Combining ability and heterosis analysis for fibre yield and quality parameters in roselle (Hibiscus sabdariffa L.)

Roselle (Hibiscus sabdariffa L.) is second important bast fibre crop after jute in India. With an aim to ex-ploit non-additive genetic variance present experiment was designed to identify good general combining parents and specific cross combination for fibre yield and fibre quality parameters (fibre fineness, fibre tenacity) in roselle. A total of 11 parents were crossed in complete diallel fashion which resulted 55 F1, 55 RF1 (reciprocal F1). Parents, F1s and RF1s were grown in randomized block design. Analysis of variance revealed significant differences (P< 0.01, P<0.05) among the parents and their hybrids. The parents AMV 1, AMV 5, GR 27 and AHS 160 were identified as good combiners since they recorded significant general combining ability (GCA) effects for fibre yield and quality parameters. Further, For fibre yield only three crosses (AMV 1 × AMV 4, AMV 1 × GR 27, HS 4288 × JRR 07) showed significant specific combining ability (SCA) effects from them hybrid AMV 1 × GR 27 (fibre yield=27.37g/ plant) exhibited positively significant best parent (Non bris 4, Mean fibre yield=21.16g/plant) heterosis (29.35%). Similarly, for fibre tenacity, hybrid GR 27 × JRR 07 (fibre tenacity=23.47g/tex) exhibited positively significant best parent (HS 4288; fibre tenacity=20.35g/tex) heterosis (15.30%)

CSmetaPred: a consensus method for prediction of catalytic residues

Abstract Background Knowledge of catalytic residues can play an essential role in elucidating mechanistic details of an enzyme. However, experimental identification of catalytic residues is a tedious and time-consuming task, which can be expedited by computational predictions. Despite significant development in active-site prediction methods, one of the remaining issues is ranked positions of putative catalytic residues among all ranked residues. In order to improve ranking of catalytic residues and their prediction accuracy, we have developed a meta-approach based method CSmetaPred. In this approach, residues are ranked based on the mean of normalized residue scores derived from four well-known catalytic residue predictors. The mean residue score of CSmetaPred is combined with predicted pocket information to improve prediction performance in meta-predictor, CSmetaPred_poc. Results Both meta-predictors are evaluated on two comprehensive benchmark datasets and three legacy datasets using Receiver Operating Characteristic (ROC) and Precision Recall (PR) curves. The visual and quantitative analysis of ROC and PR curves shows that meta-predictors outperform their constituent methods and CSmetaPred_poc is the best of evaluated methods. For instance, on CSAMAC dataset CSmetaPred_poc (CSmetaPred) achieves highest Mean Average Specificity (MAS), a scalar measure for ROC curve, of 0.97 (0.96). Importantly, median predicted rank of catalytic residues is the lowest (best) for CSmetaPred_poc. Considering residues ranked ≤20 classified as true positive in binary classification, CSmetaPred_poc achieves prediction accuracy of 0.94 on CSAMAC dataset. Moreover, on the same dataset CSmetaPred_poc predicts all catalytic residues within top 20 ranks for ~73% of enzymes. Furthermore, benchmarking of prediction on comparative modelled structures showed that models result in better prediction than only sequence based predictions. These analyses suggest that CSmetaPred_poc is able to rank putative catalytic residues at lower (better) ranked positions, which can facilitate and expedite their experimental characterization. Conclusions The benchmarking studies showed that employing meta-approach in combining residue-level scores derived from well-known catalytic residue predictors can improve prediction accuracy as well as provide improved ranked positions of known catalytic residues. Hence, such predictions can assist experimentalist to prioritize residues for mutational studies in their efforts to characterize catalytic residues. Both meta-predictors are available as webserver at: http://14.139.227.206/csmetapred/

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Not AvailableA trial was conducted to assess genetic parameters and diversity in physic nut (Jatropha curcas L.). Data were collected on ten biometric traits during year 2007 to 2008 and 2008 to 2009. Genotype Indira Gandhi Agricultural University (IGAU)-Raipur ranked first for seed yield (0.269, 0.492 kg/plant) in year 2007 to 2008 and 2008 to 2009, respectively. In 2007 to 2008, dry fruit yield/plant accounted highest phenotypic coefficient of variation (PCV) (40.27%) while seed yield/plant recorded highest genotypic coefficient of variation (GCV) (26.36%) in comparison to other traits. In contrast, in 2008 to 2009, seed yield/plant had highest PCV (45.88%) as well as GCV (34.27%). High estimates of heritabilities (h2) coupled with high genetic gains (GA) were registered for number of fruit clusters/plant, seed yield/plant and dry fruit yield/plant for both years which implies that direct selection would be effective for improvement of these traits. The maximum Euclidean distance of 11.21% was registered between IGAU Raipur and Lower Sowan. Non-hierarchical Euclidean analysis grouped forty six genotypes of J. curcas into five non-overlapping clusters. The maximum (7.723) inter-cluster distance was noticed between cluster NC-I and NC-IV whereas, minimum (2.747) inter-cluster distances was in between NC-I and NC-V. Based on three methods of clustering namely; hierarchical clustering, non-hierarchical clustering and metroglyph clustering, pooled clusters were formed which were found to be effective in selection of genotypes forhybridization. (12) (PDF) Variability and genetic diversity assessment in physic nut (Jatropha curcas L.). Available from: https://www.researchgate.net/publication/303178007_Variability_and_genetic_diversity_assessment_in_physic_nut_Jatropha_curcas_L [accessed Nov 27 2018].Not Availabl

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Not AvailableIn order to have wide variability in niger breeding, augmentation and evaluation of 1800 niger accessions, including indigenous and exotic collections, was conducted in rabi season at five diff erent locations of India, viz., Hyderabad (Telangana) in kharif and rabi seasons, Ranchi (Jharkhand), Akola (Maharashtra), Delhi (New Delhi) and Chinthapally, Araku (Andhra Pradesh). In all the locations, accessions IC0268292, IC0268293, IC0268294, IC0268295, IC 412911, IC411511, and IC305117 were found to be early for 50% flowering (30-40 days) and days to maturity (58-75 days) compared to the standard checks (JNS-9 and JNS-28). Seed yield in kharif season varied between 1.87 g/plant to 2.96 g/plant and in rabi season, it was 1.31 g/plant to 1.83 g/plant in the early accessions. These accessions can be further utilized in crosses with long-duration Ethiopian lines (which are late-types coupled with high yield) to select early and high yielding lines in the transgressive segregants.Not Availabl

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Not AvailableJack (Artocarpus heterophyllus) is a multi-purpose out-breeding tree species of the family Moraceae. We generated 42,928,887 high-quality expressed sequence reads, assembled them into 89,356 unigenes, and discovered 16,853 unigene-based perfect SSRs in A. heterophyllus. Thirty-eight polymorphic SSRs were used to analyze the genetic diversity and population structure of 224 germplasm accessions of A. heterophyllus constituting three populations from three agro-climatic zones, namely Eastern Plateau and Hills, Middle Gangetic Plain Region, and Eastern Himalayan Region, encompassing five Eastern and North-Eastern states of India. At the 38 SSR loci, we detected 142 alleles with a mean of 3.74 alleles per locus. The PIC values for the loci ranged from 0.25 to 0.69. The maximum genetic diversity was recorded in Eastern Plateau and Hills (I = 0.98, He = 0.52). The ANOVA analysis indicated significantly higher within-population variation (90%) than between populations (10%). The indirect estimation of gene flow (Nm) from PhiPT indicated significant gene flow among all three populations. The population structure analysis showed at least four distinct groups among the three populations with different introgression degrees. The NJ-based clustering grouped the 224 germplasm accessions into three main clusters, each with three sub-clusters. However, we did not observe distinct geographical structure among populations except some clustering among the germplasm accessions of the populations of geographically close locations. The transcriptome dataset and the SSR markers developed in the study would boost the species' molecular characterization, conservation, and specific need-based improvement.Not Availabl

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Not AvailableJack (Artocarpus heterophyllus) is a multi-purpose out-breeding tree species of the family Moraceae. We generated 42,928,887 high-quality expressed sequence reads, assembled them into 89,356 unigenes, and discovered 16,853 unigene-based perfect SSRs in A. heterophyllus. Thirty-eight polymorphic SSRs were used to analyze the genetic diversity and population structure of 224 germplasm accessions of A. heterophyllus constituting three populations from three agro-climatic zones, namely Eastern Plateau and Hills, Middle Gangetic Plain Region, and Eastern Himalayan Region, encompassing five Eastern and North-Eastern states of India. At the 38 SSR loci, we detected 142 alleles with a mean of 3.74 alleles per locus. The PIC values for the loci ranged from 0.25 to 0.69. The maximum genetic diversity was recorded in Eastern Plateau and Hills (I = 0.98, He = 0.52). The ANOVA analysis indicated significantly higher within-population variation (90%) than between populations (10%). The indirect estimation of gene flow (Nm) from PhiPT indicated significant gene flow among all three populations. The population structure analysis showed at least four distinct groups among the three populations with different introgression degrees. The NJ-based clustering grouped the 224 germplasm accessions into three main clusters, each with three sub-clusters. However, we did not observe distinct geographical structure among populations except some clustering among the germplasm accessions of the populations of geographically close locations. The transcriptome dataset and the SSR markers developed in the study would boost the species' molecular characterization, conservation, and specific need-based improvement.Not Availabl