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Jaguc-a software package for environmental diversity analyses

机译:Jaguc-用于环境多样性分析的软件包

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The study of microbial diversity and community structures heavily relies on the analyses of sequence data, predominantly taxonomic marker genes like the small subunit of the ribosomal RNA (SSU rRNA) amplified from environmental samples. Until recently, the "gold standard" for this strategy was the cloning and Sanger sequencing of amplified target genes, usually restricted to a few hundred sequences per sample due to relatively high costs and labor intensity. The recent introduction of massive parallel tag sequencing strategies like pyrosequencing (454 sequencing) has opened a new window into microbial biodiversity research. Due to its swift nature and relatively low expense, this strategy produces millions of environmental SSU rDNA sequences granting the opportunity to gain deep insights into the true diversity and complexity of microbial communities. The bottleneck, however, is the computational processing of these massive sequence data, without which, biologists are hardly able to exploit the full information included in these sequence data. Results: The freely available standalone software package JAGUC implements a broad regime of different functions, allowing for efficient and convenient processing of a huge number of sequence tags, including importing custom-made reference data bases for basic local alignment searches, user-defined quality and search filters for analyses of specific sets of sequences, pairwise alignment-based sequence similarity calculations and clustering as well as sampling saturation and rank abundance analyses. In initial applications, JAGUC successfully analyzed hundreds of thousands of sequence data (eukaryote SSU rRNA genes) from aquatic samples and also was applied for quality assessments of different pyrosequencing platforms. Conclusions: The new program package JAGUC is a tool that bridges the gap between computational and biological sciences. It enables biologists to process large sequence data sets in order to infer biological meaning from hundreds of thousands of raw sequence data. JAGUC offers advantages over available tools which are further discussed in this manuscript.
机译:微生物多样性和群落结构的研究在很大程度上依赖于序列数据的分析,主要是从环境样品中扩增的生物分类标记基因,例如核糖体RNA(SSU rRNA)的小亚基。直到最近,该策略的“金标准”还是扩增靶基因的克隆和Sanger测序,由于相对较高的成本和劳动强度,通常限制为每个样品几百个序列。最近引入的大规模并行标签测序策略(如焦磷酸测序(454测序))为微生物多样性研究开辟了新窗口。由于其快速的性质和相对较低的费用,该策略可产生数百万条环境SSU rDNA序列,从而有机会深入了解微生物群落的真正多样性和复杂性。但是,瓶颈在于这些海量序列数据的计算处理,没有这些瓶颈,生物学家几乎无法利用这些序列数据中包含的全部信息。结果:可免费获得的独立软件包JAGUC实现了各种功能的广泛机制,从而可以高效便捷地处理大量序列标签,包括导入用于基本本地比对搜索,用户定义的质量和搜索过滤器,用于分析特定的序列集,基于成对比对的序列相似性计算和聚类,以及采样饱和度和秩丰度分析。在最初的应用中,JAGUC成功地分析了来自水生样品的数十万个序列数据(真核SSU rRNA基因),还被用于不同焦磷酸测序平台的质量评估。结论:新的程序包JAGUC是弥合计算科学与生物科学之间鸿沟的工具。它使生物学家能够处理大型序列数据集,以便从成千上万的原始序列数据中推断出生物学意义。 JAGUC提供了优于可用工具的优势,本手册将对此进行进一步讨论。

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