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GT-WGS: an efficient and economic tool for large-scale WGS analyses based on the AWS cloud service
BACKGROUND: Whole-genome sequencing (WGS) plays an increasingly important role in clinical practice and public health. Due to the big data size, WGS data analysis is usually compute-intensive and IO-intensive. Currently it usually takes 30 to 40 h to finish a 50× WGS analysis task, which is far from...
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| 出版年: | BMC Genomics |
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| 主要な著者: | , , , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
BioMed Central
2018
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5780748/ https://ncbi.nlm.nih.gov/pubmed/29363427 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12864-017-4334-x |
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