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Workflows for Rapid Functional Annotation of Diverse Arthropod Genomes.

    Summary
    Publication Type
    Journal Article
    Abstract

    Genome sequencing of a diverse array of arthropod genomes is already underway, and these genomes will be used to study human health, agriculture, biodiversity, and ecology. These new genomes are intended to serve as community resources and provide the foundational information required to apply 'omics technologies to a more diverse set of species. However, biologists require genome annotation to use these genomes and derive a better understanding of complex biological systems. Genome annotation incorporates two related, but distinct, processes: Demarcating genes and other elements present in genome sequences (structural annotation); and associating a function with genetic elements (functional annotation). While there are well-established and freely available workflows for structural annotation of gene identification in newly assembled genomes, workflows for providing the functional annotation required to support functional genomics studies are less well understood. Genome-scale functional annotation is required for functional modeling (enrichment, networks, etc.). A first-pass genome-wide functional annotation effort can rapidly identify under-represented gene sets for focused community annotation efforts. We present an open-source, open access, and containerized pipeline for genome-scale functional annotation of insect proteomes and apply it to various arthropod species. We show that the performance of the predictions is consistent across a set of arthropod genomes with varying assembly and annotation quality.

    Citation
    Saha S, Cooksey AM, Childers AK, Poelchau MF, McCarthy FM. Workflows for Rapid Functional Annotation of Diverse Arthropod Genomes.. Insects. 2021 Aug 19; 12(8).
    Publication Date
    2021 Aug 19
    DOI
    10.3390/insects12080748
    Authors
    Saha S, Cooksey AM, Childers AK, Poelchau MF, McCarthy FM
    Cross Reference
    Database Accession
    PMID 34442314(link is external)