GENOME ANNOTATION PIPELINE PROTOTYPE DEVELOPMENT

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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.