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.

Locus minimization in breed prediction using artificial neural network approach.

Abstract: Molecular markers, viz. microsatellites and single nucleotide polymorphisms, have revolutionized breed identification through the use of small samples of biological tissue or germplasm, such as blood, carcass samples, embryos, ova and semen, that show no evident phenotype. Classical tools of molecular data analysis for breed identification have limitations, such as the unavailability of referral breed data, causing increased cost of collection each time, compromised computational accuracy and complexity of the methodology used. We report here the successful use of an artificial neural network (ANN) in background to decrease the cost of genotyping by locus minimization. The webserver is freely accessible (http://nabg.iasri.res.in/bisgoat) to the research community. We demonstrate that the machine learning (ANN) approach for breed identification is capable of multifold advantages such as locus minimization, leading to a drastic reduction in cost, and web availability of reference breed data, alleviating the need for repeated genotyping each time one investigates the identity of an unknown breed. To develop this model web implementation based on ANN, we used 51,850 samples of allelic data of microsatellite-marker-based DNA fingerprinting on 25 loci covering 22 registered goat breeds of India for training. Minimizing loci to up to nine loci through the use of a multilayer perceptron model, we achieved 96.63% training accuracy. This server can be an indispensable tool for identification of existing breeds and new synthetic commercial breeds, leading to protection of intellectual property in case of sovereignty and bio-piracy disputes. This server can be widely used as a model for cost reduction by locus minimization for various other flora and fauna in terms of variety, breed and/or line identification, especially in conservation and improvement programs.

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.

The genome of walking catfish Clarias magur (Hamilton, 1822) unveils the genetic basis that may have facilitated the development of environmental and terrestrial adaptation systems in air-breathing catfishes.

Abstract: The walking catfish Clarias magur (Hamilton, 1822) (magur) is an important catfish species inhabiting the Indian subcontinent. It is considered as a highly nutritious food fish and has the capability to walk to some distance, and survive a considerable period without water. Assembly, scaffolding and several rounds of iterations resulted in 3,484 scaffolds covering ∼94% of estimated genome with 9.88 Mb largest scaffold, and N50 1.31 Mb. The genome possessed 23,748 predicted protein encoding genes with annotation of 19,279 orthologous genes. A total of 166 orthologous groups represented by 222 genes were found to be unique for this species. The Computational Analysis of gene Family Evolution (CAFE) analysis revealed expansion of 207 gene families and 100 gene families have rapidly evolved. Genes specific to important environmental and terrestrial adaptation, viz. urea cycle, vision, locomotion, olfactory and vomeronasal receptors, immune system, anti-microbial properties, mucus, thermoregulation, osmoregulation, air-breathing, detoxification, etc. were identified and critically analysed. The analysis clearly indicated that C. magur genome possessed several unique and duplicate genes similar to that of terrestrial or amphibians' counterparts in comparison to other teleostean species. The genome information will be useful in conservation genetics, not only for this species but will also be very helpful in such studies in other catfishes.

Robustness of some designs against missing observations

Abstract: This paper investigates the robustness of certain designs against the loss of specified sets of observations. As a measure of robustness, we consider the efficiency of the residual design.

Process optimization for the supercritical carbondioxide extraction of lycopene from ripe grapefruit (Citrus paradisi) endocarp.

Abstract: In this study, an underutilized citrus family fruit named grapefruit was explored for the extraction of lycopene using supercritical carbon dioxide (CO2) extraction technique. An experimental design was developed using response surface methodology to investigate the effect of supercritical carbon dioxide (CO2) operating parameter viz., pressure, temperature, CO2 flow rate, and extraction time on the extraction yield of lycopene yield from grapefruit. A total of 30 sets of experiments were conducted with six central points. The statistical model indicated that extraction pressure and extraction time individually, and their interaction, significantly affected the lycopene yield. The central composite design showed that the polynomial regression models developed were in agreement with the experimental results, with R2 of 0.9885. The optimum conditions for extraction of lycopene from grapefruit were 305 bar pressure, 35 g/min CO2 flow rate, 135 min of extraction time, and 70 °C temperature.