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1. 发明授权

US08527547B2 Sampling the space of ancestral recombination graphs 失效
标题翻译：抽样祖先重组图的空间
公开(公告)号：US08527547B2
公开(公告)日：2013-09-03
申请号：US13169824
申请日：2011-06-27
申请人： Laxmi P. Parida , Asif Javed
发明人： Laxmi P. Parida , Asif Javed
IPC分类号： G06F7/00 , G06F17/30
CPC分类号： G06F17/30958 , G06F17/30327 , G06F17/30625 , G06F17/30961 , Y10S707/956
摘要： A method is provided for constructing an ancestral recombination graph. A value K is received representing K extant units. M non-mixing segments are also received. K vertices V are generated. K lineages for each of M trees are associated with each of the K vertices. An ancestral recombination graph is constructed. To construct the ancestral recombination graph, there is repeated, until only one lineage survives for each of the M trees, a process that includes the following. A tree is randomly selected tree. A first vertex v1 and a second vertex v2 are randomly selected. Two adjoining segments in the M non-mixing segments of the first and second vertices are combined together into a single vertex. A separate vertex is generated for at least one remaining segment in each of the M non-mixing segments of the first and second vertices. The vertices V are updated to be vertices that are non-interior vertices.
摘要翻译：提供了一种构建祖先重组图的方法。接收表示K个存在单元的值K. 还收到M个非混合段。生成K个顶点V。每个M树的K谱系与K个顶点中的每一个相关联。构建祖先重组图。为了构建祖先重组图，重复一遍，直到每个M树只有一个谱系存活，这个过程包括如下。树是随机选择的树。随机选择第一顶点v1和第二顶点v2。第一和第二顶点的M个非混合段中的两个邻接段被组合成一个顶点。为第一和第二顶点的M个非混合段中的每一个中的至少一个剩余段生成单独的顶点。顶点V被更新为非内部顶点的顶点。

2. 发明申请

US20120330563A1 Assembly Error Detection 审中-公开
标题翻译：装配错误检测
公开(公告)号：US20120330563A1
公开(公告)日：2012-12-27
申请号：US13605119
申请日：2012-09-06
申请人： Laxmi P. Parida , Niina Haiminen
发明人： Laxmi P. Parida , Niina Haiminen
IPC分类号： G06F19/24
CPC分类号： G16B99/00
摘要： A method for detecting errors in genetic sequence assemblies including defining an assembly (A) of a sequence of genetic data, collecting read data into a library of reads (L), plotting histograms of sizes or reads versus a number of reads per size, normalizing a distribution (D) with a coverage C to obtain D′ that has a mean (μ) and standard deviation (σ) and reserve positions (i) not used to obtain D′, collecting subset of reads (Si⊂L) using A and D′, computing mean (μi) and standard deviation (√ci·σi) using Si, outputting results to user on a display.
摘要翻译：一种用于检测遗传序列组合中的错误的方法，包括定义遗传数据序列的组件（A），将读取的数据收集到读取库（L）中，绘制大小或读取的直方图与每个大小的读数，归一化具有覆盖度C的分布（D）以获得具有平均值（μ）和标准偏差（＆sgr）的D'和未用于获得D'的储备位置（i），收集读取子集（Si⊂L） A和D'，使用Si计算平均值（μi）和标准偏差（√ci·＆sgr; i），将结果输出到显示器上的用户。

3. 发明申请

US20100049685A1 Pattern Discovery Techniques for Determining Maximal Irredundant and Redundant Motifs 审中-公开
标题翻译：用于确定最大不饱和和冗余主题的模式发现技术
公开(公告)号：US20100049685A1
公开(公告)日：2010-02-25
申请号：US12533251
申请日：2009-07-31
申请人： Laxmi P. Parida
发明人： Laxmi P. Parida
IPC分类号： G06N5/02
CPC分类号： G01N33/6803 , G16B30/00 , G16B40/00
摘要： Basis motifs are determined from an input sequence through an iterative technique that begins by creating small solid motifs and continues to create larger motifs that include “don't care” characters and that can include flexible portions. The small solid motifs, including don't care characters and flexible portions, are concatenated to create larger motifs. During each iteration, motifs are trimmed to remove redundant motifs and other motifs that do not meet certain criteria. The process is continued until no new motifs are determined. At this point, the basis set of motifs has been determined. The basis motifs are used to construct redundant motifs. The redundant motifs are formed by determining a number of sets for selected basis motifs. From these sets, unique intersection sets are determined. The redundant motifs are determined from the unique intersection sets and the basis motifs. This process continues, by selecting additional basis motifs, until all basis motifs have been selected.
摘要翻译：基本图案通过迭代技术从输入序列确定，该技术从创建小的实体图案开始，并继续创建包括“不关心”字符并且可以包括柔性部分的较大图案。小的实心图案，包括不关心角色和柔性部分，连接起来以创建更大的图案。在每次迭代期间，修剪图案以去除不符合某些标准的冗余图案和其他图案。该过程一直持续到没有确定新的图案。在这一点上，基本的图案已经确定了。基本图案用于构建冗余图案。冗余图案通过确定所选择的基础图案的多个组来形成。从这些集合中，确定唯一的交集。冗余图案是从独特的交集和基础图案确定的。通过选择附加基本图案，继续进行所有基本图案的选择。

4. 发明申请

US20100049449A1 Pattern Discovery Techniques for Determining Maximal Irredundant and Redundant Motifs 失效
公开(公告)号：US20100049449A1
公开(公告)日：2010-02-25
申请号：US12533221
申请日：2009-07-31
申请人： Laxmi P. Parida
发明人： Laxmi P. Parida
IPC分类号： G06F19/00
CPC分类号： G06F19/24 , G01N33/6803 , G06F19/22
摘要： Basis motifs are determined from an input sequence through an iterative technique that begins by creating small solid motifs and continues to create larger motifs that include “don't care” characters and that can include flexible portions. The small solid motifs, including don't care characters and flexible portions, are concatenated to create larger motifs. During each iteration, motifs are trimmed to remove redundant motifs and other motifs that do not meet certain criteria. The process is continued until no new motifs are determined. At this point, the basis set of motifs has been determined. The basis motifs are used to construct redundant motifs. The redundant motifs are formed by determining a number of sets for selected basis motifs. From these sets, unique intersection sets are determined. The redundant motifs are determined from the unique intersection sets and the basis motifs. This process continues, by selecting additional basis motifs, until all basis motifs have been selected.

5. 发明申请

US20080250002A1 OBJECT CLASSIFICATION 有权
标题翻译：对象分类
公开(公告)号：US20080250002A1
公开(公告)日：2008-10-09
申请号：US12139894
申请日：2008-06-16
申请人： Laxmi P. Parida , Ajay K. Royyuru
发明人： Laxmi P. Parida , Ajay K. Royyuru
IPC分类号： G06F7/06 , G06F17/30
CPC分类号： G06F17/3071 , G06F17/30943 , Y10S707/99932
摘要： Classification of objects using the best boolean expression that represents the most optimal combination of the underlying features is disclosed.
摘要翻译：公开了使用表示底层特征的最佳组合的最佳布尔表达式的对象的分类。

6. 发明申请

US20130289891A1 Rank Normalization for Differential Expression Analysis of Transcriptome Sequencing Data 审中-公开
标题翻译：转录组测序数据差分表达分析的秩归一化
公开(公告)号：US20130289891A1
公开(公告)日：2013-10-31
申请号：US13547933
申请日：2012-07-12
申请人： Niina S. Haiminen , Laxmi P. Parida
发明人： Niina S. Haiminen , Laxmi P. Parida
IPC分类号： G06F19/00
CPC分类号： G16B25/00 , G16B30/00
摘要： A computer system for rank normalization for differential expression analysis of transcriptome sequencing data includes a processor; and a memory comprising a first dataset comprising transcriptome sequencing data, the first dataset comprising a plurality of genes and a respective ranking value associated with each of the plurality of genes, the system configured to perform a method including assigning a rank to each of the genes of the plurality of genes based on the ranking value to produce a first rank normalized dataset; determining a change between a first rank of a particular gene in the first rank normalized dataset, and a second rank of the particular gene in a second rank normalized dataset, the second rank normalized dataset being based on a second dataset comprising transcriptome sequencing data; and determining whether the particular gene is differentially expressed between the first and second datasets based on the determined change in rank.
摘要翻译：用于转录组测序数据的差异表达分析的秩归一化的计算机系统包括处理器; 以及存储器，其包括包含转录组测序数据的第一数据集，所述第一数据集包括多个基因和与所述多个基因中的每一个相关联的相应排名值，所述系统被配置为执行包括为每个基因分配等级的方法基于所述排序值来生成所述多个基因以产生第一秩归一化数据集; 确定第一秩归一化数据集中的特定基因的第一等级与第二秩归一化数据集中特定基因的第二等级之间的变化，第二秩归一化数据集基于包含转录组测序数据的第二数据集; 以及基于所确定的秩的改变来确定所述特定基因是否在所述第一和第二数据集之间差异表达。

7. 发明授权

US08024131B2 Pattern discovery techniques for determining maximal irredundant and redundant motifs 有权
公开(公告)号：US08024131B2
公开(公告)日：2011-09-20
申请号：US12533233
申请日：2009-07-31
申请人： Laxmi P. Parida
发明人： Laxmi P. Parida
IPC分类号： G01N33/48 , C12Q1/68 , G06G7/58
CPC分类号： G06F19/24 , G01N33/6803 , G06F19/22
摘要： Basis motifs are determined from an input sequence through an iterative technique that begins by creating small solid motifs and continues to create larger motifs that include “don't care” characters and that can include flexible portions. The small solid motifs, including don't care characters and flexible portions, are concatenated to create larger motifs. During each iteration, motifs are trimmed to remove redundant motifs and other motifs that do not meet certain criteria. The process is continued until no new motifs are determined. At this point, the basis set of motifs has been determined. The basis motifs are used to construct redundant motifs. The redundant motifs are formed by determining a number of sets for selected basis motifs. From these sets, unique intersection sets are determined. The redundant motifs are determined from the unique intersection sets and the basis motifs. This process continues, by selecting additional basis motifs, until all basis motifs have been selected.

8. 发明授权

US07739052B2 Pattern discovery techniques for determining maximal irredundant and redundant motifs 失效
标题翻译：用于确定最大不饱和和冗余图案的图案发现技术
公开(公告)号：US07739052B2
公开(公告)日：2010-06-15
申请号：US10081834
申请日：2002-02-22
申请人： Laxmi P. Parida
发明人： Laxmi P. Parida
IPC分类号： G01N33/48 , G06G7/48 , C12Q1/68
CPC分类号： G06F19/24 , G01N33/6803 , G06F19/22
摘要： Basis motifs are determined from an input sequence though an iterative technique that begins by creating small solid motifs and continues to create larger motifs that include “don't care” characters and that can include flexible portions. The small solid motifs, including don't care characters and flexible portions, are concatenated to create larger motifs. During each iteration, motifs are trimmed to remove redundant motifs and other motifs that do not meet certain criteria. The process is continued until no new motifs are determined. At this point, the basis set of motifs has been determined. The basis motifs are used to construct redundant motifs. The redundant motifs are formed by determining a number of sets for selected basis motifs. From these sets, unique intersection sets are determined. The redundant motifs are determined from the unique intersection sets and the basis motifs. This process continues, by selecting additional basis motifs.
摘要翻译：基础图案通过一种迭代技术从输入序列确定，该技术从创建小的实体图案开始，并继续创建包含“不关心”字符的较大图案，并且可以包括柔性部分。小的实心图案，包括不关心角色和柔性部分，连接起来以创建更大的图案。在每次迭代期间，修剪图案以去除不符合某些标准的冗余图案和其他图案。该过程一直持续到没有确定新的图案。在这一点上，基本的图案已经确定了。基本图案用于构建冗余图案。冗余图案通过确定所选择的基础图案的多个组来形成。从这些集合中，确定唯一的交集。冗余图案是从独特的交集和基础图案确定的。通过选择附加的基本图案，继续该过程。

9. 发明申请

US20090259626A1 METHOD AND SYSTEM FOR IDENTIFYING PARTIAL ORDER PATTERNS IN SEQUENCES OF DATA 失效
标题翻译：用于识别数据序列中部分订单模式的方法和系统
公开(公告)号：US20090259626A1
公开(公告)日：2009-10-15
申请号：US12102176
申请日：2008-04-14
申请人： Laxmi P. Parida
发明人： Laxmi P. Parida
IPC分类号： G06F7/06 , G06F17/30
CPC分类号： G06F19/22
摘要： A method and system are disclosed for identifying partial order patterns of a set of motifs in a data sequence. The method comprises the steps of obtaining the data sequence, identifying a set of motifs in the data sequence, identifying a plurality of partial orders of the motifs in the data sequence, and using the identified partial orders to identify functions of the motifs. In the preferred embodiment of the invention, the step of identifying the plurality of partial orders of the motifs includes the step of converting the identified motifs to an (n×m) incidence matrix, I, of expressions. Also, in this preferred embodiment, the step of identifying the plurality of partial orders of the motifs includes the steps of computing a partial order description of each of said expressions, and computing a redescription of each of said partial order descriptions.
摘要翻译：公开了用于识别数据序列中的一组图案的部分顺序图案的方法和系统。该方法包括以下步骤：获得数据序列，识别数据序列中的一组基序，识别数据序列中的基序的多个部分序列，以及使用所识别的部分序列来鉴定基序的功能。在本发明的优选实施方案中，识别基序的多个部分序列的步骤包括将识别的基序转换成表达式的（n×m）个入射矩阵I的步骤。此外，在该优选实施例中，识别图案的多个部分顺序的步骤包括以下步骤：计算每个所述表达式的部分顺序描述，以及计算每个所述部分顺序描述的重新描述。

10. 发明授权

US10468122B2 Exact haplotype reconstruction of F2 populations 有权
公开(公告)号：US10468122B2
公开(公告)日：2019-11-05
申请号：US13529476
申请日：2012-06-21
申请人： Niina S. Haiminen , Laxmi P. Parida , Filippo Utro
发明人： Niina S. Haiminen , Laxmi P. Parida , Filippo Utro
IPC分类号： G16B45/00 , G06F17/16 , G16B20/00
摘要： Various embodiments reconstruct haplotypes from genotype data. In one embodiment, a set of progeny genotype data comprising n progenies encoded with m genetic markers is accessed. A first set of parent haplotypes associated with a first parent of the n progenies and a second set of parent haplotypes associated with a second parent of the n progenies are identified based on at least the set of progeny genotype data. A total minimum number of observable crossovers in the n progenies is determined. An agglomerate data structure comprising a collection of sets of haplotype sequences characterizing the n progenies is constructed based on the set of progeny genotype data and the first and second sets of parent haplotypes. Each set of haplotype sequences includes a number of crossovers equal to the total minimum number of observable crossovers in the n progenies.

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