Dataset Information

Adenoviromics: Mining the Human Adenovirus Species D Genome.

ABSTRACT: Human adenovirus (HAdV) infections cause disease world-wide. Whole genome sequencing has now distinguished 90 distinct genotypes in 7 species (A-G). Over half of these 90 HAdVs fall within species D, with essentially all of the HAdV-D whole genome sequences generated in the last decade. Herein, we describe recent new findings made possible by mining of this expanded genome database, and propose future directions to elucidate new functional elements and new functions for previously known viral components.

SUBMITTER: Ismail AM

PROVIDER: S-EPMC6141750 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Adenoviromics: Mining the Human Adenovirus Species D Genome.

Ismail Ashrafali M AM Lee Ji Sun JS Lee Jeong Yoon JY Singh Gurdeep G Dyer David W DW Seto Donald D Chodosh James J Rajaiya Jaya J

Frontiers in microbiology 20180911

Human adenovirus (HAdV) infections cause disease world-wide. Whole genome sequencing has now distinguished 90 distinct genotypes in 7 species (A-G). Over half of these 90 HAdVs fall within species D, with essentially all of the HAdV-D whole genome sequences generated in the last decade. Herein, we describe recent new findings made possible by mining of this expanded genome database, and propose future directions to elucidate new functional elements and new functions for previously known viral co ...[more]

PMID: 30254627

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Project description:Fungal secondary metabolites are a rich source of valuable natural products, and genome sequencing has revealed a proliferation of predicted biosynthetic gene clusters in the genomes. However, it is currently an unfeasible task to characterize all biosynthetic gene clusters and to identify possible uses of the compounds. Therefore, a rational approach is needed to identify a short list of gene clusters responsible for producing valuable compounds. To this end, several bioactive clusters include a resistance gene, which is a paralog of the target gene inhibited by the compound. This mechanism can be used to identify these clusters. We have developed the FRIGG (fungal resistance gene-directed genome mining) pipeline for identifying this type of biosynthetic gene cluster based on homology patterns of the cluster genes. In this work, the FRIGG pipeline was run using 51 Aspergillus and Penicillium genomes, identifying 72 unique families of putative resistance genes. The pipeline also identified the previously characterized resistance gene inpE from the fellutamide B cluster, thereby validating the approach. We have successfully developed an approach to identify putative valuable bioactive clusters based on a specific resistance mechanism. This approach will be highly useful as an ever-increasing amount of genomic data becomes available; the art of identifying and selecting the right clusters producing novel valuable compounds will only become more crucial. IMPORTANCE Species belonging to the Aspergillus genus are known to produce a large number of secondary metabolites; some of these compounds are used as pharmaceuticals, such as penicillin, cyclosporine, and statin. With whole-genome sequencing, it became apparent that the genetic potential for secondary metabolite production is much larger than expected. As an increasing number of species are whole-genome sequenced, thousands of secondary metabolite genes are predicted, and the question of how to selectively identify novel bioactive compounds from this information arises. To address this question, we have created a pipeline to predict genes involved in the production of bioactive compounds based on a resistance gene hypothesis approach.

Dataset Information

Adenoviromics: Mining the Human Adenovirus Species D Genome.

Publications

Adenoviromics: Mining the Human Adenovirus Species D Genome.

Similar Datasets

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