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Not AvailableClassification is one of the tasks that are most frequently carried out in real world applications. A large number of techniques have been developed based on statistics and machine learning methods. These classification techniques usually suffer from various limitations, and there is no single technique that works best for all classification problems. Two major drawbacks in existing techniques are accuracy and lack of actionable knowledge from results. To overcome these problems, a novel algorithm called Multi-Branch Ferns (MBFerns), and R-package has been developed to build multi-branch ferns (multi-branch decision tree) and to generate key features from training dataset employing Naïve Bayesian probabilistic model as classifier. The proposed algorithm performs well for general classification problems and extracting actionable knowledge from training data. The proposed method has been evaluated with best existing classification methods namely, Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Network (ANN) on medical benchmark data, available at https://archive.ics.uci.edu/ml/datasets/ such as Breast Cancer, Cryotherapy, Cardiotocography, Dermatology, Echocardiogram, EEG Eye State, Fertility, Haberman's Survival, Hepatitis, Indian Liver Patient, Mammographic Mass, Parkinsons, etc. Detailed investigation on proposed Multi-Branch Ferns (MBFerns) with respect to accuracy, time, space complexity and knowledge discovery has also been presented.CABin scheme grant (FN/Agril-Edn./4-1/2013-A&P) Indian Council of Agricultural Researc

A Novel Way of Comparing Protein 3D Structure Using Graph Partitioning Approach

Not AvailableAlignment and comparison of protein 3D structures is an important and fundamental task in structural biology to study evolutionary, functional and structural relatedness among proteins. Since two decades, the research on protein structure alignment has been taken up on priority and numbers of research articles are being published. There are incremental advances over previous efforts, and still these methods continue to improve over the time and still this is an open problem in structural biology. A novel methodology has been developed for comparing protein 3D structure by employing conversion of pair of protein 3D structures into 2D graphs (undirected weighted graph), partitioning of 2D graphs into sub-graphs, matching sub-graphs with main graphs and finally these sub-graphs matches calculates similarity between the pair of proteins. The proposed method has been implemented in MATLAB and R Package. The performance of the developed methodology is tested with four existing best methods such as CE, jFATCAT, TM_Align and Dali on 100 proteins benchmark dataset with SCOP database. The proposed method is efficient in terms of time complexity, accuracy, grouping of proteins in relevant structural groups and provides additional information towards non-bonded interactions and sub-graphs indicates the dominance of secondary structure.Not Availabl

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Not AvailableThe ICAR-National Institute of Animal Nutrition and Physiology, Bangalore, funded by Indian Council of Agricultural Research (ICAR), Government of India, New Delhi has developed a tool named FeedBase-India, using secondary datasets. The software assesses feed supply and demand for livestock in India. The Ethiopian Agricultural Transformation Agency (ATA) showed considerable interest in this innovative software, which resulted in a collaboration between ICAR, ATA and the International Livestock Research Institute (ILRI), to adopt and adapt the concept for feed supply–demand analysis of livestock in Ethiopia. The supply is estimated from cropping, land use pattern, demand from livestock census and nutrient requirements for various categories of livestock based on their body maintenance, production and reproduction potentials. Key stakeholders were familiarized with the concepts, approaches, and software at an inception workshop held in Addis Ababa, Ethiopia between 1st and 3rd August 2017 at the ILRI campus. The database and tools were modified for Ethiopian conditions based on the data collected by ATA in two districts from each of the four main regions of Ethiopia. The tool relationally arranges these datasets using algorithms that connect feed quantity and quality to livestock maintenance and production requirements to calculate surpluses, deficits or sufficiency of feed biomass and of key nutrients such as dry matter, protein, total digestible nutrients and metabolizable energy. This enables users to compare and prioritize feed and animal interventions for impact. This will support researchers, development agencies, governments, industry and farmers in constructive planning and decision making, resulting in higher livestock production and productivity, feed use efficiency and reduced cost of feeding.Not Availabl

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Not AvailableTea is a cross-pollinated woody perennial plant, which is why, application of conventional breeding is limited for its genetic improvement. However, lack of the genome-wide high-density SNP markers and genome-wide haplotype information has greatly hampered the utilization of tea genetic resources toward fast-track tea breeding programs. To address this challenge, we have generated a first-generation haplotype map of tea (Tea HapMap-1). Out-crossing and highly heterozygous nature of tea plants, make them more complicated for DNA-level variant discovery.Not Availabl

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Not AvailablePearl millet (Pennisetum glaucum L.) is affected by drought stress, affecting crop productivity and survival. Long non-coding RNAs (lncRNAs) are reported to play a vital role in the response to drought stress. LncRNAs represent a major part of non-protein coding RNAs and are present prevalently. These are involved in various biological processes, which may functionally act as RNA rather than getting transcribed as protein. We targeted genome-wide identification of lncRNAs in pearl millet from root and leaf tissues subjected to drought stress. A total of 879 lncRNAs were identified, out of which 209 (leaf control, root control), 198 (leaf treated, root treated), 115 (leaf control, leaf treated) and 194 (root control, root treated) were differentially expressed. Two lncRNAs were found as potential target mimics of three miRNAs from the miRBase database. Gene ontology study revealed that drought-responsive lncRNAs are involved in biological processes like ‘metabolic process’ and ‘cellular process’, molecular functions like ‘binding’ and ‘catalytic activities’ and cellular components like ‘cell’, ‘cell part’ and ‘membrane part’. LncRNA-miRNA-mRNA network shows that it plays a vital role in the stress-responsive mechanism through their activities in hormone signal transduction, response to stress, response to auxin and transcription factor activity. Only four lncRNAs were found to get a match with the lncRNAs present in the plant lncRNA database CANTATAdb, which shows its poorly conserved nature among species. This information has been cataloged in the pearl millet drought-responsive long non-coding RNA database (PMDlncRDB). The discovered lncRNAs can be used in the improvement of important traits, as well as CISPR-Cas technology, in the editing of ncRNAs in plants for trait improvement. Such a study will increase our understanding of the expression behavior of lncRNAs, as well as its underlying mechanisms under drought stress in pearl millet.Not Availabl

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Not AvailableBlack pepper (Piper nigrum), the “King of Spices,” is an economically important spice in India and is known for its medicinal and cultural values. SSRs, the tandem repeats of small DNA sequences, are often polymorphic in nature with diverse applications. For population structure, QTL/gene discovery, MAS, and diversity analysis, it is imperative to have their location specificity. The existing PinigSSRdb catalogs ∼70K putative SSR markers but these are anonymous (unknown chromosomal location), based on 916 scaffolds rather than 26 chromosomes. Under this study, we generated ddRAD sequence data of 29 black pepper genotypes from all over India, being low-cost and most efficient technique for the identification of polymorphicmarkers. Themajor limitation of ddRAD with compromised/non-uniform coverage has been successfully overcome by taking advantage of chromosome-wise data availability. The latest black pepper genome assembly was used to extract genome-wide SSRs. A total of 276,230 genomic SSRs were mined distributed over 26 chromosomes, with relative density of 362.88 SSRs/Mb and average distance of 2.76 Kb between two SSRs. This assembly was also used to find the polymorphic SSRs in the generated GBS data of 29 black pepper genotypes utilizing rapid and cost-effective method giving 3,176 polymorphic SSRs, out of which 2015 were found to be hypervariable. The developed web-genomic resource, BlackP2MSATdb (http://webtom.cabgrid.res.in/blackp2msatdb/), is the largest and first reported web resource for genomic and polymorphic SSRs of black pepper, which is useful to develop varietal signature, coreset, physical map, QTL/gene identification, and MAS in endeavor of black pepper production.Not Availabl

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Not AvailableWater buffalo (Bubalus bubalis) are an important animal resource that contributes milk, meat, leather, dairy products, and power for plowing and transport. However, mastitis, a bacterial disease affecting milk production and reproduction efficiency, is most prevalent in populations having intensive selection for higher milk yield, especially where the inbreeding level is also high. Climate change and poor hygiene management practices further complicate the issue. The management of this disease faces major challenges, like antibiotic resistance, maximum residue level, horizontal gene transfer, and limited success in resistance breeding. Bovine mastitis genome wide association studies have had limited success due to breed differences, sample sizes, and minor allele frequency, lowering the power to detect the diseases associated with SNPs. In this work, we focused on the application of targeted gene panels (TGPs) in screening for candidate gene association analysis, and how this approach overcomes the limitation of genome wide association studies. This work will facilitate the targeted sequencing of buffalo genomic regions with high depth coverage required to mine the extremely rare variants potentially associated with buffalo mastitis. Although the whole genome assembly of water buffalo is available, neither mastitis genes are predicted nor TGP in the form of web-genomic resources are available for future variant mining and association studies. Out of the 129 mastitis associated genes of cattle, 101 were completely mapped on the buffalo genome to make TGP. This further helped in identifying rare variants in water buffalo. Eighty-five genes were validated in the buffalo gene expression atlas, with the RNA-Seq data of 50 tissues. The functions of 97 genes were predicted, revealing 225 pathways. The mastitis proteins were used for protein-protein interaction network analysis to obtain additional cross-talking proteins. A total of 1,306 SNPs and 152 indels were identified from 101 genes. Water Buffalo-MSTdb was developed with 3-tier architecture to retrieve mastitis associated genes having genomic coordinates with chromosomal details for TGP sequencing for mining of minor alleles for further association studies. Lastly, a web-genomic resource was made available to mine variants of targeted gene panels in buffalo for mastitis resistance breeding in an endeavor to ensure improved productivity and the reproductive efficiency of water buffalo.Not Availabl

A pilot-scale comparison between single and double-digest RAD markers generated using GBS strategy in sesame (Sesamum indicum L.).

To reduce the genome sequence representation, restriction site-associated DNA sequencing (RAD-seq) protocols is being widely used either with single-digest or double-digest methods. In this study, we genotyped the sesame population (48 sample size) in a pilot scale to compare single and double-digest RAD-seq (sd and ddRAD-seq) methods. We analysed the resulting short-read data generated from both protocols and assessed their performance impacting the downstream analysis using various parameters. The distinct k-mer count and gene presence absence variation (PAV) showed a significant difference between the sesame samples studied. Additionally, the variant calling from both datasets (sdRAD-seq and ddRAD-seq) exhibits a significant difference between them. The combined variants from both datasets helped in identifying the most diverse samples and possible sub-groups in the sesame population. The most diverse samples identified from each analysis (k-mer, gene PAV, SNP count, Heterozygosity, NJ and PCA) can possibly be representative samples holding major diversity of the small sesame population used in this study. The best possible strategies with suggested inputs for modifications to utilize the RAD-seq strategy efficiently on a large dataset containing thousands of samples to be subjected to molecular analysis like diversity, population structure and core development studies were discussed