Indian Agricultural Statistics Research Institute

About: Indian Agricultural Statistics Research Institute is a facility organization based out in New Delhi, India. It is known for research contribution in the topics: Population & Small area estimation. The organization has 454 authors who have published 870 publications receiving 7987 citations.

miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles.

Abstract: Formation of mature miRNAs and their expression is a highly controlled process. It is very much dependent upon the post-transcriptional regulatory events. Recent findings suggest that several RNA binding proteins beyond Drosha/Dicer are involved in the processing of miRNAs. Deciphering of conditional networks for these RBP-miRNA interactions may help to reason the spatio-temporal nature of miRNAs which can also be used to predict miRNA profiles. In this direction, >25TB of data from different platforms were studied (CLIP-seq/RNA-seq/miRNA-seq) to develop Bayesian causal networks capable of reasoning miRNA biogenesis. The networks ably explained the miRNA formation when tested across a large number of conditions and experimentally validated data. The networks were modeled into an XGBoost machine learning system where expression information of the network components was found capable to quantitatively explain the miRNAs formation levels and their profiles. The models were developed for 1,204 human miRNAs whose accurate expression level could be detected directly from the RNA-seq data alone without any need of doing separate miRNA profiling experiments like miRNA-seq or arrays. A first of its kind, miRbiom performed consistently well with high average accuracy (91%) when tested across a large number of experimentally established data from several conditions. It has been implemented as an interactive open access web-server where besides finding the profiles of miRNAs, their downstream functional analysis can also be done. miRbiom will help to get an accurate prediction of human miRNAs profiles in the absence of profiling experiments and will be an asset for regulatory research areas. The study also shows the importance of having RBP interaction information in better understanding the miRNAs and their functional projectiles where it also lays the foundation of such studies and software in future.

Comparative transcriptome analysis of female and hermaphrodite flower buds in bitter gourd (Momordica charantia L.) by RNA sequencing

Abstract: Bitter gourd, Momordica charantia L., is an economically and nutraceutically important crop in the family Cucurbitaceae. The predominant sex-form of bitter gourd is monoecious, but a gynoecious form also exists which is controlled by a single recessive gene (gy-1). Whole transcriptome sequencing of female and hermaphrodite flower buds of bitter gourd was performed using Roche 454 parallel pyrosequencing technology. A total of 1,994,113 expressed sequence tags (ESTs) were generated, of which 834,476 and 1,159,637 ESTs were obtained from female and hermaphrodite flower buds of the gynoecious bitter gourd inbred line, DBGY-201, respectively. The unigenes were functionally annotated by comparing their sequences with various protein and functional domain databases and assigned gene ontology (GO) terms. A total of 477 annotated unigenes were found to be significantly differentially expressed between the female and hermaphrodite flower buds of DBGY-201, of which 237 were down-regulated and 59 were up-reg...

Small Area Estimation Under Spatial Nonstationarity

Abstract: A geographical weighted empirical best linear unbiased predictor (GWEBLUP) for a small area average is proposed, and an estimator of its conditional mean squared error is developed. The popular empirical best linear unbiased predictor under the linear mixed model is obtained as a special case of the GWEBLUP. Empirical results using both model-based and design-based simulations, with the latter based on two real data sets, show that the GWEBLUP predictor can lead to efficiency gains when spatial nonstationarity is present in the data. A practical gain from using the GWEBLUP is in small area estimation for out of sample areas. In this case the efficient use of geographical information can potentially improve upon conventional synthetic estimation.

Strengthening soil taxonomy ontology software for description and classification of USDA soil taxonomy up to soil series

Abstract: Software's using ontology as their knowledge base are of due importance now a days due to their synergism with agents and Semantic Web Architecture. Ontologies provide domain language by defining domain concepts and relationships between them which is ultimately meaningful to both humans and machines. This is IEEE standard Web Ontology Language (OWL). Taxonomies are well-defined hierarchy existing in a standardized form to describe real world concepts in various domains of knowledge. The indispensable role of ontology in Agriculture is to convert the unstructured knowledge into structured one, sharing across application. Das (2010) and Das et al. (2012) developed Soil Ontology for USDA soil taxonomy for orders available in India to only Sub group level. This newly developed Soil Ontology has been strengthened and is now available up to family and series level for orders in India and also for the twelve orders worldwide. The web based application follows N-tier architecture. By mentioning the soil properties one can easily get information related to soil taxonomy and also newly found soils can be classified. Information edition or addition facilities of soil taxonomy are available with domain experts. Advance Search and series navigation keys can be use to easily get the detailed information of taxonomic hierarchy and state wise series description respectively. Its knowledge base is in the form of Ontology.