Eric Hugoson1,2, Wai Tin Lam2, Lionel Guy1. 1. Department of Medical Biochemistry and Microbiology, Science for Life Laboratories, Uppsala University, Uppsala SE-751 23, Sweden. 2. Department of Microbial Population Biology, Max Planck Institute for Evolutionary Biology, Plön D-24306, Germany.
Abstract
SUMMARY: Metagenomics and single-cell genomics have revolutionized the study of microorganisms, increasing our knowledge of microbial genomic diversity by orders of magnitude. A major issue pertaining to metagenome-assembled genomes (MAGs) and single-cell amplified genomes (SAGs) is to estimate their completeness and redundancy. Most approaches rely on counting conserved gene markers. In miComplete, we introduce a weighting strategy, where we normalize the presence/absence of markers by their median distance to the next marker in a set of complete reference genomes. This approach alleviates biases introduced by the presence/absence of shorter DNA pieces containing many markers, e.g. ribosomal protein operons. AVAILABILITY AND IMPLEMENTATION: miComplete is written in Python 3 and released under GPLv3. Source code and documentation are available at https://bitbucket.org/evolegiolab/micomplete. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
SUMMARY: Metagenomics and single-cell genomics have revolutionized the study of microorganisms, increasing our knowledge of microbial genomic diversity by orders of magnitude. A major issue pertaining to metagenome-assembled genomes (MAGs) and single-cell amplified genomes (SAGs) is to estimate their completeness and redundancy. Most approaches rely on counting conserved gene markers. In miComplete, we introduce a weighting strategy, where we normalize the presence/absence of markers by their median distance to the next marker in a set of complete reference genomes. This approach alleviates biases introduced by the presence/absence of shorter DNA pieces containing many markers, e.g. ribosomal protein operons. AVAILABILITY AND IMPLEMENTATION: miComplete is written in Python 3 and released under GPLv3. Source code and documentation are available at https://bitbucket.org/evolegiolab/micomplete. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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