J Am Med Inform Assoc - Bionimbus: a cloud for managing, analyzing and sharing large genomics datasets.

Tópicos

{ data(1714) softwar(1251) tool(1186) }
{ gene(2352) biolog(1181) express(1162) }
{ research(1218) medic(880) student(794) }
{ method(984) reconstruct(947) comput(926) }
{ ehr(2073) health(1662) electron(1139) }
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{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ chang(1828) time(1643) increas(1301) }
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{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
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{ drug(1928) target(777) effect(648) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

CKGROUND: As large genomics and phenotypic datasets are becoming more common, it is increasingly difficult for most researchers to access, manage, and analyze them. One possible approach is to provide the research community with several petabyte-scale cloud-based computing platforms containing these data, along with tools and resources to analyze it.METHODS: Bionimbus is an open source cloud-computing platform that is based primarily upon OpenStack, which manages on-demand virtual machines that provide the required computational resources, and GlusterFS, which is a high-performance clustered file system. Bionimbus also includes Tukey, which is a portal, and associated middleware that provides a single entry point and a single sign on for the various Bionimbus resources; and Yates, which automates the installation, configuration, and maintenance of the software infrastructure required.RESULTS: Bionimbus is used by a variety of projects to process genomics and phenotypic data. For example, it is used by an acute myeloid leukemia resequencing project at the University of Chicago. The project requires several computational pipelines, including pipelines for quality control, alignment, variant calling, and annotation. For each sample, the alignment step requires eight CPUs for about 12 h. BAM file sizes ranged from 5 GB to 10 GB for each sample.CONCLUSIONS: Most members of the research community have difficulty downloading large genomics datasets and obtaining sufficient storage and computer resources to manage and analyze the data. Cloud computing platforms, such as Bionimbus, with data commons that contain large genomics datasets, are one choice for broadening access to research data in genomics.

Resumo Limpo

ckground larg genom phenotyp dataset becom common increas difficult research access manag analyz one possibl approach provid research communiti sever petabytescal cloudbas comput platform contain data along tool resourc analyz itmethod bionimbus open sourc cloudcomput platform base primarili upon openstack manag ondemand virtual machin provid requir comput resourc glusterf highperform cluster file system bionimbus also includ tukey portal associ middlewar provid singl entri point singl sign various bionimbus resourc yate autom instal configur mainten softwar infrastructur requiredresult bionimbus use varieti project process genom phenotyp data exampl use acut myeloid leukemia resequenc project univers chicago project requir sever comput pipelin includ pipelin qualiti control align variant call annot sampl align step requir eight cpus h bam file size rang gb gb sampleconclus member research communiti difficulti download larg genom dataset obtain suffici storag comput resourc manag analyz data cloud comput platform bionimbus data common contain larg genom dataset one choic broaden access research data genom

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