Analytical Tools and Databases for Metagenomics in the Next-Generation Sequencing Era
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김민철 | - |
dc.contributor.author | 이기현 | - |
dc.contributor.author | 윤석환 | - |
dc.contributor.author | 김봉수 | - |
dc.contributor.author | 천종식 | - |
dc.contributor.author | 이하나 | - |
dc.date.accessioned | 2021-09-06T06:46:21Z | - |
dc.date.available | 2021-09-06T06:46:21Z | - |
dc.date.created | 2021-06-17 | - |
dc.date.issued | 2013 | - |
dc.identifier.issn | 1598-866X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/104733 | - |
dc.description.abstract | Metagenomics has become one of the indispensable tools in microbial ecology for the last few decades, and a new revolution in metagenomic studies is now about to begin, with the help of recent advances of sequencing techniques. The massive data production and substantial cost reduction in next-generation sequencing have led to the rapid growth of metagenomic research both quantitatively and qualitatively. It is evident that metagenomics will be a standard tool for studying the diversity and function of microbes in the near future, as fingerprinting methods did previously. As the speed of data accumulation is accelerating, bioinformatic tools and associated databases for handling those datasets have become more urgent and necessary. To facilitate the bioinformatics analysis of metagenomic data, we review some recent tools and databases that are used widely in this field and give insights into the current challenges and future of metagenomics from a bioinformatics perspective. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | 한국유전체학회 | - |
dc.title | Analytical Tools and Databases for Metagenomics in the Next-Generation Sequencing Era | - |
dc.title.alternative | Analytical Tools and Databases for Metagenomics in the Next-Generation Sequencing Era | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 이하나 | - |
dc.identifier.doi | 10.5808/GI.2013.11.3.102 | - |
dc.identifier.bibliographicCitation | Genomics & Informatics, v.11, no.3, pp.102 - 113 | - |
dc.relation.isPartOf | Genomics & Informatics | - |
dc.citation.title | Genomics & Informatics | - |
dc.citation.volume | 11 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 102 | - |
dc.citation.endPage | 113 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART001807444 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.description.journalRegisteredClass | other | - |
dc.subject.keywordAuthor | computational biology | - |
dc.subject.keywordAuthor | high-throughput nucleotide sequencing | - |
dc.subject.keywordAuthor | metagenomics | - |
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