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Parallel-META 2.0: Enhanced Metagenomic Data Analysis with Functional Annotation, High Performance Computing and Advanced Visualization
Su, Xiaoquan1,2; Pan, Weihua3; Song, Baoxing1,2; Xu, Jian1,2; Ning, Kang1,2
2014-03-03
Source PublicationPLOS ONE
Volume9Issue:3
AbstractThe 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.
SubtypeArticle
WOS HeadingsScience & Technology
DOI10.1371/journal.pone.0089323
WOS KeywordSEQUENCING DATA ; GUT MICROBIOME ; GENOMES ; RESOURCE ; PROJECT ; GENES ; HMMER ; ARB
Indexed BySCI
Language英语
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000332468900014
Citation statistics
Document Type期刊论文
Identifierhttp://ir.qibebt.ac.cn/handle/337004/6361
Collection单细胞中心组群
Affiliation1.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
Recommended Citation
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).
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