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1. 发明申请

WO2013040583A2 DETERMINING VARIANTS IN A GENOME OF A HETEROGENEOUS SAMPLE 审中-公开
标题翻译：确定异构样品基因组中的变量
公开(公告)号：WO2013040583A2
公开(公告)日：2013-03-21
申请号：PCT/US2012/055800
申请日：2012-09-17
申请人： COMPLETE GENOMICS, INC , BACCASH, Jonathan , HALPERN, Aaron , TIAN, Chao , PANT, Krishna , CARNEVALI, Paolo
发明人： BACCASH, Jonathan , HALPERN, Aaron , TIAN, Chao , PANT, Krishna , CARNEVALI, Paolo
IPC分类号： G06F17/18
CPC分类号： G06F19/22 , G06F19/24
摘要： After DNA fragments are sequenced and mapped to a reference, various hypotheses for the sequences in a variant region can be scored to find which sequence hypotheses are more likely. A hypothesis can include a specific variable fraction for the plurality of alleles that comprise the sequence hypothesis in the region. A likelihood of each hypothesis can be determined using a probability that accounts for the fraction of the alleles specified in the respective sequence hypothesis. Thus, other hypotheses besides standard homozygous and equal heterozygous (i.e., one chromosome with A and one with B in a cell) can be explored by explicitly including the variable fractions of the alleles as a parameter in the optimization. Also, a variant score can be determined for a variant relative to a reference. The variant score can be used to determine a variant calibrated score indicating a likelihood that the variant call is correct.
摘要翻译：对DNA片段进行测序并映射到参考文献后，可以对变体区域中的序列进行各种假设，以确定哪些序列假设更有可能。假设可以包括在该区域中构成序列假设的多个等位基因的特定可变部分。每个假设的可能性可以使用考虑各个序列假设中规定的等位基因部分的概率来确定。因此，除了标准纯合和等同杂合（即，具有A和一个在细胞中具有B的一个染色体）之外的其他假设可以通过在优化中明确地包括等位基因的可变部分作为参数来探索。此外，可以针对相对于参考的变体确定变体得分。变体得分可用于确定变体校准分数，指示变体调用正确的可能性。

2. 发明申请

WO2012051346A1 METHODS FOR ESTIMATING GENOME-WIDE COPY NUMBER VARIATIONS 审中-公开
标题翻译：估计基因型复制数变化的方法
公开(公告)号：WO2012051346A1
公开(公告)日：2012-04-19
申请号：PCT/US2011/056024
申请日：2011-10-12
申请人： COMPLETE GENOMICS , HALPERN, Aaron , PANT, Krishna
发明人： HALPERN, Aaron , PANT, Krishna
IPC分类号： G06F19/00
CPC分类号： G06F19/18 , C12Q1/6809 , C12Q1/6827 , G06F19/20 , G06F19/22 , G06F19/24 , C12Q2537/16 , C12Q2537/165
摘要： Methods for determining the copy number of a genomic region at a detection position of a target sequence in a sample are disclosed. Genomic regions of a target sequence in a sample are sequenced and measurement data for sequence coverage is obtained. Sequence coverage bias is corrected and may be normalized against a baseline sample. Hidden Markov Model (HMM) segmentation, scoring, and output are performed, and in some embodiments population-based no-calling and identification of low-confidence regions may also be performed. A total copy number value and region-specific copy number value for a plurality of regions are then estimated.
摘要翻译：公开了用于确定样品中靶序列的检测位置处的基因组区域的拷贝数的方法。对样品中靶序列的基因组区域进行测序，并获得序列覆盖度的测量数据。校正序列覆盖偏差并且可以相对于基线样本进行归一化。执行隐马尔科夫模型（HMM）分割，评分和输出，并且在一些实施例中，也可以执行基于群体的无呼叫和低置信区域的识别。然后估计多个区域的总拷贝数值和区域特定拷贝数值。

3. 发明申请

WO2013166517A1 METHODS FOR DETERMINING ABSOLUTE GENOME-WIDE COPY NUMBER VARIATIONS OF COMPLEX TUMORS 审中-公开
标题翻译：确定复杂肿瘤的绝对基因组复制数变异的方法
公开(公告)号：WO2013166517A1
公开(公告)日：2013-11-07
申请号：PCT/US2013/039777
申请日：2013-05-06
申请人： COMPLETE GENOMICS, INC.
发明人： HALPERN, Aaron , PANT, Krishna
IPC分类号： C12Q1/68
CPC分类号： G06F19/22 , C12Q1/6827 , C12Q1/6886 , C12Q2600/156 , G06F19/18 , C12Q2535/122 , C12Q2537/16 , C12Q2537/165
摘要： Methods for interpreting absolute copy number of complex tumors and for determining the copy number of a genomic region at a detection position of a target sequence in a sample are disclosed. In certain aspects, genomic regions of a target sequence in a sample are sequenced and measurement data for sequence coverage is obtained. Sequence coverage bias is corrected and may be normalized against a baseline sample. Hidden Markov Model (HMM) segmentation, scoring, and output are performed, and in some embodiments population-based no-calling and identification of low-confidence regions may also be performed. A total copy number value and region-specific copy number value for a plurality of regions are then estimated.
摘要翻译：公开了用于解释复合肿瘤的绝对拷贝数和用于确定样品中靶序列的检测位置的基因组区域的拷贝数的方法。在某些方面，对样品中靶序列的基因组区域进行测序，并获得序列覆盖率的测量数据。校正序列覆盖偏差并且可以相对于基线样本进行归一化。执行隐马尔科夫模型（HMM）分割，评分和输出，并且在一些实施例中，也可以执行基于群体的无呼叫和低置信区域的识别。然后估计多个区域的总拷贝数值和区域特定拷贝数值。

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