GENOME ANNOTATION PIPELINE PROTOTYPE DEVELOPMENT

这个子项目是许多利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 我们提出了一个启动请求，为几个基因组分析项目创建支持TeraGrid的原型。如果原型成功，我们将提交项目作为TeraGrid分配供独立考虑。这些项目将共享序列分析应用程序，如NCBI blast和HMMER，以及数据集，如NCBI非冗余蛋白质数据文件。第一个项目是计算覆盖人类基因组整个外显子组（编码区）的PCR引物。PCR引物用于从基因组扩增特定的感兴趣的小序列，例如，以确定可能与高血压有关的基因的序列。通过创建整个基因组的PCR引物数据库，将有可能自动化和简化研究人员从患者群体中研究特定基因的能力。PCR引物测定是计算密集型的，因为必须确保每个引物在整个基因组中是唯一的，使得所得PCR产物足够纯。第二个项目是注释原核（细菌）基因组的管道。该管道将以组装的基因组作为输入，并执行许多分析步骤，例如识别基因边界和编码区，使用几种类型的计算证据为基因分配假定的功能，并确定是否存在完整的生化途径。第三个项目与第二个项目类似，但将注释真核生物（多细胞）基因组。真核注释比原核注释更复杂，其自动化涉及人工智能和机器学习技术的使用。第四个项目是宏基因组序列的注释管道。宏基因组数据是对复杂样品（如海水或人类消化道）的DNA进行测序的结果。它通常包含来自数百种不同细菌和病毒物种的片段序列。宏基因组分析对于检测生物体而不培养它们是有用的，并且对于理解不同环境的微生态也是有用的。通过计算识别和量化给定样品中的酶，可以更好地理解生物分子的加工过程。宏基因组学的一个应用是足够详细地了解全球碳循环，以便将长期天气预测与碳封存建模结合起来。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. We propose a startup request to create TeraGrid-enabled prototypes for several genomic analysis projects. If the prototypes are successful, we will submit the projects for independent consideration as TeraGrid allocations. These projects will share sequence analysis applications such as NCBI blast and HMMER, and also datasets such as the NCBI non-redundant protein data files. The first project is to calculate PCR primers to cover the entire exome (the coding region) of the human genome. PCR primers are used to amplify a particular small sequence of interest from a genome, for example, to determine the sequence of a gene which might be involved with hypertension. By creating a database of PCR primers for the entire genome, it will be possible to automate and simplify the ability of researchers to investigate specific genes of interest from patient populations. PCR primer determination is computationally intensive because each primer must be ensured to be unique across the genome such that the resulting PCR product is sufficiently pure. The second project is a pipeline to annotate prokaryotic (bacterial) genomes. This pipeline will take as input assembled genomes and perform a number of analytical steps such as identify gene boundaries and coding regions, assign putative functions to genes using several types of computational evidence, and identify the presence or absence of complete biochemical pathways. The third project is similar to the second but will annotate eukaryotic (multicellular) genomes. Eukaryotic annotation is more complex than prokaryotic annotation and its automation involves the use of AI and machine-learning techniques. The fourth project is an annotation pipeline for metagenomic sequences. Metagenomic data is the result of sequencing DNA from complex samples such as ocean water or the human digestive tract. It typically contains fragmentary sequences from hundreds of distinct bacterial and viral species. Metagenomic analysis is useful for detecting organisms without culturing them, and also for understanding the microecology of different environments. By computationally identifying and quantifying the enzymes in a given sample, the processing of biomolecules can be better understood. One application of metagenomics is to understand the global carbon cycle in enough detail to couple long-term weather prediction with carbon sequestration modeling.