up to 2.27 months at 10 °C and up to 3.21 months at 5 °C.

Transcriptomic signature reveals mechanism of flower bud distortion in witches’-broom disease of soybean (Glycine max)

Abstract: Soybean (Glycine max L. Merril) crop is major source of edible oil and protein for human and animals besides its various industrial uses including biofuels. Phytoplasma induced floral bud distortion syndrome (FBD), also known as witches’ broom syndrome (WBS) has been one of the major biotic stresses adversely affecting its productivity. Transcriptomic approach can be used for knowledge discovery of this disease manifestation by morpho-physiological key pathways. We report transcriptomic study using Illumina HiSeq NGS data of FBD in soybean, revealing 17,454 differentially expressed genes, 5561 transcription factors, 139 pathways and 176,029 genic region putative markers single sequence repeats, single nucleotide polymorphism and Insertion Deletion. Roles of PmbA, Zn-dependent protease, SAP family and auxin responsive system are described revealing mechanism of flower bud distortion having abnormalities in pollen, stigma development. Validation of 10 randomly selected genes was done by qPCR. Our findings describe the basic mechanism of FBD disease, right from sensing of phytoplasma infection by host plant triggering molecular signalling leading to mobilization of carbohydrate and protein, phyllody, abnormal pollen development, improved colonization of insect in host plants to spread the disease. Study reveals how phytoplasma hijacks metabolic machinery of soybean manifesting FBD. This is the first report of transcriptomic signature of FBD or WBS disease of soybean revealing morphological and metabolic changes which attracts insect for spread of disease. All the genic region putative markers may be used as genomic resource for variety improvement and new agro-chemical development for disease control to enhance soybean productivity.

District level estimates and mapping of prevalence of diarrhoea among under-five children in Bangladesh by combining survey and census data.

Abstract: The demand for district level statistics has increased tremendously in Bangladesh due to existence of decentralised approach to governance and service provision. The Bangladesh Demographic Health Surveys (BDHS) provide a wide range of invaluable data at the national and divisional level but they cannot be used directly to produce reliable district-level estimates due to insufficient sample sizes. The small area estimation (SAE) technique overcomes the sample size challenges and can produce reliable estimates at the district level. This paper uses SAE approach to generate model-based district-level estimates of diarrhoea prevalence among under-5 children in Bangladesh by linking data from the 2014 BDHS and the 2011 Population Census. The diagnostics measures show that the model-based estimates are precise and representative when compared to the direct survey estimates. Spatial distribution of the precise estimates of diarrhoea prevalence reveals significant inequality at district-level (ranged 1.1–13.4%) with particular emphasis in the coastal and north-eastern districts. Findings of the study might be useful for designing effective policies, interventions and strengthening local-level governance.

Comparative transcriptome analysis reveals the genetic basis of coat color variation in Pashmina goat.

Abstract: The genetics of coat color variation remains a classic area. Earlier studies have focused on a limited number of genes involved in color determination; however, the complete set of trait determinants are still not well known. In this study, we used high-throughput sequencing technology to identify and characterize intricate interactions between genes that cause complex coat color variation in Changthangi Pashmina goats, producer of finest and costly commercial animal fiber. We systematically identified differentially expressed mRNAs and lncRNAs from black, brown and white Pashmina goat skin samples by using RNA-sequencing technique. A pairwise comparison of black, white and brown skin samples yielded 2479 significantly dysregulated genes (2422 mRNA and 57 lncRNAs). Differentially expressed genes were enriched in melanin biosynthesis, melanocyte differentiation, developmental pigmentation, melanosome transport activities GO terms. Our analysis suggested the potential role of lncRNAs on color coding mRNAs in cis and trans configuration. We have also developed online data repository as a component of the study to provide a central location for data access, visualization and interpretation accessible through http://pcd.skuastk.org/ .

Comparative performance of wavelet-based neural network approaches

Abstract: An agriculture-dominated developing country like India has been always in need of efficient and reliable time series forecasting methodologies to describe various agricultural phenomenons, whereas agricultural price forecasting continue to be the challenging areas in this domain. The observed features of many temporal price data set constitute complex nonlinearity, and modeling these features often go beyond the capability of Box–Jenkins autoregressive integrated moving average methodology. Moreover, despite the popularity and sheer power of traditional neural network model, the empirical forecasting performance of this model has not been found satisfactory in all cases. To address the problem, wavelet-based modeling approach is recently upsurging. Present study discusses two wavelet-based neural network approaches envisaging monthly wholesale onion price of three markets, namely Bangalore, Hubli, and Solapur. Wavelet-based decomposition makes it possible to describe the useful pattern of the series from both global as well as local aspects and found to be highly proficient in denoising and capturing the inherent pattern of the series through a distinctive approach. Besides, wavelet method can also be used as a tool for function approximation. The improvement upon time-delay neural network also be made up to a great extent through using wavelet-based approaches as exhibited through proper empirical evidence.

RiceMetaSysB: a database of blast and bacterial blight responsive genes in rice and its utilization in identifying key blast-resistant WRKY genes.

Abstract: Nearly two decades of revolution in the area of genomics serves as the basis of present-day molecular breeding in major food crops such as rice. Here we report an open source database on two major biotic stresses of rice, named RiceMetaSysB, which provides detailed information about rice blast and bacterial blight (BB) responsive genes (RGs). Meta-analysis of microarray data from different blast- and BB-related experiments across 241 and 186 samples identified 15135 unique genes for blast and 7475 for BB. A total of 9365 and 5375 simple sequence repeats (SSRs) in blast and BB RGs were identified for marker development. Retrieval of candidate genes using different search options like genotypes, tissue, developmental stage of the host, strain, hours/days post-inoculation, physical position and SSR marker information is facilitated in the database. Search options like 'common genes among varieties' and 'strains' have been enabled to identify robust candidate genes. A 2D representation of the data can be used to compare expression profiles across genes, genotypes and strains. To demonstrate the utility of this database, we queried for blast-responsive WRKY genes (fold change ≥5) using their gene IDs. The structural variations in the 12 WRKY genes so identified and their promoter regions were explored in two rice genotypes contrasting for their reaction to blast infection. Expression analysis of these genes in panicle tissue infected with a virulent and an avirulent strain of Magnaporthe oryzae could identify WRKY7, WRKY58, WRKY62, WRKY64 and WRKY76 as potential candidate genes for resistance to panicle blast, as they showed higher expression only in the resistant genotype against the virulent strain. Thus, we demonstrated that RiceMetaSysB can play an important role in providing robust candidate genes for rice blast and BB.

Spatio-temporal trend in heat waves over India and its impact assessment on wheat crop

Abstract: This study presents two aspects of heat waves over India. Firstly, we have analysed the gridded daily temperature data (1° × 1°) for six decades (1951–2015) to understand the changes in heat waves using the heat wave magnitude index daily (HWMId). Secondly, we have post-facto assessed the impact of the March 2010 heat wave on the growth and yield of wheat crop over north India. The study clearly showed the effectiveness of HWMId to capture the heat waves over India. The most-intense heat waves have significantly increased over 56% area of the country, and over the past three decades, it also started occurring in non-conventional heat wave regions of the southern peninsula and northeastern India. Among different categories of HWMId, the rate of spatial spread was highest for the very extreme category. The auto-regressive integrated moving average (ARIMA) intervention technique showed negative impact of the March 2010 extreme heat on yield of wheat over north India. The yield decreased by 4.9%, 4.1% and 3.5% over Punjab, Haryana and Uttar Pradesh, respectively, which were statistically significant (p < 0.1). Though the total production decreased, it was non-significant due to the slight increase in harvested area. Satellite-derived crop phenology parameters also captured the event. The rate of browning increased significantly over the study area. There were inter-district variations in the heat-wave impacts. The results may be used for identification of potential heat wave zones. It may also be used for devising zone-specific adaptation strategies for shielding wheat crop from such events.

Rice pest management with reduced risk pesticides in India

Abstract: Sitapur district in the Uttar Pradesh (U.P.) state of northern India has been observed to consume large amounts of WHO classified “extremely” and “highly hazardous” pesticides, in rice crop, posing significant health and environmental threats. Keeping in view this problem, integrated pest management (IPM) modules were synthesized for rice crop and then compared with non-IPM/farmer’s practice (NIPM). This study assisted in identifying pesticides with reduced risk to the environment. To measure and compare risks, environmental impact quotient (EIQ) has been used as a pesticide risk indicator model, between IPM and NIPM programs. Using this model, the field EIQ values (EIQ field use rating or EIQ-FUR), for 32 commonly used pesticides in the region, were evaluated based on dosage, frequency, and percent active ingredients present in the pesticide formulations. The results conclude that copper oxychloride (CuOCl2) (50 WP at 1.25 kg/ha) and mancozeb (75 WP at 1.25 kg/ha) were the most detrimental to arthropod parasitoids and were the highest contributors to environmental risk (13–16%), in rice crop. This is based on the comparison of total dosage and active ingredients of pesticides applied under IPM and NIPM, with the total field EIQ values. The IPM modules were observed to have least impact on natural enemies with 30–40% increase in population, while keeping the weed population below 10%. NIPM, on the other hand, had resulted in 20% reduction in crop yields, 50% reduction in biodiversity, and about 150% increase in weed population, relative to the control (untreated) rice fields. Moreover, NIPM practices had been observed to pose 56% greater risk as per the total field EIQ values (62 for IPM and 141 for NIPM). The observations concluded that the EIQ model is a useful tool and can be easily used by the pesticide managers for assessing the risk against NIPM.

funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model

Abstract: Identification of unknown fungal species aids to the conservation of fungal diversity. As many fungal species cannot be cultured, morphological identification of those species is almost impossible. But, DNA barcoding technique can be employed for identification of such species. For fungal taxonomy prediction, the ITS (internal transcribed spacer) region of rDNA (ribosomal DNA) is used as barcode. Though the computational prediction of fungal species has become feasible with the availability of huge volume of barcode sequences in public domain, prediction of fungal species is challenging due to high degree of variability among ITS regions within species. A Random Forest (RF)-based predictor was built for identification of unknown fungal species. The reference and query sequences were mapped onto numeric features based on gapped base pair compositions, and then used as training and test sets respectively for prediction of fungal species using RF. More than 85% accuracy was found when 4 sequences per species in the reference set were utilized; whereas it was seen to be stabilized at ~88% if ≥7 sequence per species in the reference set were used for training of the model. The proposed model achieved comparable accuracy, while evaluated against existing methods through cross-validation procedure. The proposed model also outperformed several existing models used for identification of different species other than fungi. An online prediction server “funbarRF” is established at http://cabgrid.res.in:8080/funbarrf/ for fungal species identification. Besides, an R-package funbarRF ( https://cran.r-project.org/web/packages/funbarRF/ ) is also available for prediction using high throughput sequence data. The effort put in this work will certainly supplement the future endeavors in the direction of fungal taxonomy assignments based on DNA barcode.

Development of model web-server for crop variety identification using throughput SNP genotyping data

Abstract: Crop varieties or genotypes of a given species are pivotal for agricultural production and ownership, management and improvement of their germplasm is a great challenge. Its morphological identification requires time, cost and descriptors are often compromised statistically due to phenotypic plasticity. Development of DNA based signature of varieties can overcome these limitations. There is a global need to implement world trade organization (WTO) and intellectual property rights (IPR) guidelines of Plant Breeders Rights (PBR) where DUS (distinctness, uniformity and stability) testing can be supplemented by DNA profile. Universalization and minimization of SNP number without compromising identification accuracy is the major challenge in development of varietal profile by rapid genotype assay. Besides this, there is no server-based approach reducing computational skill with global accessibility of referral phenotypic and genotypic data. We report world’s first model web server for crop variety identification using >350 Indian wheat varieties and Axiom 35 K SNP chip data. Standard filtering and linkage disequilibrium approach were used to develop varietal signature in Linux using HTML, Java, PHP and MySQL with provision of QR code generator to facilitate bar-coding. Phylogenetic tree constructed by selected SNPs confirms six major trait based clusters of varieties and their pedigree. Our user friendly server based tool, VISTa (Variety Identification System of Triticum aestivum) ( http://webtom.cabgrid.res.in/vista ) can be used in DUS testing having dispute resolution of sovereignty and access benefit sharing (ABS) issues. This model approach can be used in other crops with pan-global level management of crop germplasm in endeavour of crop productivity.

The role of conserved residues in the catalytic activity of NDM-1: an approach involving site directed mutagenesis and molecular dynamics

Abstract: The rise of New Delhi metallo-beta-lactamase-1 (NDM-1) producers is a major public health concern due to carbapenem resistance. Infections caused by carbapenem-resistant enterobacteria (CRE) are classified as a serious problem. To understand the structure and function of NDM-1, an amino acid replacement approach is considered as one of the methods to get structural insight. Therefore, we have generated novel mutations (N193A, S217A, G219A and T262A) near active sites and an omega-like loop to study the role of conserved residues of NDM-1. The minimum inhibitory concentrations (MICs) of ampicillin, imipenem, meropenem, cefotaxime, cefoxitin and ceftazidime for all mutants were found to be reduced 2 to 6 fold, compared to a wild type NDM-1 producing strain. The Km values increased while Kcat and Kcat/Km values were decreased compared to wild type. The affinity as well as the catalysis properties of these mutants were reduced considerably for imipenem, meropenem, cefotaxime, cefoxitin, and ceftazidimem compared to wild type, hence the catalytic efficiencies (Kcat/Km) of all mutant enzymes were reduced owing to the poor affinity of the enzyme. The IC50 values of these mutants with respect to each drug were reduced compared to wild type NDM-1. MD simulations and docking results from the mutant protein models, along with the wild type example, showed stable and consistent RMSD, RMSF and Rg behavior. The α-helix content values of all mutant proteins were reduced by 13%, 6%, 14% and 9% compared to NDM-1. Hence, this study revealed the impact role of active sites near residues on the enzyme catalytic activity of NDM-1.

Standardizing Proteomics Workflow for Liquid Chromatography-Mass Spectrometry: Technical and Statistical Considerations.

Abstract: Introduction: The quantitative measurements based on liquid chromatography (LC) coupled with mass spectrometry (MS) often suffer from the problem of missing values and data heterogeneity from technical variability. We considered a proteomics data set generated from human kidney biopsy material to investigate the technical effects of sample preparation and the quantitative MS. Methods: We studied the effect of tissue storage methods (TSMs) and tissue extraction methods (TEMs) on data analysis. There are two TSMs: frozen (FR) and FFPE (formalin-fixed paraffin embedded); and three TEMs: MAX, TX followed by MAX and SDS followed by MAX. We assessed the impact of different strategies to analyze the data while considering heterogeneity and MVs. We have used analysis of variance (ANOVA) model to study the effects due to various sources of variability. Results and Conclusion: We found that the FFPE TSM is better than the FR TSM. We also found that the one-step TEM (MAX) is better than those of two-steps TEMs. Furthermore, we found the imputation method is a better approach than excluding the proteins with MVs or using unbalanced design.

PolyMorphPredict: A Universal Web-Tool for Rapid Polymorphic Microsatellite Marker Discovery From Whole Genome and Transcriptome Data.

Abstract: Microsatellites are ubiquitously distributed, polymorphic repeat sequence valuable for association, selection, population structure and identification. They can be mined by genomic library, probe hybridization and sequencing of selected clones. Such approach has many limitations like biased hybridization and selection of larger repeats. In silico mining of polymorphic markers using data of various genotypes can be rapid and economical. Available tools lack in some or other aspects like: targeted user defined primer generation, polymorphism discovery using multiple sequence, size and number limits of input sequence, no option for primer generation and e-PCR evaluation, transferability, lack of complete automation and user-friendliness. They also lack the provision to evaluate published primers in e-PCR mode to generate additional allelic data using re-sequenced data of various genotypes for judicious utilization of previously generated data. We developed the tool (PolyMorphPredict) using Perl, R, Java and launched at Apache which is available at http://webtom.cabgrid.res.in/polypred/. It mines microsatellite loci and computes primers from genome/transcriptome data of any species. It can perform e-PCR using published primers for polymorphism discovery and across species transferability of microsatellite loci. Present tool has been evaluated using five species of different genome size having 21 genotypes. Though server is equipped with genomic data of three species for test run with gel simulation, but can be used for any species. Further, polymorphism predictability has been validated using in silico and in vitro PCR of four rice genotypes. This tool can accelerate the in silico microsatellite polymorphism discovery in re-sequencing projects of any species of plant and animal for their diversity estimation along with variety/breed identification, population structure, MAS, QTL and gene discovery, traceability, parentage testing, fungal diagnostics and genome finishing.

Molecular detection and in silico characterization of cold shock protein coding gene (cspA) from cold adaptive Pseudomonas koreensis

Abstract: Cold shock proteins (CSPs) are greatly conserved family of structurally related DNA binding proteins which are produced during temperature drift. A 213 bp long cspA gene was cloned and sequenced from Pseudomonas koreensis P2 in the present study. The expression analysis of the cspA showed > 2.5 folds increase in the mRNA level at 15 °C while the expression was almost on par at 30 °C and 5 °C indicating its role in moderately low temperature. In silico analyses of the gene showed that the gene codes for 7.69 kDa protein which was phylogenetically very similar to CspA present in Pseudomonads. Amino acid composition of the CspA from P. koreensis was different from that of mesophilic Pseudomonas and tiny/small amino varied significantly between CspA of cold adaptive and mesophilic species. The CspA from P. koreensis P2 contained RNP motifs involved in binding of DNA and RNA. Phylogenetic analyses revealed that the CspA protein of P. koreensis P2 was more close to CspA of distant subgroups of Pseudomonas like P. fluorescens and P. putida subgroup indicating a possible intra-specific gene transfer.

A Comparative Study of Single-Trait and Multi-Trait Genomic Selection.

Abstract: In recent years of animal and plant breeding research, genomic selection (GS) became a choice for selection of appropriate candidate for breeding as it significantly contributes to enhance the genetic gain. Various studies related to GS have been carried out in the recent past. These studies were mostly confined to single trait. Although GS methods based on single trait have not performed very well in cases like pleiotropy, missing data and when the trait under study has low heritability. Gradually, some studies were carried out to explore the possibility of methods for GS based on multiple traits in the view of overcoming the above-mentioned problems in the method of single-trait GS (STGS). Currently, multi-trait-based GS methods are getting importance as it exploits the information of correlated structure among response. In this study, we have compared various methods related to STGS, such as stepwise regression, ridge regression, least absolute shrinkage and selection operator (LASSO), Bayesian, best linear unbiased prediction, and support vector machine, and multi-trait-based GS methods, such as multivariate regression with covariance estimation, conditional Gaussian graphical models, mixed model, and LASSO. In almost all cases, multi-trait-based methods are found to be more accurate. Based on the results of this study, it may be concluded that multi-trait-based methods have great potential to increase genetic gain as they utilize the correlation among the response variable as extra information, which contributes to estimate breeding value more precisely. This study is a comprehensive review of the methods of GS right from single trait to multiple traits and comparisons among these two classes.

Genomics Assisted Breeding for Abiotic Stress Tolerance in Millets

Abstract: Large-scale genomic resources have been generated in sorghum, finger millet and pearl millet leading to availability of large number of molecular markers and transcriptome sequences. With the availability of genome sequence in sorghum, pearl millet, and others in progress, integration of genomic technologies in millet breeding has now started in general for most of the stresses. This has raised the status of millets to genome rich crops from resource poor crops. Genomics-assisted breeding is an advanced breeding approach, wherein both the genomic information and the phenotypic selection are considered concurrently for designing phenotypes. Genomics-assisted breeding is strongly supported by third generation DNA sequencing techniques, which have provided enormous nucleotide information. Data mining and allele identification tools have allowed us to generate information for genes of interest and their functional specificity. For genomics-assisted breeding the basic need is to have maximum genomic information, trait specific mapping populations and highly precise phenotyping facilities. In millets, whole genome sequence information of sorghum, pearl millet and foxtail millets are available, which can be utilized efficiently to identify candidate genes for abiotic stress tolerance and for advancing breeding strategies such as genomic selection. QTLs conferring stress tolerance have been identified in few of the major millet crops but fine mapping and development of gene specific markers for high throughput selection needs emphasis. This chapter is a brief account of the accomplishments made in field of genomics for important millet crops like sorghum, pearl millet, foxtail millet, proso millet etc. and its application in improving abiotic tolerance.

VigSatDB: genome-wide microsatellite DNA marker database of three species of Vigna for germplasm characterization and improvement.

Abstract: Genus Vigna represented by more than 100 species is a source of nutritious edible seeds and sprouts that are rich sources of protein and dietary supplements. It is further valuable because of therapeutic attributes due to its antioxidant and anti-diabetic properties. A highly diverse and an extremely ecological niche of different species can be valuable genomic resources for productivity enhancement. It is one of the most underutilized crops for food security and animal feeds. In spite of huge species diversity, only three species of Vigna have been sequenced; thus, there is a need for molecular markers for the remaining species. Computational approach of microsatellite marker discovery along with evaluation of polymorphism utilizing available genomic data of different genotypes can be a quick and an economical approach for genomic resource development. Cross-species transferability by e-PCR over available genomes can further prioritize the potential SSR markers, which could be used for genetic diversity and population differentiation of the remaining species saving cost and time. We present VigSatDB-the world's first comprehensive microsatellite database of genus Vigna, containing >875 K putative microsatellite markers with 772 354 simple and 103 865 compound markers mined from six genome assemblies of three Vigna species, namely, Vigna radiata (Mung bean), Vigna angularis (Adzuki bean) and Vigna unguiculata (Cowpea). It also contains 1976 validated published markers. Markers can be selected on the basis of chromosomes/location specificity, and primers can be generated using Primer3core tool integrated at backend. Efficacy of VigSatDB for microsatellite loci genotyping has been evaluated by 15 markers over a panel of 10 diverse genotype of V. radiata. Our web genomic resources can be used in diversity analysis, population and varietal differentiation, discovery of quantitative trait loci/genes, marker-assisted varietal improvement in endeavor of Vigna crop productivity and management.

Rheological evaluations and molecular marker analysis of cultivated bread wheat varieties of India

Abstract: The aim of this study was to screen Indian cultivated wheat varieties and list out the parameters/genes required to be improved for an end-product. Therefore, 30 Indian wheat varieties under cultivation by farmers were screened for 14 physico-chemical and rheological parameters, sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) for high molecular weight glutenin subunits (HMW-GS), DNA based molecular markers for low molecular weight glutenin subunits (LMW-GS) and puroindolines (Pin) genes. Based on grain texture, sedimentation value, farinographic, alveographic, HMW-GS and LMW-GS and biscuit making parameters, HS490 was found to be a highly suited for biscuit and soft wheat products. HI1563 and DBW14 were also found to possess characteristics such as low protein, low to medium SDS-sedimentation value and combination of 2*, 7+8 and 2+12 (HMW-GS). DBW14 also had LMW alleles desirable for biscuit quality. DBW14 needs to be improved for grain softness to make it suitable for biscuit quality while both grain softness and LMW alleles need to be improved for HI1563 to improve its biscuit spread factor and alveographic indices for extensible gluten. Rest varieties showed moderate to very strong gluten but the gluten lacked extensibility. Only four varieties K307, DBW39, NI5439 and DBW17 possessed high flour protein and moderately strong gluten. They had more balanced deformation energy (W) and configuration ratio (P/L) combination suggestive of strong and extensible gluten needed for raised bread making. Marker assisted backcross breeding is suggested as solution to produce end-use specific varieties where appropriate alleles at only a few loci need to be incorporated.

HRGPred: Prediction of herbicide resistant genes with k-mer nucleotide compositional features and support vector machine.

Abstract: Herbicide resistance (HR) is a major concern for the agricultural producers as well as environmentalists. Resistance to commonly used herbicides are conferred due to mutation(s) in the genes encoding herbicide target sites/proteins (GETS). Identification of these genes through wet-lab experiments is time consuming and expensive. Thus, a supervised learning-based computational model has been proposed in this study, which is first of its kind for the prediction of seven classes of GETS. The cDNA sequences of the genes were initially transformed into numeric features based on the k-mer compositions and then supplied as input to the support vector machine. In the proposed SVM-based model, the prediction occurs in two stages, where a binary classifier in the first stage discriminates the genes involved in conferring the resistance to herbicides from other genes, followed by a multi-class classifier in the second stage that categorizes the predicted herbicide resistant genes in the first stage into any one of the seven resistant classes. Overall classification accuracies were observed to be ~89% and >97% for binary and multi-class classifications respectively. The proposed model confirmed higher accuracy than the homology-based algorithms viz., BLAST and Hidden Markov Model. Besides, the developed computational model achieved ~87% accuracy, while tested with an independent dataset. An online prediction server HRGPred ( http://cabgrid.res.in:8080/hrgpred ) has also been established to facilitate the prediction of GETS by the scientific community.

Small area estimation combining data from two surveys

Abstract: Many often two surveys conducted independently with same or different objectives, may have some auxiliary variables in common. The first survey, which has small sample size, collects both variable ...