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ADAM:云规模计算的基因组格式及处理模式

ADAM: Genomics Formats and Processing Patterns for Cloud Scale Computing
作者:Matt Massi;Frank Austin Nothaft;Christopher Hart;Christos Kozanitis;Andre Schumacher;Anthony D. Joseph;and David Patterson 作者单位:Department of Electrical Engineering and Computer Science, University of California, Berkeley;The Broad Institute of MIT and Harvard;International Computer Science Institute (ICSI), University of Cali 加工时间:2014-03-18 信息来源:EECS 索取原文[23 页]
关键词:ADAM;基因组学;云计算;数据存储
摘 要:Current genomics data formats and processing pipelines are not designed to scale well to large datasets. The current Sequence/Binary Alignment/Map (SAM/BAM) formats were intended for single node processing. There have been attempts to adapt BAM to distributed computing environments, but they see limited scalability past eight nodes. Additionally, due to the lack of an explicit data schema, there are well known incompatibilities between libraries that implement SAM/BAM/Variant Call Format (VCF) data access. To address these problems, we introduce ADAM, a set of formats, APIs, and processing stage implementations for genomic data.
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