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.

Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean ( Glycine max L.)

Abstract: Selection of informative genes is an important problem in gene expression studies. The small sample size and the large number of genes in gene expression data make the selection process complex. Further, the selected informative genes may act as a vital input for gene co-expression network analysis. Moreover, the identification of hub genes and module interactions in gene co-expression networks is yet to be fully explored. This paper presents a statistically sound gene selection technique based on support vector machine algorithm for selecting informative genes from high dimensional gene expression data. Also, an attempt has been made to develop a statistical approach for identification of hub genes in the gene co-expression network. Besides, a differential hub gene analysis approach has also been developed to group the identified hub genes into various groups based on their gene connectivity in a case vs. control study. Based on this proposed approach, an R package, i.e., dhga (https://cran.r-project.org/web/packages/dhga) has been developed. The comparative performance of the proposed gene selection technique as well as hub gene identification approach was evaluated on three different crop microarray datasets. The proposed gene selection technique outperformed most of the existing techniques for selecting robust set of informative genes. Based on the proposed hub gene identification approach, a few number of hub genes were identified as compared to the existing approach, which is in accordance with the principle of scale free property of real networks. In this study, some key genes along with their Arabidopsis orthologs has been reported, which can be used for Aluminum toxic stress response engineering in soybean. The functional analysis of various selected key genes revealed the underlying molecular mechanisms of Aluminum toxic stress response in soybean.

Harnessing Next Generation Sequencing in Climate Change: RNA-Seq Analysis of Heat Stress-Responsive Genes in Wheat (Triticum aestivum L.)

Abstract: Wheat is a staple food worldwide and provides 40% of the calories in the diet. Climate change and global warming pose a threat to wheat production, however, and demand a deeper understanding of how heat stress might impact wheat production and wheat biology. However, it is difficult to identify novel heat stress associated genes when the genomic information is not available. Wheat has a very large and complex genome that is about 37 times the size of the rice genome. The present study sequenced the whole transcriptome of the wheat cv. HD2329 at the flowering stage, under control (22°±3°C) and heat stress (42°C, 2 h) conditions using Illumina HiSeq and Roche GS-FLX 454 platforms. We assembled more than 26.3 and 25.6 million high-quality reads from the control and HS-treated tissues transcriptome sequences respectively. About 76,556 (control) and 54,033 (HS-treated) contigs were assembled and annotated de novo using different assemblers and a total of 21,529 unigenes were obtained. Gene expression profile showed significant differential expression of 1525 transcripts under heat stress, of which 27 transcripts showed very high (>10) fold upregulation. Cellular processes such as metabolic processes, protein phosphorylation, oxidations-reductions, among others were highly influenced by heat stress. In summary, these observations significantly enrich the transcript dataset of wheat available on public domain and show a de novo approach to discover the heat-responsive transcripts of wheat, which can accelerate the progress of wheat stress-genomics as well as the course of wheat breeding programs in the era of climate change.

Identification of Differentially Expressed Genes in RNA-seq Data of Arabidopsis thaliana: A Compound Distribution Approach

Abstract: Gene expression is the process by which information from a gene is used in the synthesis of a functional gene product, which may be proteins. A gene is declared differentially expressed if an observed difference or change in read counts or expression levels between two experimental conditions is statistically significant. To identify differentially expressed genes between two conditions, it is important to find statistical distributional property of the data to approximate the nature of differential genes. In the present study, the focus is mainly to investigate the differential gene expression analysis for sequence data based on compound distribution model. This approach was applied in RNA-seq count data of Arabidopsis thaliana and it has been found that compound Poisson distribution is more appropriate to capture the variability as compared with Poisson distribution. Thus, fitting of appropriate distribution to gene expression data provides statistically sound cutoff values for identifying differentially expressed genes.

Assessment of spatial and temporal variations in water quality by the application of multivariate statistical methods in the Kali River, Uttar Pradesh, India

Abstract: The Kali River is a significant source of surface water as well as the main tributary of River Hindon that flows through major cities of western Uttar Pradesh, India. It flows throughout the urban and industrial regions; hence, it carries various amounts of pollutant. Therefore, a study was conducted to examine spatial-temporal variations in river water quality by determining physicochemical variables and heavy metal concentrations at seventeen sampling stations (S1-S17) throughout the river stretch. Various physicochemical variables, namely pH, EC, TDS, turbidity, BOD, COD, TH, TA, Ca, Mg, Na, K, HCO3-, Cl-, SO42-, NO3-, and PO43- were higher in summer than in winter. The order of mean metal concentrations was Fe > Pb > Mn > Ni > Zn > Cu > Cr > Cd. The relationships among measured physicochemical variables and pollution index were examined. Furthermore, multivariate statistical methods were used to assess spatial-temporal variation in water quality to identify current pollution sources and validate results. Water quality index and comprehensive pollution index indicated that the Kali River was less polluted from S1 to S8. However, downstream sampling sites were polluted. Pollution starts from S9 and drastically increases at and beyond S13 because of effluents from industries and sugar mills in Muzaffarnagar. The study suggests cleaning the downstream region of river to restore human health and flora and fauna in the river ecosystem.