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.

Genetic Variability for Chickpea (Cicer arietinum L.) under Late Sown Season

Abstract: Forty four promising lines of chickpea were grown in RBD with three replications under late sown season. The maximum genotypic coefficient of variation was noticed for damaged pod percentage, total number of seeds per plant and total number of pods per plant. Days to 50% flowering, days to maturity, plant height, 100 seed weight and seed yield per plant showing high heritability coupled with medium genetic advance as percentage of mean, whereas, damage pod percentage, number of seeds per plant and number of pods per plant showing medium heritability and high genetic advance as percentage of mean. Seed yield per plant showed high significant positive correlation with total number of seeds per plant, total number of pods per plant, biological yield, plant height and 100 seed weight, whereas, significant negative correlation with days to 50% flowering and damaged pod percentage. Based on D2 cluster analysis, the forty four genotypes were grouped into nine clusters, depending upon the genetic constitution of the genotypes. The maximum intra cluster distance was found in cluster IV followed by cluster I, cluster VI and cluster VIII. Inter cluster values varied from 2.75 to 9.02. Total pods per plant, 100 seed weight, days to maturity, biological yield and seed yield per plant considered as selection criteria, while selecting superior genotypes under late condition. High yielding advanced breeding lines viz. JG 14, JSC 56, AKG 70, JG 9602974, BG 3005, PG 03110, Phule G 00108 were found suitable under late sown condition.

Transcriptome profiling of differentially expressed genes in cytoplasmic male-sterile line and its fertility restorer line in pigeon pea (Cajanus cajan L.).

Abstract: Pigeon pea (Cajanus cajan L.) is the sixth major legume crop widely cultivated in the Indian sub-continent, Africa, and South-east Asia. Cytoplasmic male-sterility (CMS) is the incompetence of flowering plants to produce viable pollens during anther development. CMS has been extensively utilized for commercial hybrid seeds production in pigeon pea. However, the molecular basis governing CMS in pigeon pea remains unclear and undetermined. In this study transcriptome analysis for exploring differentially expressed genes (DEGs) between cytoplasmic male-sterile line (AKCMS11) and its fertility restorer line (AKPR303) was performed using Illumina paired-end sequencing. A total of 3167 DEGs were identified, of which 1432 were up-regulated and 1390 were down-regulated in AKCMS11 in comparison to AKPR303. By querying, all the 3167 DEGs against TAIR database, 34 pigeon pea homologous genes were identified, few involved in pollen development (EMS1, MS1, ARF17) and encoding MYB and bHLH transcription factors with lower expression in the sterile buds, implying their possible role in pollen sterility. Many of these DEGs implicated in carbon metabolism, tricarboxylic acid cycle (TCA), oxidative phosphorylation and elimination of reactive oxygen species (ROS) showed reduced expression in the AKCMS11 (sterile) buds. The comparative transcriptome findings suggest the potential role of these DEGs in pollen development or abortion, pointing towards their involvement in cytoplasmic male-sterility in pigeon pea. The candidate DEGs identified in this investigation will be highly significant for further research, as they could lend a comprehensive basis in unravelling the molecular mechanism governing CMS in pigeon pea.

Identification of donor splice sites using support vector machine: a computational approach based on positional, compositional and dependency features

Abstract: Identification of splice sites is essential for annotation of genes. Though existing approaches have achieved an acceptable level of accuracy, still there is a need for further improvement. Besides, most of the approaches are species-specific and hence it is required to develop approaches compatible across species. Each splice site sequence was transformed into a numeric vector of length 49, out of which four were positional, four were dependency and 41 were compositional features. Using the transformed vectors as input, prediction was made through support vector machine. Using balanced training set, the proposed approach achieved area under ROC curve (AUC-ROC) of 96.05, 96.96, 96.95, 96.24 % and area under PR curve (AUC-PR) of 97.64, 97.89, 97.91, 97.90 %, while tested on human, cattle, fish and worm datasets respectively. On the other hand, AUC-ROC of 97.21, 97.45, 97.41, 98.06 % and AUC-PR of 93.24, 93.34, 93.38, 92.29 % were obtained, while imbalanced training datasets were used. The proposed approach was found comparable with state-of-art splice site prediction approaches, while compared using the bench mark NN269 dataset and other datasets. The proposed approach achieved consistent accuracy across different species as well as found comparable with the existing approaches. Thus, we believe that the proposed approach can be used as a complementary method to the existing methods for the prediction of splice sites. A web server named as ‘HSplice’ has also been developed based on the proposed approach for easy prediction of 5′ splice sites by the users and is freely available at http://cabgrid.res.in:8080/HSplice .