SBMDb: First whole genome putative microsatellite DNA marker database of sugarbeet for bioenergy and industrial applications

© 2015 The Author(s) 2015. DNA marker plays important role as valuable tools to increase crop productivity by finding plausible answers to genetic variations and linking the Quantitative Trait Loci (QTL) of beneficial trait. Prior approaches in development of Short Tandem Repeats (STR) markers were time consuming and inefficient. Recent methods invoking the development of STR markers using whole genomic or transcriptomics data has gained wide importance with immense potential in developing breeding and cultivator improvement approaches. Availability of whole genome sequences and in silico approaches has revolutionized bulk marker discovery. We report world's first sugarbeet whole genome marker discovery having 145 K markers along with 5 K functional domain markers unified in common platform using MySQL, Apache and PHP in SBMDb. Embedded markers and corresponding location information can be selected for desired chromosome, location/interval and primers can be generated using Primer3 core, integrated at backend. Our analyses revealed abundance of 'mono' repeat (76.82%) over 'di' repeats (13.68%). Highest density (671.05 markers/Mb) was found in chromosome 1 and lowest density (341.27 markers/Mb) in chromosome 6. Current investigation of sugarbeet genome marker density has direct implications in increasing mapping marker density. This will enable present linkage map having marker distance of ∼2 cM, i.e. from 200 to 2.6 Kb, thus facilitating QTL/gene mapping. We also report e-PCR-based detection of 2027 polymorphic markers in panel of five genotypes. These markers can be used for DUS test of variety identification and MAS/GAS in variety improvement program. The present database presents wide source of potential markers for developing and implementing new approaches for molecular breeding required to accelerate industrious use of this crop, especially for sugar, health care products, medicines and color dye. Identified markers will also help in improvement of bioenergy trait of bioethanol and biogas production along with reaping advantage of crop efficiency in terms of low water and carbon footprint especially in era of climate change

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

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

Structural SCOP superfamily level classification using unsupervised machine learning , IEEE Transaction on Computational Biology and Bioinformatics, 9: 601-608

Not AvailableOne of the major research directions in bioinformatics is that of assigning superfamily classification to a given set of proteins. The classification reflects the structural, evolutionary, and functional relatedness. These relationships are embodied in a hierarchical classification, such as the Structural Classification of Protein (SCOP), which is mostly manually curated. Such a classification is essential for the structural and functional analyses of proteins. Yet a large number of proteins remain unclassified. In this study, we have proposed an unsupervised machine learning approach to classify and assign a given set of proteins to SCOP superfamilies. In the method, we have constructed a database and similarity matrix using P-values obtained from an all-against-all BLAST run and trained the network with the ART2 unsupervised learning algorithm using the rows of the similarity matrix as input vectors, enabling the trained network to classify the proteins from 0.82 to 0.97 f-measure accuracy. The performance of ART2 has been compared with that of spectral clustering, Random forest, SVM, and HHpred. ART2 performs better than the others except HHpred. HHpred performs better than ART2 and the sum of errors is smaller than that of the other methods evaluated.Not Availabl

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Not AvailableFeed scarcity results in large yield gaps in livestock production in low- and middle-income countries (LMC). In India, for example, the feed deficit in terms of digestible crude protein and total digestible nutrients is estimated to be 50 and 60%, respectively. Feed supply scenarios in India suggest that on a dry matter basis, crop residues (i.e., straws, stover, and haulms, the byproducts from grain production) contribute about 70% to feed resources. Crop residues are therefore the single most important feed resource in India, a scenario that likely holds good for many LMCs. Crop residues and crop byproduct-based feeding systems have the advantage of low direct and indirect food competing feed resourcing, since no or few grains (i.e. food) are fed to livestock and no land or water had to be exclusively allocated to feed production. Considering the importance of crop residues as feed resources, it comes as no surprise that many attempts to upgrade crop residues post-harvest by physical, chemical and biological treatments have been made. However, comparatively little uptake of these technologies has been observed. The lack of adoption of post-harvest approaches to improvement of crop residues gave way to a new research paradigm of targeted improvement of crop residues by plant breeding and selection at source. It was in the mid-nineties that the International Livestock Research Institute (ILRI) and the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) started to explore concomitant improvement of grain and crop residue traits as a major cross-CGIAR collaborative project. Initially more attention was given to typical rain-fed crops such as sorghum, pearl millet, groundnut and cowpea than to crops such as rice and wheat which are usually grown in irrigated areas such as the Indo-Gangetic Plains, Eastern Gangetic Plains and Southern India. In 2016 rice and wheat straw production potentially available as fodder was about 113 and 78 million tons respectively in India, contributing about 67% of all cereal straws and about 37% of the total feed metabolizable energy from straws. Using three scenarios, a 10% increase in rice and wheat straw yield (scenario 1), a 10% increase in rice and wheat straw metabolizable energy content (scenario 2) and a combination of the two (scenario 3) the paper estimates that at all India level an increase in yield (scenario 1) would add around 23 million tons while increase in the quality of the straw (scenario 2) would add additional 138 × 109 MJ ME and a combination of yield and quality increase (scenario 3) would add around 23 million tons and additional 291 × 109 MJ ME. Translated in terms of milk and meat production excluding the maintenance requirements the additional energy in scenario 1 or 2 would be equal to 27.6 million tons of milk or 9.2 million tons of mutton and the corresponding figures in scenario three would be 58.2 and 19.4 million tons, respectively. Hence, the opportunity lies with whole plant optimization which needs to be worked out by the plant and animal scientist together for mitigating the shortage of feed resources in LMCs.Not Availabl

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Not AvailableInadequate feeding is the major factor for low livestock productivity in India. In dairying, feed cost is a major input and feeding practices has to be improved to ensure profits. Still the small scale farmers are following traditional feeding practices and fail to address the complexities involved in ration formulation. To address the complexities in ration balancing based on the nutrient requirements for different categories of livestock, nutrient composition of wide range of feed resources and the cost - a number of expert systems have been developed. However existing expert systems have not been widely used by majority of small farmers due to lack of awareness, access and basic skills required to operate. To address these limitations, “Feed Assist” a farmer friendly expert system for balanced feeding of dairy animals at least cost has been developed using linear programming. “Feed Assist” does not require much expertise to operate and enables the farmers to formulate least cost rations for different categories of livestock using locally available feed resources.ICAR-National Institute of Animal Nutrition and Physiolog

PolyMorphPredict: Web server for rapid polymorphic SSR locus discovery from whole genome and transcriptome data

Not AvailableMicrosatellites 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.Not Availabl

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Not AvailableMicrosatellites 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.Not Availabl

Table_6_PolyMorphPredict: A Universal Web-Tool for Rapid Polymorphic Microsatellite Marker Discovery From Whole Genome and Transcriptome Data.DOCX

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.</p

Table_1_PolyMorphPredict: A Universal Web-Tool for Rapid Polymorphic Microsatellite Marker Discovery From Whole Genome and Transcriptome Data.XLSX

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.</p