Dataset Information

Large-scale computational discovery and analysis of virus-derived microbial nanocompartments.

ABSTRACT: Encapsulins are a class of microbial protein compartments defined by the viral HK97-fold of their capsid protein, self-assembly into icosahedral shells, and dedicated cargo loading mechanism for sequestering specific enzymes. Encapsulins are often misannotated and traditional sequence-based searches yield many false positive hits in the form of phage capsids. Here, we develop an integrated search strategy to carry out a large-scale computational analysis of prokaryotic genomes with the goal of discovering an exhaustive and curated set of all HK97-fold encapsulin-like systems. We find over 6,000 encapsulin-like systems in 31 bacterial and four archaeal phyla, including two novel encapsulin families. We formulate hypotheses about their potential biological functions and biomedical relevance, which range from natural product biosynthesis and stress resistance to carbon metabolism and anaerobic hydrogen production. An evolutionary analysis of encapsulins and related HK97-type virus families shows that they share a common ancestor, and we conclude that encapsulins likely evolved from HK97-type bacteriophages.

SUBMITTER: Andreas MP

PROVIDER: S-EPMC8346489 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Large-scale computational discovery and analysis of virus-derived microbial nanocompartments.

Andreas Michael P MP Giessen Tobias W TW

Nature communications 20210806 1

Encapsulins are a class of microbial protein compartments defined by the viral HK97-fold of their capsid protein, self-assembly into icosahedral shells, and dedicated cargo loading mechanism for sequestering specific enzymes. Encapsulins are often misannotated and traditional sequence-based searches yield many false positive hits in the form of phage capsids. Here, we develop an integrated search strategy to carry out a large-scale computational analysis of prokaryotic genomes with the goal of d ...[more]

PMID: 34362927

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Project description:Quantitative trait loci (QTL) denote regions of DNA whose variation is associated with variations in quantitative traits. QTL discovery is a powerful approach to understand how changes in molecular and clinical phenotypes may be related to DNA sequence changes. However, QTL discovery analysis encompasses multiple analytical steps and the processing of multiple input files, which can be laborious, error prone, and hard to reproduce if performed manually. In order to facilitate and automate large-scale QTL analysis, we developed the yQTL Pipeline, where the 'y' indicates the dependent quantitative variable being modeled. Prior to genome-wide association test, the pipeline supports the calculation or the direct input of pre-defined genome-wide principal components and genetic relationship matrix when applicable. User-specified covariates can also be provided. Depending on whether familial relatedness exists among the subjects, genome-wide association tests will be performed using either a linear mixed-effect model or a linear model. Using the workflow management tool Nextflow, the pipeline parallelizes the analysis steps to optimize run-time and ensure results reproducibility. In addition, a user-friendly R Shiny App is developed to facilitate result visualization. Upon uploading the result file, it can generate Manhattan plots of user-selected phenotype traits and trait-QTL connection networks based on user-specified p-value thresholds. We applied the yQTL Pipeline to analyze metabolomics profiles of blood serum from the New England Centenarians Study (NECS) participants. A total of 9.1M SNPs and 1,052 metabolites across 194 participants were analyzed. Using a p-value cutoff 5e-8, we found 14,983 mQTLs cumulatively associated with 312 metabolites. The built-in parallelization of our pipeline reduced the run time from ~90 min to ~26 min. Visualization using the R Shiny App revealed multiple mQTLs shared across multiple metabolites. The yQTL Pipeline is available with documentation on GitHub at https://github.com/montilab/yQTL-Pipeline.

Dataset Information

Large-scale computational discovery and analysis of virus-derived microbial nanocompartments.

Publications

Large-scale computational discovery and analysis of virus-derived microbial nanocompartments.

Similar Datasets

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