98 research outputs found

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

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    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

    Zebrafish Whole-Adult-Organism Chemogenomics for Large-Scale Predictive and Discovery Chemical Biology

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    The ability to perform large-scale, expression-based chemogenomics on whole adult organisms, as in invertebrate models (worm and fly), is highly desirable for a vertebrate model but its feasibility and potential has not been demonstrated. We performed expression-based chemogenomics on the whole adult organism of a vertebrate model, the zebrafish, and demonstrated its potential for large-scale predictive and discovery chemical biology. Focusing on two classes of compounds with wide implications to human health, polycyclic (halogenated) aromatic hydrocarbons [P(H)AHs] and estrogenic compounds (ECs), we generated robust prediction models that can discriminate compounds of the same class from those of different classes in two large independent experiments. The robust expression signatures led to the identification of biomarkers for potent aryl hydrocarbon receptor (AHR) and estrogen receptor (ER) agonists, respectively, and were validated in multiple targeted tissues. Knowledge-based data mining of human homologs of zebrafish genes revealed highly conserved chemical-induced biological responses/effects, health risks, and novel biological insights associated with AHR and ER that could be inferred to humans. Thus, our study presents an effective, high-throughput strategy of capturing molecular snapshots of chemical-induced biological states of a whole adult vertebrate that provides information on biomarkers of effects, deregulated signaling pathways, and possible affected biological functions, perturbed physiological systems, and increased health risks. These findings place zebrafish in a strategic position to bridge the wide gap between cell-based and rodent models in chemogenomics research and applications, especially in preclinical drug discovery and toxicology

    The one dimensional Kondo lattice model at partial band filling

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    The Kondo lattice model introduced in 1977 describes a lattice of localized magnetic moments interacting with a sea of conduction electrons. It is one of the most important canonical models in the study of a class of rare earth compounds, called heavy fermion systems, and as such has been studied intensively by a wide variety of techniques for more than a quarter of a century. This review focuses on the one dimensional case at partial band filling, in which the number of conduction electrons is less than the number of localized moments. The theoretical understanding, based on the bosonized solution, of the conventional Kondo lattice model is presented in great detail. This review divides naturally into two parts, the first relating to the description of the formalism, and the second to its application. After an all-inclusive description of the bosonization technique, the bosonized form of the Kondo lattice hamiltonian is constructed in detail. Next the double-exchange ordering, Kondo singlet formation, the RKKY interaction and spin polaron formation are described comprehensively. An in-depth analysis of the phase diagram follows, with special emphasis on the destruction of the ferromagnetic phase by spin-flip disorder scattering, and of recent numerical results. The results are shown to hold for both antiferromagnetic and ferromagnetic Kondo lattice. The general exposition is pedagogic in tone.Comment: Review, 258 pages, 19 figure

    Inferring single-trial neural population dynamics using sequential auto-encoders

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    Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from data averaged across many trials, but deeper understanding requires studying phenomena detected in single trials, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce latent factor analysis via dynamical systems, a deep learning method to infer latent dynamics from single-trial neural spiking data. When applied to a variety of macaque and human motor cortical datasets, latent factor analysis via dynamical systems accurately predicts observed behavioral variables, extracts precise firing rate estimates of neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping recording sessions spanning months to improve inference of underlying dynamics
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