Mendeley | Further Information

{"title"=>"metaSNV: A tool for metagenomic strain level analysis", "type"=>"journal", "authors"=>[{"first_name"=>"Paul Igor", "last_name"=>"Costea", "scopus_author_id"=>"56094478300"}, {"first_name"=>"Robin", "last_name"=>"Munch", "scopus_author_id"=>"57195244504"}, {"first_name"=>"Luis Pedro", "last_name"=>"Coelho", "scopus_author_id"=>"24479368500"}, {"first_name"=>"Lucas", "last_name"=>"Paoli", "scopus_author_id"=>"57195239228"}, {"first_name"=>"Shinichi", "last_name"=>"Sunagawa", "scopus_author_id"=>"24468849800"}, {"first_name"=>"Peer", "last_name"=>"Bork", "scopus_author_id"=>"7103061421"}], "year"=>2017, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"85026443055", "pmid"=>"28753663", "pui"=>"617564186", "isbn"=>"1111111111", "scopus"=>"2-s2.0-85026443055", "doi"=>"10.1371/journal.pone.0182392", "issn"=>"19326203"}, "id"=>"f89aec2f-0b42-307b-96d9-8fe4d1ce9b87", "abstract"=>"We present metaSNV, a tool for single nucleotide variant (SNV) analysis in metagenomic samples, capable of comparing populations of thousands of bacterial and archaeal species. The tool uses as input nucleotide sequence alignments to reference genomes in standard SAM/BAM format, performs SNV calling for individual samples and across the whole data set, and generates various statistics for individual species including allele frequencies and nucleotide diversity per sample as well as distances and fixation indices across samples. Using published data from 676 metagenomic samples of different sites in the oral cavity, we show that the results of metaSNV are comparable to those of MIDAS, an alternative imple-mentation for metagenomic SNV analysis, while data processing is faster and has a smaller storage footprint. Moreover, we implement a set of distance measures that allow the com-parison of genomic variation across metagenomic samples and delineate sample-specific variants to enable the tracking of specific strain populations over time. The implementation of metaSNV is available at:", "link"=>"http://www.mendeley.com/research/metasnv-tool-metagenomic-strain-level-analysis", "reader_count"=>68, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>1, "Researcher"=>13, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>25, "Student > Postgraduate"=>1, "Student > Master"=>8, "Other"=>4, "Student > Bachelor"=>5, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>5, "Professor > Associate Professor"=>1, "Researcher"=>13, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>25, "Student > Postgraduate"=>1, "Student > Master"=>8, "Other"=>4, "Student > Bachelor"=>5, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Engineering"=>2, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>10, "Agricultural and Biological Sciences"=>32, "Medicine and Dentistry"=>1, "Chemistry"=>1, "Computer Science"=>9, "Immunology and Microbiology"=>5, "Earth and Planetary Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Chemistry"=>{"Chemistry"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>5}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>32}, "Computer Science"=>{"Computer Science"=>9}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>10}, "Unspecified"=>{"Unspecified"=>5}, "Environmental Science"=>{"Environmental Science"=>1}}, "group_count"=>3}

CrossRef

• http://doi.org/10.1016/j.tim.2017.11.002
• http://doi.org/10.7554/eLife.42693
• http://doi.org/10.1186/s12866-019-1689-y
• http://doi.org/10.3389/fmicb.2020.01925
• http://doi.org/10.1038/s41522-020-0121-6
• http://doi.org/10.1093/femsre/fuaa015
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• http://doi.org/10.1016/j.cell.2018.02.044
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• http://doi.org/10.3390/genes9090445
• http://doi.org/10.1038/s41467-019-08303-0
• http://doi.org/10.1038/s41467-019-08844-4
• http://doi.org/10.1093/gbe/evy057
• http://doi.org/10.1038/s41591-018-0202-8
• http://doi.org/10.1080/20002297.2019.1586421
• http://doi.org/10.15252/msb.20177589
• http://doi.org/10.3897/mbmg.3.36236
• http://doi.org/10.1074/mcp.TIR118.001233
• http://doi.org/10.7554/eLife.46497
• http://doi.org/10.1099/mgen.0.000409
• http://doi.org/10.1128/mSystems.00198-19
• http://doi.org/10.1038/s41579-020-0368-1
• http://doi.org/10.1111/1755-0998.12985
• http://doi.org/10.1002/2211-5463.12744
• http://doi.org/10.1186/s13062-018-0211-z
• http://doi.org/10.1089/fpd.2019.2636
• http://doi.org/10.1038/s41467-019-09303-w
• http://doi.org/10.1126/sciadv.aaz2299
• http://doi.org/10.1128/mSystems.00190-17
• http://doi.org/10.1093/gbe/evz184
• http://doi.org/10.1371/journal.pcbi.1007895
• http://doi.org/10.1186/s13059-019-1791-3
• http://doi.org/10.1038/s41591-018-0270-9
• http://doi.org/10.1186/s13073-020-00765-y

Scopus | Further Information

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F1000Prime