Subtyping of Dengue Viruses using Return Time Distribution based Appproach

Dengue virus (DENV) is the causative agent of Dengue Hemorrhagic Fever and Dengue Shock Syndrome, and continues to represent a major public health hazard. DENVs are antigenically classified in four serotypes and each serotype is further divided into respective genotypes. The association between DENV subtypes and the kind & severity of disease caused by them is known. Experimental and computational approaches for subtyping are routinely used for the purpose of diagnosis and treatment of DENV, in addition to the study of phylodynamics. All virus-specific molecular subtyping tools make use of sequence alignments at backend. But as the volume of molecular data increases, alignment-dependent methods become computationally intensive. Hence, the need for alternative efficient approaches for subtyping of viruses becomes apparent. Recently, the concept of Return time distribution (RTD) was proposed and validated for alignment-free clustering and molecular phylogeny. The RTD-based approach is extended here for the subtyping of DENVs. 
Subtyping methodology involves compilation of curated genomic data of known subtypes, computing RTD of these sequences at different levels of k-mers, derivation a distance matrix and clustering. The subtype of the unknown is predicted based on its clustering with known subtypes.
Dataset consisting of 1359 DENV genomes with sequence identity (>92%) were clustered using the RTD based approach at k=5. Serotype specific clades, despite geographical and temporal variation in the dataset, were observed with 100% accuracy. The method was also found to be efficient in terms of time and implementation, apart from accuracy in the subtyping of DENV

CEP: a conformational epitope prediction server

CEP server () provides a web interface to the conformational epitope prediction algorithm developed in-house. The algorithm, apart from predicting conformational epitopes, also predicts antigenic determinants and sequential epitopes. The epitopes are predicted using 3D structure data of protein antigens, which can be visualized graphically. The algorithm employs structure-based Bioinformatics approach and solvent accessibility of amino acids in an explicit manner. Accuracy of the algorithm was found to be 75% when evaluated using X-ray crystal structures of Ag–Ab complexes available in the PDB. This is the first and the only method available for the prediction of conformational epitopes, which is an attempt to map probable antibody-binding sites of protein antigens

Analysis of Next-generation Sequencing Data in Virology - Opportunities and Challenges

Viruses are the most abundant and the smallest organisms, which are relatively simple to sequence. Genome sequence data of viruses for individual species to populations outnumber that of other species. Although this offers an opportunity to study viral diversity at varying levels of taxonomic hierarchy, it also poses challenges for systematic and structured organization of data and its downstream processing. Extensive computational analyses using a number of algorithms and programs have opened exciting opportunities for virus discovery and diagnostics, apart from augmenting our understanding of the intriguing world of viruses. Unravelling evolutionary dynamics of viruses permits improved understanding of phenomena such as quasispecies diversity, role of mutations in host switching and drug resistance, which enables the tangible measurements of genotype and phenotype of viruses. Improved understanding of geno-/serotype diversity in correlation with antigenic diversity will facilitate rational design and development of efficacious vaccines against emerging and re-emerging viruses. Mathematical models developed using the genomic data could be used to predict the spread of viruses due to vector switching and the (re)emergence due to host switching and, thereby, contribute towards designing public health policies for disease management and control

Databases and Algorithms in Allergen Informatics

Allergic diseases are considered as one of the major health problems worldwide due to their increasing prevalence. Advancements in genomic, proteomic, and analytical techniques have resulted in considerable progress in the field of allergology, which has led to accumulation of huge amount of data. Allergen bioinformatics comprises allergen-related data resources and computational methods/tools, which deal with an efficient archival, management, and analysis of allergological data. Significant work has been done in the area of allergen bioinformatics that has proven pivotal for the development and progress of this field. In this chapter, we describe the current status of databases and algorithms, encompassing the field of allergen bioinformatics by examining work carried out thus far with respect to features such as allergens and allergenicity, allergen databases, algorithms/tools for allergen/allergenicity prediction, allergen epitope prediction, and allergenic cross-reactivity assessment. This chapter illustrates concepts and algorithms in allergen bioinformatics, as well as it outlines the key areas for potential development in allergology field

Curation of viral genomes: challenges, applications and the way forward

BACKGROUND: Whole genome sequence data is a step towards generating the 'parts list' of life to understand the underlying principles of Biocomplexity. Genome sequencing initiatives of human and model organisms are targeted efforts towards understanding principles of evolution with an application envisaged to improve human health. These efforts culminated in the development of dedicated resources. Whereas a large number of viral genomes have been sequenced by groups or individuals with an interest to study antigenic variation amongst strains and species. These independent efforts enabled viruses to attain the status of 'best-represented taxa' with the highest number of genomes. However, due to lack of concerted efforts, viral genomic sequences merely remained as entries in the public repositories until recently. RESULTS: VirGen is a curated resource of viral genomes and their analyses. Since its first release, it has grown both in terms of coverage of viral families and development of new modules for annotation and analysis. The current release (2.0) includes data for twenty-five families with broad host range as against eight in the first release. The taxonomic description of viruses in VirGen is in accordance with the ICTV nomenclature. A well-characterised strain is identified as a 'representative entry' for every viral species. This non-redundant dataset is used for subsequent annotation and analyses using sequenced-based Bioinformatics approaches. VirGen archives precomputed data on genome and proteome comparisons. A new data module that provides structures of viral proteins available in PDB has been incorporated recently. One of the unique features of VirGen is predicted conformational and sequential epitopes of known antigenic proteins using in-house developed algorithms, a step towards reverse vaccinology. CONCLUSION: Structured organization of genomic data facilitates use of data mining tools, which provides opportunities for knowledge discovery. One of the approaches to achieve this goal is to carry out functional annotations using comparative genomics. VirGen, a comprehensive viral genome resource that serves as an annotation and analysis pipeline has been developed for the curation of public domain viral genome data . Various steps in the curation and annotation of the genomic data and applications of the value-added derived data are substantiated with case studies

Kolaskar AS: Prediction of 3D structure of envelope glycoprotein of Sri Lanka strain of Japanese encephalitis virus. the proceedings of first APBC conference: 4–7 February 2003; Conferences in research and practice in information technology 19

Abstract This paper describes knowledge-based homology modeling studies of envelope glycoprotein (Egp) of Sri Lanka strain of Japanese encephalitis virus (JEVS). JEVS is a mosquito-borne Flavivirus, which is an important human pathogen. The Egp is a major structural antigen and is responsible for viral hemagglutination and neutralisation. The 3D structure of 399 amino acids from the extra cellular domain of Egp of JEVS has been predicted using the x-ray crystal structure of Egp of Tickborne encephalitis virus as a template and the knowledge-based homology modeling approach. Even though the homology modeling is the best method for prediction of 3D structure, prediction of structures of loop regions is still a challenge. A novel approach of molecular dynamics simulations and geometry optimisation has been used to sample the conformations of loop regions. The Egp of JEVS has an extended structure with nine β-sheets, two α-helices and three domains. The predicted structure was compared with the model of Egp of Nakayama strain of Japanese encephalitis virus (JEVN), which was developed earlier (Kolaskar & KulkarniKale, 1999). Similarities and differences between the structures of Egps of two strains of JEV are discussed. These models illustrate effect of mutations on the local and global conformation of Egp and help to explain strain specific properties. The sequential and conformational epitopes of Egp of JEV were predicted using an algorithm developed in hous

doi:10.1093/nar/gki460 CEP: a conformational epitope prediction server

CEP serve