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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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9883684 | PMC |
http://dx.doi.org/10.1093/bioinformatics/btz664 | DOI Listing |
Bioinformatics
February 2020
Department of Medical Biochemistry and Microbiology, Science for Life Laboratories, Uppsala University, Uppsala SE-751 23, Sweden.
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.
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