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公开(公告)号:US09813467B1
公开(公告)日:2017-11-07
申请号:US15452241
申请日:2017-03-07
申请人: Ryan Barrett , Taylor Sittler , Krishna Pant , Zhenghua Li , Katsuya Noguchi , Nishant Bhat
发明人: Ryan Barrett , Taylor Sittler , Krishna Pant , Zhenghua Li , Katsuya Noguchi , Nishant Bhat
CPC分类号: G06F17/30 , G06F17/30516
摘要: Techniques are disclosed for processing and aligning incomplete data. A stream of data is received from a data source including a plurality of reads. While receiving the stream of data and prior to having received all of the plurality of reads, a set of reads is extracted from the plurality of reads. Each of the set of reads is aligned to a corresponding portion of a reference data set. For each particular position of a plurality of particular positions of the reference data set, a subset of reads of the aligned set of reads is identified. A value of a client data set is generated based on the subset of reads. A variable is generated based on the client data set. Data is routed when a condition, based on the variable, is satisfied.
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公开(公告)号:US09811391B1
公开(公告)日:2017-11-07
申请号:US15449579
申请日:2017-03-03
申请人: Ryan Barrett , Taylor Sittler , Krishna Pant , Zhenghua Li
发明人: Ryan Barrett , Taylor Sittler , Krishna Pant , Zhenghua Li
CPC分类号: G06F19/18 , G06F9/5083 , G06F19/22 , G06F19/24
摘要: Embodiments in the disclosure are directed to the use of distributed computing to align reads against multiple portions of a reference dataset. Aligned portions of the reference dataset that correspond with an above-threshold alignment score can be assessed for the presence of sparse indicators that can be categorized and used to influence a determination of a state transition likelihood. Various tasks associated with the processing of reads (e.g., alignment, sparse indicator detection, and/or determination of a state transition likelihood) may be able to take advantage of parallel processing and can be distributed among the machines while considering the resource utilization of those machines. Different load-balancing mechanisms can be employed in order to achieve even resource utilization across the machines, and in some cases may involve assessing various processing characteristics that reflect a predicted resource expenditure and/or time profile for each task to be processed by a machine.
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公开(公告)号:US09773031B1
公开(公告)日:2017-09-26
申请号:US15489473
申请日:2017-04-17
申请人: Krishna Pant , Taylor Sittler , Ryan Barrett
发明人: Krishna Pant , Taylor Sittler , Ryan Barrett
IPC分类号: G06F17/30
CPC分类号: G06F17/30303 , G06F17/30312 , G06F17/30569 , G06K9/00483 , G06K9/6249
摘要: Techniques for accurately identifying duplications and deletions using depth vectors. A depth vector is generated for each of multiple clients based on a set of reads that is received and aligned to a reference data set. A transformation processing of the depth vectors is performed to produce multiple components. Each of the components is assigned an order based on the extent to which it accounts for cross-client differences in the depth vectors. Each of the components includes an intensity, multiple values, and multiple client weights. A subset of the components is identified based on the order. A sparse indicator and positional data for the sparse indicator can be determined from the components in the subset, and one or more clients can be identified as being associated with the components.
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