2010
DOI: 10.1038/ismej.2010.180
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Practical application of self-organizing maps to interrelate biodiversity and functional data in NGS-based metagenomics

Abstract: Next-generation sequencing (NGS) technologies have enabled the application of broad-scale sequencing in microbial biodiversity and metagenome studies. Biodiversity is usually targeted by classifying 16S ribosomal RNA genes, while metagenomic approaches target metabolic genes. However, both approaches remain isolated, as long as the taxonomic and functional information cannot be interrelated. Techniques like self-organizing maps (SOMs) have been applied to cluster metagenomes into taxon-specific bins in order t… Show more

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Cited by 51 publications
(43 citation statements)
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“…The rumen microbiome is rich in microbial interactions between members, such as metabolic cooperation, synergism, predation, cell-cell signalling and structural organisation such as biofilms (McAllister et al, 1994;Hobson and Stewart, 1997;Erickson et al, 2002). Genomic information, bioinformatic tools for the phylogenetic assignment of sequences (Weber et al, 2011) and the application of techniques that can provide information on direct interactions between microbes in the environment, such as fluorescence in situ hybridization and single-cell sequencing, will provide invaluable insights of the ecosystem.…”
Section: Linking Genomics and Metagenomics Data To Nutrition And Othementioning
confidence: 99%
“…The rumen microbiome is rich in microbial interactions between members, such as metabolic cooperation, synergism, predation, cell-cell signalling and structural organisation such as biofilms (McAllister et al, 1994;Hobson and Stewart, 1997;Erickson et al, 2002). Genomic information, bioinformatic tools for the phylogenetic assignment of sequences (Weber et al, 2011) and the application of techniques that can provide information on direct interactions between microbes in the environment, such as fluorescence in situ hybridization and single-cell sequencing, will provide invaluable insights of the ecosystem.…”
Section: Linking Genomics and Metagenomics Data To Nutrition And Othementioning
confidence: 99%
“…๊ทธ๋Ÿฌ๋‚˜ ์ตœ๊ทผ DNA pyrosequencing ๊ธฐ์ˆ ์˜ ๋“ฑ์žฅ์œผ๋กœ ํ‘œ์ธตํ•ด์ˆ˜๋Š” ๋ฌผ๋ก , ์‹ฌํ•ด์˜ ๊ทนํ•œํ™˜๊ฒฝ์— ์„œ์‹ํ•˜๋Š” ํ•ด์–‘๋ฏธ์ƒ๋ฌผ์˜ ๋‹ค์–‘์„ฑ ๋ถ„์„์ด ๊ฐ€๋Šฅ ํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค (Huber et al, 2007;Weber et al, 2010). et al, 2010;Imhoff et al, 2011;McGenity et al, 2012).…”
unclassified
“…๊ทธ๋Ÿฌ๋‚˜ ์ตœ๊ทผ DNA pyrosequencing ๊ธฐ์ˆ ์˜ ๋“ฑ์žฅ์œผ๋กœ ํ‘œ์ธตํ•ด์ˆ˜๋Š” ๋ฌผ๋ก , ์‹ฌํ•ด์˜ ๊ทนํ•œํ™˜๊ฒฝ์— ์„œ์‹ํ•˜๋Š” ํ•ด์–‘๋ฏธ์ƒ๋ฌผ์˜ ๋‹ค์–‘์„ฑ ๋ถ„์„์ด ๊ฐ€๋Šฅ ํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค (Huber et al, 2007;Weber et al, 2010). et al, 2010;Imhoff et al, 2011;McGenity et al, 2012). ๋Œ€์„œ์–‘๊ณผ ํƒœํ‰์–‘ ์ง€์—ญ์— ๊ฑธ์ณ ์ ๋„ ๋ถ€๊ทผ์˜ ํ‘œ์ธตํ•ด์ˆ˜์— ์„œ์‹ ํ•˜๋Š” ๋ฏธ์ƒ๋ฌผ์˜ ๋ถ„ํฌ๊ฐ€ ์กฐ์‚ฌ๋˜์—ˆ์œผ๋ฉฐ (Rusch et al, 2007), ๋ถ๊ทน, ๋‚จ๊ทน, ์•„๋ฉ”๋ฆฌ์นด, ์•„ํ”„๋ฆฌ์นด, ํ•˜์™€์ด ๋“ฑ์—์„œ ์ˆ˜์‹ฌ 5500 m์— ์ด๋ฅด๋Š” ํ•ด์–‘์‹ฌ์ธต์ˆ˜, ํ•ด์ €ํ† ์–‘, ํ•ด์ €์—ด์ˆ˜๊ตฌ ๋“ฑ์— ์„œ์‹ํ•˜๋Š” ํ•ด์–‘๋ฏธ์ƒ๋ฌผ์˜ ๋ถ„ํฌ๋„ ๋ณด๊ณ ๋˜์—ˆ๋‹ค (Zinger et al, 2011).…”
unclassified
“…๋ฐ”๋‹ค์—๋Š” ์œก์ง€๋ณด๋‹ค ํ›จ์”ฌ ๋” ๋งŽ์€ ์ข…๋ฅ˜์˜ ๋ฏธ์ƒ๋ฌผ์ด ์„œ์‹ํ•˜๊ณ  ์žˆ์ง€๋งŒ ๋ฐฐ์–‘๊ธฐ์ˆ ์˜ ํ•œ๊ณ„๋กœ ๊ทธ ๋™์•ˆ ํ•ด์–‘๋ฏธ์ƒ๋ฌผ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ํ™œ๋ฐœํ•˜๊ฒŒ ์ง„ํ–‰๋˜์ง€ ๋ชปํ•˜์˜€์œผ๋‚˜, ์ตœ๊ทผ next-generation sequencing (NGS) ๊ธฐ์ˆ ์˜ ๋“ฑ์žฅ์œผ๋กœ ๋‹ค์–‘ํ•œ ํ•ด์–‘๋ฏธ์ƒ๋ฌผ์˜ ์ƒํƒœ๋ฅผ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค (Weber et al, 2010). ์ผ๋ฐ˜์ ์œผ๋กœ ํ•ด์–‘ ํ‘œ ์ธต์ˆ˜์—๋Š” alphaproteobacteria์™€ gammaproteobacteria์— ์†ํ•œ ๊ด‘ํ•ฉ์„ฑ์„ธ๊ท ๋“ค์ด๋‚˜ bacterioplankton ๋“ฑ์ด ์ฃผ๋กœ ์„œ์‹ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ (Pontarpet al, 2012;Ritchieand Johnson, 2012), ์ˆ˜์‹ฌ 500 m ์ด ์ƒ์˜ ํ•ด์ €์— ์žˆ๋Š” ์นจ์ ํ† ์—๋Š” ์šฉ์กด์‚ฐ์†Œ์˜ ๋ถ€์กฑ์œผ๋กœ ํ™ฉํ™˜์›๊ท  (Desulfobacteraceae)์ด๋‚˜ ๋ฉ”ํƒ„๊ท (Methanobacteriale)๊ณผ ๊ฐ™์€ ํ˜๊ธฐ์„ฑ ๋ฏธ์ƒ๋ฌผ ๋“ฑ์ด ์ฃผ๋กœ ์„œ์‹ํ•˜๊ณ  ์žˆ๋‹ค (Anderson et al, 2011;Edgcomb et al, 2011).…”
unclassified
“…์‹ฌ ํ•ด์— ๋ถ„ํฌํ•˜๋Š” ํ•ด์–‘๋ฏธ์ƒ๋ฌผ ๊ตฐ์ง‘์˜ ์ƒํƒœ๋ฅผ ํŒŒ์•…ํ•˜๋ฉด ํ•ด์–‘ํ™˜๊ฒฝ๊ด€๋ฆฌ ๋‚˜ ์ €์˜จ์„ฑ ํšจ์†Œ๋‚˜ ์ƒ๋ฆฌํ™œ์„ฑ๋ฌผ์งˆ ๋“ฑ์„ ๊ฐœ๋ฐœํ•˜๊ธฐ ์œ„ํ•œ ์—ฐ๊ตฌ์— ๋„์›€ ์ด ๋  ์ˆ˜ ์žˆ๋‹ค (Heidelberg et al, 2010;Imhoff et al, 2011;McGenity et al, 2012). ๋ณธ ์—ฐ๊ตฌ๋Š” ์šธ๋ฆ‰๋„ ์—ฐ์•ˆ์˜ ํ•ด์–‘์‹ฌ์ธต์ˆ˜์— ์„œ์‹ํ•˜๋Š” ๋ฏธ์ƒ๋ฌผ๊ตฐ์ง‘์˜ ๋ถ„ํฌ๋ฅผ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•˜์—ฌ 16S rRNA gene ์„ ๊ธฐ์ค€์œผ๋กœ ๊ฐ ๊ท ์ฃผ์— ํ•ด๋‹นํ•˜๋Š” ์—ผ๊ธฐ์„œ์—ด์„ pyrosequencing ๋ฐฉ ๋ฒ•์œผ๋กœ ๋ถ„์„ํ•˜์—ฌ 1500 m ์‹ฌํ•ด์— ์กด์žฌํ•˜๋Š” ํ•ด์–‘๋ฏธ์ƒ๋ฌผ์˜ ์ƒํƒœ๋ถ„ ํฌ๋ฅผ ์กฐ์‚ฌํ•˜์˜€๋‹ค (Weber et al, 2010;Hong et al, 2011).…”
unclassified