Software for the analysis of large-scale genotyping and sequencing studies
Software for the analysis of large-scale genotyping and sequencing studies
批准号:
7934359
负责人:
Shaun M Purcell
金额:
$31.94万
依托单位国家:
美国
项目类别:
财政年份:
2010
资助国家:
美国
项目状态:
已结题
起止时间:
2010-09-27 至 2013-06-30
关键词:
AttentionBioinformaticsCatalogingCatalogsChromosome MappingClinical TrialsCollectionCommunitiesComplexComputer softwareCouplingDataData AnalysesData SetData Storage and RetrievalDevelopmentDiseaseDocumentationEnsureFrequenciesGenerationsGeneric DrugsGenesGeneticGenetic DeterminismGenetic VariationGenomeGenomicsGenotypeIndividualLanguageLeadMailsMaintenanceMedicalMemoryMethodsNIH Program AnnouncementsObservational StudyOntologyPathway interactionsProcessResearchResearch PersonnelResourcesRiskSequence AnalysisSoftware ToolsTechnologyTrainingVariantWorkbasedesignepigenomicsexomefile formatgenetic associationgenome sequencinggenome wide association studygenome-wide analysisinsightinterestinteroperabilitynext generationnovelparallel processingpopulation basedpublic health relevanceresponsesoftware developmenttooltool developmenttraitwiki
中文摘要
点击翻译按钮获取中文摘要
英文摘要
The objective of this project is to develop software for the analysis of data from large-
scale genotyping and sequencing genetic studies, building on the existing software
package PLINK. PLINK, a software tool to manipulate and analyze whole-genome SNP
datasets that has been actively developed over the past four years and has a wide base
of users. The specific aims are to significantly upgrade core capacities, the interface,
auxiliary resources and user-support: Core capacities: significantly adapt and upgrade
data-storage capacities to handle a) order-of-magnitude larger datasets than can fit into
memory and b) a more generic, unified representation of different types of genetic
variation data and meta-information. Interface: extend the existing interface to provide a)
a looser coupling between data storage and analysis components, via multiple interfaces
in external languages, including standard bioinformatics tools such as R and Perl, and b)
features designed to facilitate reproducible research and parallel processing. Auxiliary
resources: package standard existing resources, including the functional annotation of
variants, reference genome sequences and gene assemblies, pathways and ontologies,
in a manner that allows seamless integration between genomic resources and user data.
Support: create a high-quality collection resources to support users, via online
documentation and tutorials, including user-generated wiki pages, e-mail support and an
annual training course. Particular attention will be paid to ensure interoperability with
other major software, file-formats and resources that are generated by the broader
genetics community.
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