Parallel-META 2.0: Enhanced Metagenomic Data Analysis with Functional Annotation, High Performance Computing and Advanced Visualization
文献类型：期刊论文
| 作者 | Su, Xiaoquan1,2 ; Pan, Weihua3; Song, Baoxing1,2; Xu, Jian1,2; Ning, Kang1,2
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| 刊名 | PLOS ONE
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| 出版日期 | 2014-03-03 |
| 卷号 | 9期号:3 |
| 英文摘要 | The metagenomic method directly sequences and analyses genome information from microbial communities. The main computational tasks for metagenomic analyses include taxonomical and functional structure analysis for all genomes in a microbial community (also referred to as a metagenomic sample). With the advancement of Next Generation Sequencing (NGS) techniques, the number of metagenomic samples and the data size for each sample are increasing rapidly. Current metagenomic analysis is both data-and computation-intensive, especially when there are many species in a metagenomic sample, and each has a large number of sequences. As such, metagenomic analyses require extensive computational power. The increasing analytical requirements further augment the challenges for computation analysis. In this work, we have proposed Parallel-META 2.0, a metagenomic analysis software package, to cope with such needs for efficient and fast analyses of taxonomical and functional structures for microbial communities. Parallel-META 2.0 is an extended and improved version of Parallel-META 1.0, which enhances the taxonomical analysis using multiple databases, improves computation efficiency by optimized parallel computing, and supports interactive visualization of results in multiple views. Furthermore, it enables functional analysis for metagenomic samples including short-reads assembly, gene prediction and functional annotation. Therefore, it could provide accurate taxonomical and functional analyses of the metagenomic samples in high-throughput manner and on large scale. |
| WOS标题词 | Science & Technology |
| 类目[WOS] | Multidisciplinary Sciences |
| 研究领域[WOS] | Science & Technology - Other Topics |
| 关键词[WOS] | SEQUENCING DATA ; GUT MICROBIOME ; GENOMES ; RESOURCE ; PROJECT ; GENES ; HMMER ; ARB |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000332468900014 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.qibebt.ac.cn/handle/337004/6361]  |
| 专题 | 青岛生物能源与过程研究所_单细胞中心 |
| 作者单位 | 1.Chinese Acad Sci, Shandong Key Lab Energy Genet, CAS Key Lab Biofuels, Qingdao, Peoples R China 2.Chinese Acad Sci, BioEnergy Genome Ctr, Qingdao Inst Bioenergy & Bioproc Technol, Computat Biol Grp,Single Cell Ctr, Qingdao, Peoples R China 3.Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230026, Anhui, Peoples R China |
| 推荐引用方式 GB/T 7714 | Su, Xiaoquan,Pan, Weihua,Song, Baoxing,et al. Parallel-META 2.0: Enhanced Metagenomic Data Analysis with Functional Annotation, High Performance Computing and Advanced Visualization[J]. PLOS ONE,2014,9(3). |
| APA | Su, Xiaoquan,Pan, Weihua,Song, Baoxing,Xu, Jian,&Ning, Kang.(2014).Parallel-META 2.0: Enhanced Metagenomic Data Analysis with Functional Annotation, High Performance Computing and Advanced Visualization.PLOS ONE,9(3). |
| MLA | Su, Xiaoquan,et al."Parallel-META 2.0: Enhanced Metagenomic Data Analysis with Functional Annotation, High Performance Computing and Advanced Visualization".PLOS ONE 9.3(2014). |
入库方式： OAI收割
来源：青岛生物能源与过程研究所
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