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Alignment and comparison of protein 3D structures is an important and fundamental task in structural biology to study evolutionary, functional and structural relatedness among proteins. Since two decades, the research on protein structure alignment has been taken up on priority and numbers of research articles are being published. There are incremental advances over previous efforts, and still these methods continue to improve over the time and still this is an open problem in structural biology. A novel methodology has been developed for comparing protein 3D structure by employing conversion of pair of protein 3D structures into 2D graphs (undirected weighted graph), partitioning of 2D graphs into sub-graphs, matching sub-graphs with main graphs and finally these sub-graphs matches calculates similarity between the pair of proteins. The proposed method has been implemented in MATLAB and R Package. The performance of the developed methodology is tested with four existing best methods such as CE, jFATCAT, TM_Align and Dali on 100 proteins benchmark dataset with SCOP database. The proposed method is efficient in terms of time complexity, accuracy, grouping of proteins in relevant structural groups and provides additional information towards non-bonded interactions and sub-graphs indicates the dominance of secondary structure.

Throughout Asian countries including India, water buffalo (Bubalus bubalis) plays a crucial role in socio-economic status of the farmers by providing nutritional security. The concept of genomic selection through genetic markers has been widely used in various livestock species and this is extended to buffalo species as well. Molecular markers have been extensively used in animal breeding for improvements of desirable animal traits. Single Nucleotide Polymorphism (SNP), one of the important molecular markers is widely used in animal breeding program. In this study, SNPs related to four important traits of buffalo i.e., milk volume, age at first calving, post-partum cyclicity and feed conversion efficiency have been identified based on genome sequence data generated using ddRAD (double digest Restriction-site Associated DNA) sequencing technology. These identified SNPs have been compiled as database accessible through Web and can be used in molecular breeding program of buffalo species. This database facilitates easy search of SNPs, Polymorphic Loci and Haplotypes along with their important features like minor and major allele frequencies, observed and expected heterozygosity, observed and expected homozygosity and nucleotide diversity. This database will help to accelerate the molecular breeding program for developing trait specific breeds of buffalo to meet the need of food and nutritional security of the world including India.

Terminal heat stress (HS) has adverse effect on the quantity and quality of wheat grains, as evident from the reduction in the yield. Plant has inherited tolerance mechanism to protect itself from the environmental stresses by modulating the expression and activity of stress associated genes (SAGs)/proteins (SAPs) which protect the plant from the damage caused by HS. Heat shock transcription factor (HSF) regulates the expression of SAGs in plant under HS. Bioinformatics and phylogenetic characterization of wheat showed the presence of 56 HSFs classified into three groups—A, B, and C. The regulation of Plant HSFs basically takes place at transcriptional, post-transcriptional, translational, and post-translation levels. It also undergoes post-translational modifications such as phosphorylation, ubiquitination, and Small Ubiquitin-like MOdifier (SUMO)-mediated degradation. The expression of Heat Shock Protein (HSP) genes in response to various stimuli is regulated by HSFs. HSF1 has been reported to be the master regulator for cytoprotective HSPs expression. HSF potentially bind and activate his own promoters as well as the promoters of other members of their gene family. HSFs perceive the elevation in temperature through different signaling molecules like H2O2, kinases and ultimately increase the expression of HSPs and other SAPs inside the cell in order to protect the nascent protein from denaturation. HSFs, being placed at pivotal position, needs to be further identified, characterized and manipulated using the advanced genetic tools in order to regulate the expression of potential genes involved in defense mechanism of plants under stress. It can also be used as potential molecular marker in wheat breeding program.

Wheat is an important cereal crop, which holds the second rank globally in terms of production after maize. However, its productivity is highly sensitive to heat stress, which is one of the most serious threats due to global warming. Therefore, development of heat tolerance variety of wheat through molecular breeding approach is an urgent need of the hour for not only reducing productivity loss but also improving crop yield for feeding growing population. In this context, identification of heat-related genes is the first step for this molecular breeding. In this regard, several studies have been conducted in the past, but due to identification of large number of genes, it was found to be practically difficult to use these in molecular breeding programs. In order to address this issue, in this study, system biology approach has been followed to identify set of key genes related to heat stress in wheat which contributes significantly to regulating this entire process. Here, high-throughput RNAseq data were generated using control and treated samples of two contrasting wheat varieties, namely HD2967 (thermo-tolerant) and BT-Schomburgk (thermo-susceptible). Further, in order to identify important key genes, an advanced statistical framework called weighted gene co-expression network analysis (WGCNA) has been used. Moreover, functional annotation of these identified key genes has also been carried out, which confirms their association with the heat stress. These results will provide important lead to experimenters involved in the development of new heat-stress-tolerant wheat cultivars to mitigate effects of global warming.

Since the inception of the theory and conceptual framework of genomic selection (GS), extensive research has been done on evaluating its efficiency for utilization in crop improvement. Though, the marker-assisted selection has proven its potential for improvement of qualitative traits controlled by one to few genes with large effects. Its role in improving quantitative traits controlled by several genes with small effects is limited. In this regard, GS that utilizes genomic-estimated breeding values of individuals obtained from genome-wide markers to choose candidates for the next breeding cycle is a powerful approach to improve quantitative traits. In the last two decades, GS has been widely adopted in animal breeding programs globally because of its potential to improve selection accuracy, minimize phenotyping, reduce cycle time, and increase genetic gains. In addition, given the promising initial evaluation outcomes of GS for the improvement of yield, biotic and abiotic stress tolerance, and quality in cereal crops like wheat, maize, and rice, prospects of integrating it in breeding crops are also being explored. Improved statistical models that leverage the genomic information to increase the prediction accuracies are critical for the effectiveness of GS-enabled breeding programs. Study on genetic architecture under drought and heat stress helps in developing production markers that can significantly accelerate the development of stress-resilient crop varieties through GS. This review focuses on the transition from traditional selection methods to GS, underlying statistical methods and tools used for this purpose, current status of GS studies in crop plants, and perspectives for its successful implementation in the development of climate-resilient crops.

In recent years of animal and plant breeding research, genomic selection (GS) became a choice for selection of appropriate candidate for breeding as it significantly contributes to enhance the genetic gain. Various studies related to GS have been carried out in the recent past. These studies were mostly confined to single trait. Although GS methods based on single trait have not performed very well in cases like pleiotropy, missing data and when the trait under study has low heritability. Gradually, some studies were carried out to explore the possibility of methods for GS based on multiple traits in the view of overcoming the above-mentioned problems in the method of single-trait GS (STGS). Currently, multi-trait-based GS methods are getting importance as it exploits the information of correlated structure among response. In this study, we have compared various methods related to STGS, such as stepwise regression, ridge regression, least absolute shrinkage and selection operator (LASSO), Bayesian, best linear unbiased prediction, and support vector machine, and multi-trait-based GS methods, such as multivariate regression with covariance estimation, conditional Gaussian graphical models, mixed model, and LASSO. In almost all cases, multi-trait-based methods are found to be more accurate. Based on the results of this study, it may be concluded that multi-trait-based methods have great potential to increase genetic gain as they utilize the correlation among the response variable as extra information, which contributes to estimate breeding value more precisely. This study is a comprehensive review of the methods of GS right from single trait to multiple traits and comparisons among these two classes.

Wheat, being sensitive to terminal heat, causes drastic reduction in grain quality and yield. MAPK cascade regulates the network of defense mechanism operated inside plant system. Here, we have identified 21 novel MAPKs through gel-based proteomics and RNA-seq data analysis. Based on digital gene expression, two transcripts (transcript_2834 and transcript_8242) showing homology with MAPK were cloned and characterized from wheat (acc. nos. MK854806 and KT835664). Transcript_2834 was cloned in pET28a vector and recombinant MAPK protein of ∼40.3 kDa was isolated and characterized to have very high in-vitro kinase activity under HS. Native MAPK showed positive correlation with the expression of TFs, HSPs, genes linked with antioxidant enzyme (SOD, CAT, GPX), photosynthesis and starch biosynthesis pathways in wheat under HS. Wheat cv. HD3086 (thermotolerant) having higher expression and activity of MAPK under HS showed significant increase in accumulation of proline, H2O2, starch, and granule integrity, compared with BT-Schomburgk (thermosusceptible).

Rice blast is a global threat to food security with up to 50% yield losses. Panicle blast is a more severe form of rice blast and the response of rice plant to leaf and panicle blast is distinct in different genotypes. To understand the specific response of rice in panicle blast, transcriptome analysis of blast resistant cultivar Tetep, and susceptible cultivar HP2216 was carried out using RNA-Seq approach after 48, 72 and 96 h of infection with Magnaporthe oryzae along with mock inoculation. Transcriptome data analysis of infected panicle tissues revealed that 3553 genes differentially expressed in HP2216 and 2491 genes in Tetep, which must be the responsible factor behind the differential disease response. The defense responsive genes are involved mainly in defense pathways namely, hormonal regulation, synthesis of reactive oxygen species, secondary metabolites and cell wall modification. The common differentially expressed genes in both the cultivars were defense responsive transcription factors, NBS-LRR genes, kinases, pathogenesis related genes and peroxidases. In Tetep, cell wall strengthening pathway represented by PMR5, dirigent, tubulin, cell wall proteins, chitinases, and proteases was found to be specifically enriched. Additionally, many novel genes having DOMON, VWF, and PCaP1 domains which are specific to cell membrane were highly expressed only in Tetep post infection, suggesting their role in panicle blast resistance. Thus, our study shows that panicle blast resistance is a complex phenomenon contributed by early defense response through ROS production and detoxification, MAPK and LRR signaling, accumulation of antimicrobial compounds and secondary metabolites, and cell wall strengthening to prevent the entry and spread of the fungi. The present investigation provided valuable candidate genes that can unravel the mechanisms of panicle blast resistance and help in the rice blast breeding program.

Machine learning algorithms were employed for predicting the feed conversion efficiency (FCE), using the blood parameters and average daily gain (ADG) as predictor variables in buffalo heifers. It was observed that isotonic regression outperformed other machine learning algorithms used in study. Further, we also achieved the best performance evaluation metrics model with additive regression as the meta learner and isotonic regression as the base learner on 10-fold cross-validation and leaving-one-out cross-validation tests. Further, we created three separate partial least square regression (PLSR) models using all 14 parameters of blood and ADG as independent (explanatory) variables and FCE as the dependent variable, to understand the interactions of blood parameters, ADG with FCE each by inclusion of all FCE values (i), only higher FCE values (negative RFI) (ii), and inclusion of only lower FCE (positive RFI) values (iii). The PLSR model including only the higher FCE values was concluded the best, based on performance evaluation metrics as compared to PLSR models developed by inclusion of the lower FCE values and all types of FCE values. IGF1 and its interactions with the other blood parameters were found highly influential for higher FCE measures. The strength of the estimated interaction effects of the blood parameter in relation to FCE may facilitate understanding of intricate dynamics of blood parameters for growth.

Internet of Things (IoT) is a revolutionary technology that aims to interconnect everyday objects equipped with identity, sensors, networking, and processing capabilities and allow them to communicate with one another and with other devices and services over the Internet to accomplish some objective. This is a transition from interconnected computers to interconnected things that require support for interoperability among heterogeneous devices enabling simplification of new application development for programmers under the infrastructure of IoT. Middleware for IoT is a software layer interposed between the infrastructure and the applications that basically aims to support important requirements for these applications (Yu et al. in Cybern. Inf. Technol. 14(5):51–62, 2014). Generally, the form of communication has been human–human or human–device, but the IoT is a communication as machine–machine. So, it is a network of objects with a self-configured wireless network. These IoT frameworks are used to collect, process, and analyze data streams in real time and facilitate provision of smart solutions. IoT is observed as a natural evolution of environmental sensing systems. This aims to use different sensors to measure key parameters in forest areas in regular basis, with no need of human intervention and to send this information via wireless communication to a central platform. IoT-based environment monitoring technologies and a smart home technology are being accepted by people because they have good prospects for development. IoT products in agriculture include a number of IoT devices and sensors as well as a powerful dashboard with analytical capabilities and in-built reporting features (Yu et al. in Cybern. Inf. Technol. 14(5):51–62, 2014). A networking-based intelligent platform can monitor forest environmental factors in time by applying IoT. This technology has the advantages of low power dissipation, low data rate, and high-capacity transportation.

NBS-encoding genes play a critical role in the plant defense system. Wild relatives of crop plants are rich reservoirs of plant defense genes. Here, we performed a stringent genome-wide identification of NBS-encoding genes in three cultivated and eight wild Oryza species, representing three different genomes (AA, BB, and FF) from four continents. A total of 2688 NBS-encoding genes were identified from 11 Oryza genomes. All the three progenitor species of cultivated rice, namely O. barthii, O. rufipogon, and O. nivara, were the richest reservoir of NBS-encoding genes (214, 313, and 307 respectively). Interestingly, the two Asian cultivated species showed a contrasting pattern in the number of NBS-encoding genes. While indica subspecies maintained nearly equal number of NBS genes as its progenitor (309 and 313), the japonica subspecies had retained only two third in the course of evolution (213 and 307). Other major sources for NBS-encoding genes could be (i) O. longistaminata since it had the highest proportion of NBS-encoding genes and (ii) O. glumaepatula as it clustered distinctly away from the rest of the AA genome species. The present study thus revealed that NBS-encoding genes can be exploited from the primary gene pool for disease resistance breeding in rice.

Background Late blight is a serious disease in potato caused by Phytophthora infestans. To date only few miRNA have been discovered which are related to late blight disease of potato during host pathogen interaction. Recent studies showed that miRNA, an important gene expression regulator, plays a very important role in host-pathogen interaction by silencing genes either by destructing or blocking of translation of mRNA. Method Homology search was performed between non-redundant mature miRNA sequences from miRBase database and Solanum tuberosum EST sequences from NCBI database. Screening of the potential miRNA was done after secondary structure prediction. The target related to late blight disease of respective miRNA was functionally annotated. To identify the relationship between the predicted and mature miRNAs, multiple sequence alignment and evolutionary relationships were established. Results and conclusion 34 Candidate miRNA related to late blight disease of potato were identified which were associated to five target genes. These miRNAs were linked with Avr3a, INF1, INF2b genes which are elicitin like protein and triggers a hypersensitive response to host cell. Mapping of target sequences showed similarity with Solanum lycopersicum NRC1 gene of chr.1, which are reported as a casual protein required for Pto-mediated cell death and resistance in N. benthamiana. NRC1 are considered as a RX-CC_like domain-containing protein which shows similarity with coiledcoil domain of the potato virus X resistance protein (RX) in Solanum tuberosum. RX recognizes pathogen effector proteins and triggers a response that may be as severe as localized cell death thereby providing resistance against potato virus X.

Single Nucleotide Polymorphism (SNP) is one of the important molecular markers widely used in animal breeding program for improvement of any desirable genetic traits. Considering this, the present study was carried out to identify, annotate and analyze the SNPs related to four important traits of buffalo viz. milk volume, age at first calving, post-partum cyclicity and feed conversion efficiency. We identified 246,495, 168,202, 74,136 and 194,747 genome-wide SNPs related to mentioned traits, respectively using ddRAD sequencing technique based on 85 samples of Murrah Buffaloes. Distribution of these SNPs were highest (61.69%) and lowest (1.78%) in intron and exon regions, respectively. Under coding regions, the SNPs for the four traits were further classified as synonymous (4697) and non-synonymous (3827). Moreover, Gene Ontology (GO) terms of identified genes assigned to various traits. These characterized SNPs will enhance the knowledge of cellular mechanism for enhancing productivity of water buffalo through molecular breeding.

Nearly two decades of revolution in the area of genomics serves as the basis of present-day molecular breeding in major food crops such as rice. Here we report an open source database on two major biotic stresses of rice, named RiceMetaSysB, which provides detailed information about rice blast and bacterial blight (BB) responsive genes (RGs). Meta-analysis of microarray data from different blast- and BB-related experiments across 241 and 186 samples identified 15135 unique genes for blast and 7475 for BB. A total of 9365 and 5375 simple sequence repeats (SSRs) in blast and BB RGs were identified for marker development. Retrieval of candidate genes using different search options like genotypes, tissue, developmental stage of the host, strain, hours/days post-inoculation, physical position and SSR marker information is facilitated in the database. Search options like ‘common genes among varieties’ and ‘strains’ have been enabled to identify robust candidate genes. A 2D representation of the data can be used to compare expression profiles across genes, genotypes and strains. To demonstrate the utility of this database, we queried for blast-responsive WRKY genes (fold change ≥5) using their gene IDs. The structural variations in the 12 WRKY genes so identified and their promoter regions were explored in two rice genotypes contrasting for their reaction to blast infection. Expression analysis of these genes in panicle tissue infected with a virulent and an avirulent strain of Magnaporthe oryzae could identify WRKY7, WRKY58, WRKY62, WRKY64 and WRKY76 as potential candidate genes for resistance to panicle blast, as they showed higher expression only in the resistant genotype against the virulent strain. Thus, we demonstrated that RiceMetaSysB can play an important role in providing robust candidate genes for rice blast and BB.

Bibliographic data contains necessary information about literature to help users to recognize and retrieve that resource. These data are used quantitatively by a “Bibliometrician” for analysis and dissemination purpose but with the increasing rate of literature publication in open access journals such as Nucleic Acids Research (NAR), Springer, Oxford Journals etc., it has become difficult to retrieve structured bibliographic information in desired format. A digital bibliographic database contains necessary and structured information about published literature. Bibliographic records of different articles are scattered and resides on different web pages. This thesis presents the retrieval system for bibliographic data of NAR at a single place. For this purpose, parser agents have been developed which access the web pages of NAR and parse the scattered bibliographic data and finally store it into a local bibliographic database. Based on the bibliographic database, “three-tier architecture” has been utilized to display the bibliographic information in systematized format. Using this system, it would be possible to build the network between different authors and affiliations and also other analytical reports can be generated.

Brassica juncea is an economically important oilseed crop worldwide. It has limited genomic resources at present. We generated 47,962,057 expressed sequence reads which were assembled into 45,280 unigenes. A total of 4108 SSR loci (≥10 bp) were identified in these unigenes. Trinucleotide was the most frequent repeat unit (59.91 %) followed by di- (38.66 %), tetra - (0.71 %), hexa - (0.49 %) and pentanucleotide repeats (0.24 %). Primers were designed for 2863 SSR loci among which 460 were selected for primer synthesis. A total of 339 loci amplified successfully of which 134 (39.5 %) exhibited polymorphism among six B. juncea genotypes with PIC values ranging from 0.18 to 0.81. Further, 25 polymorphic SSRs were used for analysis of genetic variability in 25 genotypes of Brassicas and their wild relatives. Two to five alleles with PIC values 0.22–0.66 were detected at these loci. The dendrogram grouped the genotypes according to their known pedigree/systematic position.

The study was undertaken to decipher the microRNA (miRNA) related markers associated with corpus luteum (CL) tropism in buffalo. The data obtained from deep sequencing of CL tissue from different physiological stages was mined in silico for the identification of miRNA-related markers (SSR & SNP). From the present study, 5 annotated and 176 unannotated miRNA were deduced while comparing with Bos taurus genome. In addition, 4 SSRs and 9 SNPs were deduced from the miRNA sequences. These SSRs were on the genes viz. Eukaryotic translation initiation factor 1-like, myocyte enhancer factor 2A, beta casein, T cell receptor gamma cluster 1. The SNP positions on genes viz. PYGO1 (Pygopus family PHD finger 1), LOC100337244 (Multidrug resistance-associated protein 4), FTH1 (Ferritin heavy chain 1), LOC788634 (BOLA class I histocompatibility antigen), PLXND1 (Plexin D1) and UBC (Ubiquitin C) show that these genes play critical role in CL tropism during estrous cycle in buffalo.

With the advancements in sequencing technologies, there is an exponential growth in the availability of the biological databases. Biological databases consist of information and knowledge collected from scientific experiments, published literature and statistical analysis of text, numerical, image and video data. These databases are widely spread across the globe and are being maintained by many organizations. A number of tools have been developed to retrieve the information from these databases. Most of these tools are available on web but are scattered. So, finding a relevant information is a very difficult, and tedious task for the researchers. Moreover, many of these databases use disparate storage formats but are linked to each other. So, an important issue concerning present biological resources is their availability and integration at single platform. This chapter provides an insight into existing biological resources with an aim to provide consolidated information at one place for ease of use and access by researchers, academicians and students.

Changes in source code of the software products are inevitable. We need to change the source code to fix the feature improvements, new features and bugs. Feature improvements, new features and bugs are collectively termed as issues. The changes in the source code of the software negatively impact its product, but necessary for the evolution of the software. The changes in source code are quantified using entropy based measure and it is called the complexity of code changes. In this paper, we built regression models to predict the next release time of software using the complexity of code changes (entropy), feature improvements, new feature implementation and bugs fixed. The regression models have been built using Multiple Linear Regression (MLR), various kernel functions based Support Vector Regression (SVR) and k-Nearest Neighbor (k-NN) methods. The proposed models have been empirically validated using four open source sub-projects of the Apache software foundation. The proposed models exhibit a good fit. The developed models will assist release managers in release planning of the software.

Understanding the extent and pattern of codon bias and the forces affecting codon usage are the key steps towards elucidating choice of codons at the level of individual genes. In the present study, codon usage pattern among 3,703 genes of nitrogen fixing bacterium, Rhizobium etli CFN 42 was analyzed. The study aims to identify the factors responsible for codon usage bias and highly expressed genes of this bacterium. Multivariate statistical analysis revealed a single major explanatory axis accounting for codon usage variation among the genes. Correlation analysis suggested that the axis has strong significant positive correlation with GC3s i.e. GC content at the third codon position. A significant negative correlation between effective number of codons and GC3s content was observed suggesting that the codon usage was affected by gene nucleotide composition. These findings suggested mutational role as the major factor in shaping codon usage bias among the genes. Further, correspondence analysis of Relative Synonymous Codon Usage revealed cluster of highly expressed genes. Notably, 26 codons were determined as the ‘optimal codons’ that were significantly more frequent among the highly expressed genes tested by χ2 test (P < 0.01). Such results may add value to the efforts of developing bio-fertilizers based on symbiotic or non-symbiotic bacteria for improving soil fertility.

Researchers have proposed and implemented a plethora of bug prediction approaches in terms of different mathematical models for measuring the reliability growth of the software and to predict the latent bugs lying dormant in the software. During the last four decades, software reliability growth models (SRGM) have been successfully used to measure the reliability growth of closed source software. The SRGM developed were based on either calendar time or on testing effort. In late 90s, due to the advancement in communication and internet technologies, the development of open source software gets an edge and is proven to be very successful in different fields. Recently, researchers have measured the latent bugs in the open source software using an SRGM which has been developed for closed source software and concluded that the existing SRGM can well predict the latent bugs, but, still, it needs more investigation. In open source software, the source codes are frequently changes (the complexity of code changes) to meet the new feature introduction, feature enhancement and bug repair. In this paper, we have developed two complexity of code changes/entropy based bug prediction models namely (i) time vs entropy and (ii) entropy vs bugs. We have compared the proposed models with the existing time vs bugs SRGM. The empirical work has been carried out using three subsystems of Mozilla project. The statistical significance of different approaches has been tested using a non-parametric Kolmogorov–Smirnov (K–S) test. The bug prediction approaches have been compared on the basis of various performance measures namely R-Square (R2), Adjusted R-Square (adj. R2), Bias, variation and root mean square prediction errors. We found that the potential complexity of code changes based bug prediction approach i.e. time vs entropy is better over the time vs bugs and entropy vs bugs on the basis of different comparison criteria and statistical test.

Changes in software source codes are inevitable. The source codes of software are frequently changed to meet the user’s enormous requirements. These changes are occurring due to bug repairs (BR), enhancement/modification (EM) and the addition of new features (NF). The maintenance task becomes quite difficult if these changes are not properly recorded. The versions of these frequent changes are being maintained using the software configuration management repository. These continuous changes in the software source code make the code complex and negatively affect the quality of the product. In the literature, the complexity of the code changes has been quantified using entropy based measures (Hassan, in: Proceedings of the 31st international conference on software engineering, pp. 78–88, 2009). In this paper, we have proposed a model to predict the potential complexity of code changes using entropy based measures. The predicted potential complexity of code changes helps in determining the remaining code changes yet to be diffused in the software. The proposed model has been validated using seven components of web browser Mozilla. The model has been evaluated using goodness of fit criteria namely R squared, bias, mean squared error, variation, and root mean squared prediction error (RMSPE).The statistical significance of the proposed model has been tested using χ2 and Kolmogorov–Smirnov (K–S) test. The proposed model is found statistically significant based on the associated p value of the K–S test. Further, we conclude that the rate of complexity diffusion due to BR is found higher in four cases namely Bonsai, Mozbot, tables and XUL. The other components of Mozilla namely AUS, MXR and Tinderbox show increase in complexity due to EM and NF.

The application of novel and modern techniques in genetic engineering and genomics has resulted in information explosion in genomics. Three major genome databases under International Nucleotide Sequence Database collaboration NCBI, DDBJ and EMBL have been providing a convenient platform for submission of sequences which they share among themselves. Many institutes in India under Indian Council of Agricultural Research have scientists working on biotechnology and bioinformatics research. The various studies conducted by them, generate massive data related to biological information of plants, animals, insects, microbes and fisheries. These scientists are dependent on NCBI, EMBL, DDBJ and other portals for their sequence submissions, analysis and other data mining tasks. Due to various limitations imposed on these sites and the poor connectivity problem prevents them to conduct their studies on these open domain databases. The valued information generated by them needs to be shared by the scientific communities to eliminate the duplication of efforts and expedite their knowledge extended towards new findings. A secured common submission portal system with user-friendly interfaces, integrated help and error checking facilities has been developed in such a way that the database at the backend consists of a union of the items available on the above mentioned databases. Standard database management concepts have been employed for their systematic storage management. Extensive hardware resources in the form of high performance computing facility are being installed for deployment of this portal. Availability http://cabindb.iasri.res.in:8080/sequence_portal/

Background Protein structure comparison play important role in in silico functional prediction of a new protein. It is also used for understanding the evolutionary relationships among proteins. A variety of methods have been proposed in literature for comparing protein structures but they have their own limitations in terms of accuracy and complexity with respect to computational time and space. There is a need to improve the computational complexity in comparison/alignment of proteins through incorporation of important biological and structural properties in the existing techniques. Results An efficient algorithm has been developed for comparing protein structures using elastic shape analysis in which the sequence of 3D coordinates atoms of protein structures supplemented by additional auxiliary information from side-chain properties are incorporated. The protein structure is represented by a special function called square-root velocity function. Furthermore, singular value decomposition and dynamic programming have been employed for optimal rotation and optimal matching of the proteins, respectively. Also, geodesic distance has been calculated and used as the dissimilarity score between two protein structures. The performance of the developed algorithm is tested and found to be more efficient, i.e., running time reduced by 80–90 % without compromising accuracy of comparison when compared with the existing methods. Source codes for different functions have been developed in R. Also, user friendly web-based application called ProtSComp has been developed using above algorithm for comparing protein 3D structures and is accessible free. Conclusions The methodology and algorithm developed in this study is taking considerably less computational time without loss of accuracy (Table 2). The proposed algorithm is considering different criteria of representing protein structures using 3D coordinates of atoms and inclusion of residue wise molecular properties as auxiliary information.

Bacteria from genus Rhizobium have ability to fix atmospheric nitrogen in symbiosis with leguminous plants resulting in formation of root nodules. They act as an alternate source of nitrogenous fertilizers. The study of codon usage patterns of Rhizobium species is gaining increasing attention over the times. In the present study three strains of Rhizobium namely Sinorhizobium meliloti 1021, Bradyrhizobium japonicum USDA110 and Rhizobium tropici CIAT899 whose complete genome sequence are available were retrieved from NCBI for the analysis of codon usage. The overall codon usage analysis showed that codons ending with G and C are preferred more in the rhizobium genome than codon ending with A and T. ENc plot revealed that compositional constraints along with translational selection are the major cause of codon usage bias. Correspondence analysis (COA) showed that the variation in codon usage is accounted mainly by the first two axes. From the Pearson correlation analysis significant correlation was identified among the first axis of COA and Codon adaptation index (CAI) and other factors of codon usage bias. 17 optimal codons were identified that were shared among these three strains.

Codon is the basic unit for biological message transmission during synthesis of proteins in an organism. Codon Usage Bias is preferential usage among synonymous codons, in an organisms. This preferential use of a synonymous codon was found not only among species but also occurs among genes within the same genome of a species. This variation of codon usage patterns are controlled by natural processes such as mutation, drift and pressure. In this study, we have used computational as well as statistical techniques for finding codon usage bias and codon context pattern of Salinibacter ruber (extreme halophilic), Chromohalobacter salexigens (moderate halophilic) and Rhizobium etli (nonhalophilic). In addition to this, compositional variation in translated amino acid frequency, effective number of codons and optimal codons were also studied. A plot of ENc versus GC3s suggests that both mutation bias and translational selection contribute to these differences of codon bias. However, mutation bias is the driving force of the synonymous codon usage patterns in halophilic bacteria (Salinibacter ruber and Chromohalobacter salexigens) and translational selection seems to affect codon usage pattern in non-halophilic bacteria (Rhizobium etli). Correspondence analysis of Relative Synonymous Codon Usage revealed different clusters of genes varying in numbers in the bacteria under study. Moreover, codon context pattern was also seen variable in these bacteria. These results clearly indicate the variation in the codon usage pattern in these bacterial genomes.

Bug severity is the degree of impact that a defect has on the development or operation of a component or system, and can be classified into different levels based on their impact on the system. Identification of severity level can be useful for bug triager in allocating the bug to the concerned bug fixer. Various researchers have attempted text mining techniques in predicting the severity of bugs, detection of duplicate bug reports and assignment of bugs to suitable fixer for its fix. In this paper, an attempt has been made to compare the performance of different machine learning techniques namely Support vector machine (SVM), probability based Naïve Bayes (NB), Decision Tree based J48 (A Java implementation of C4.5), rule based Repeated Incremental Pruning to Produce Error Reduction (RIPPER) and Random Forests (RF) learners in predicting the severity level (1 to 5) of a reported bug by analyzing the summary or short description of the bug reports. The bug report data has been taken from NASA’s PITS (Projects and Issue Tracking System) datasets as closed source and components of Eclipse, Mozilla & GNOME datasets as open source projects. The analysis has been carried out in RapidMiner and STATISTICA data mining tools. The authors measured the performance of different machine learning techniques by considering (i) the value of accuracy and F-Measure for all severity level and (ii) number of best cases at different threshold level of accuracy and F-Measure.

X-ray K-absorption spectra of some cobalt(II) complexes of newly synthesized mannich bases of biological interest have been studied using Seifert X-ray generator and Cauchois type bent crystal X-ray spectrograph of 0.4 m radius. The observed X-ray parameters, e.g. chemical shift, shift of principal absorption maximum, edge-width, effective nuclear charge and percentage covalency have been used to explain the structure of the complexes, and these parameters have also been correlated with earlier chemical studies.

Planners, researchers, development agencies and farmers require information on animal resources for further studies and evolving realistic strategies for improvement and rearing of livestock and poultry. Data is also required for keeping watch on prices and movement of animal products, animal feed and establishment of services such as veterinary hospitals, artificial insemination (AI) centers, meat and dairy industries, etc. Further, there is a need to study animal resources in relation to other aspects of agriculture, such as soils, vegetation, agro-meteorology, socio-economic, land use, water resources for overall development of agricultural production system. Indian Agricultural Statistics Research Institute (IASRI), New Delhi has designed and implemented a Central Data Warehouse (CDW) under a National Agricultural Technology Project (NATP) Mission Mode sub-project entitled ''Integrated National Agricultural Resources Information System (INARIS)''. In this CDW, 13 different data marts related to various subjects in agriculture were designed, implemented and integrated. In this article, attempt was made to discuss concepts and problems of dimensional modeling of the animal data mart in relation to available source data on livestock resources in the country. Alternative solutions to specific problems were also discussed along with the solution implemented for modeling of this data mart. The complete process of building On-line Analytical Processing (OLAP) system for research managers including inbuilt technique of data quality and consistency checks to be implemented is being described with special reference to animal resource management. This article will provide guidelines for design and development of similar complex data marts in agricultural sector, particularly in the field of livestock management.

Studies on Sulphaguanidine Salicylaldimine Metal Chelates

A STUDY OF A*-FUNCTION (II)

EXPANSION THEOREMS OF A* FUNCTION

A*-FUNCTION (I)

STUDY OF A*-FUNCTION (III)

“When you can measure what you are speaking about and express it in numbers, you know something about it; but when you cannot measure, when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely, in your thoughts, advanced to the stage of science.” LORD WILLIAM KELVIN (1824 – 1907). During the last decade, the quantification of software engineering process has got a pace due to availability of a huge amount of software repositories. These repositories include source code, bug, communication among developers/users, changes in code, etc. Researchers are trying to find out useful information from these repositories for improving the quality of software.

X-ray absorption fine structure studies of some cobalt(II) complexes were carried out using a Seifert X-ray gener ator and Cauchois-type bent crystal x-ray spectrograph of 0.4 m radius. It was found that the graphical method originally proposed by Lytle et al. gives useful information about the bond length and also the environment of the absorbing atom. The phase parameters of the first coordination shell, α 1 and β 1 , provide valuable information about the forward- and back-scattering atoms, respectively. The total phase shift δ 1 gives an idea of the periodicity of the x-ray absorption fine structure. It was also found that the periodicity is affected by the presence of different ligands in the studied cobalt(II) complexes.

The reactions of In(O-i-Pr)3 with bidentate ligands of the type R-C(CH3):NNHCONH2 [R = C4H3O, C4H3S, C5H4N and C10H7] leads to the isolation of products of the type In(O-i-Pr)3-n(L)n [n = 1, 2 or 3 and L = anion of the ligand], depending on the stoichiometry of the reaction. The alcoholysis of indium isopropoxy complexes with t-BuOH has also been investigated. All the complexes were fully characterized by elemental analyses, molar conductance, IR, 1H and 13C NMR spectroscopy. The molecular weight determinations confirm the dimeric nature of mono- and bis-alkoxy indium(III) complexes and monomeric nature of the tris(ligand)indium(III) derivatives.