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Accurate molecular deconvolution of mixtures samples

A sample, fetal technique for precise molecular deconvolution of mixture samples

Pending Publication Date: 2018-04-17
ARIMA GENOMICS INC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Deconvoluting such mixture samples is fundamental to several clinical applications and is very challenging

Method used

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  • Accurate molecular deconvolution of mixtures samples
  • Accurate molecular deconvolution of mixtures samples
  • Accurate molecular deconvolution of mixtures samples

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0109] Example 1: Maximizing the Enumeration Effect by HaploSeq in the Context of NIPT

[0110] The method described here takes full advantage of HaploSeq-based cross-chromosomal parental haplotypes through long-range and maximized enumeration, thus facilitating minimal and low-depth MPcfDNA sequencing. This innovative approach supports figure 1 The theoretical limit estimate shown in A: the long parental haplotype significantly reduces the required MPcfDNA sequencing depth to determine the fetal genome ( figure 2 B(i)). Furthermore, the availability of parental haplotypes across chromosome-groups (e.g. exome) by HaploSeq means that fetal-group (e.g. exome) can be determined from group-targeted sequencing of MPcfDNA ( figure 2 B(ii))—The key is that the -group elements are distributed throughout the chromosome, thus, cross-chromosomal haplotypes of parental-groups enable maximum enumeration and minimize MPcfDNA-group sequencing. Furthermore, any targeted fetal loci can be...

Embodiment 2

[0113] Example 2: HMM with enumerated input from low-depth sequencing of MPcfDNA accurately determines fetal sequence

[0114] Using the enumerated allele fractions as observations, one aspect of the present disclosure is to use an HMM algorithm that assumes the hidden haplotype state of the fetus (2-state: M1, M2) as determined by MHet-PeqMT et al. A binomial distribution among the alleles truly emits the enumerated allele fractions. The 2-state binomial HMM model identified fetal genetic content with >98% accuracy in 2X simulated MPcfDNA sequencing with 5Mb enumeration ( Figure 4 A). This is remarkable because the method achieves comparable accuracy to other methods using >40X MPcfDNA sequencing, but at 2X MPcfDNA sequencing, thus reducing the sequencing cost by more than 20-fold. While the binomial HMM algorithm is a natural choice because it directly models the probability of emitting the hidden state of an enumerated observation via the binomial expectation (p(success)...

Embodiment 3

[0115] Example 3: Optimization of enumeration window size

[0116] An important feature of the present disclosure is the correct parental haplotype enumeration block window. The 5Mb sliding window provided a robust input for a 2-state Gaussian HMM to predict fetal genotypes and haplotypes with >99% accuracy in our simulated MPcfDNA data of 2X sequenced MHet-PeqMT alleles ( Figure 4 A). A longer enumeration window size enables a more robust cumulative input to the HMM, but also a higher probability of recombination within that window. Meanwhile, shorter window sizes may not have enough data points to generate robust HMM inputs. Therefore, to address this tradeoff, multiple window sizes were analyzed to utilize the best possible HMM input to obtain the highest accuracy for fetal sequence inference. Our analysis of the MHet-PeqMT alleles revealed that enumeration within a ~2 Mb window size upstream and downstream of each MHet-PeqMT allele yielded the best accuracy (~99.5%) fo...

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Abstract

The present disclosure relates to methods to deconvolute a mixture sample of genetic material from different origins or sources. The disclosed methods can be used in various applications, including, the non-invasive determination of a fetal genome, a fetal -ome (e.g. exome). or other targeted fetal locus from cell-free nucleic acids in maternal plasma or other body fluids; the determination of cancer-associated mutations from cell-free nucleic acids in a body fluid sample that contains a mixture of nucleic acids from normal cells and tumor cells; and quantification of donor cell contaminationusing a body fluid from a transplantation recipient to monitor and / or predict the outcome of a transplantation procedure.

Description

[0001] Cross References to Related Applications [0002] This application claims priority to U.S. Provisional Application No. 62 / 188,355, entitled "Whole Genome Diploid Sequence Sequencing of the Fetus," filed July 2, 2015, which application is hereby incorporated by reference in its entirety for for all purposes. [0003] Statement of Government Support [0004] This invention was made with Government support under Grant No. 1R43HD087113-01A1 awarded by the National Institutes of Health. The government has certain rights in this invention. technical field [0005] The present disclosure relates to methods of deconvoluting samples containing mixtures of genetic material from different origins or sources, referred to as mixture samples or impure samples. The disclosed methods can be used in a variety of applications, including but not limited to: a) Fetal genome, fetal-ome such as exome, or other targets from cell-free nucleic acid in maternal plasma or other bodily fluids ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): C12P19/34C12Q1/6888G06F19/22G16B20/10G16B20/20G16B20/40G16B40/00
CPCC12Q1/6883C12Q1/6886C12Q2600/156G16B20/00G16B40/00G16B20/20G16B20/40G16B20/10C12Q2600/172C12Q1/6806
Inventor S·塞尔瓦拉N·海兹曼C·E·莱恩
Owner ARIMA GENOMICS INC
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