Structural variation detection model and construction method and device thereof

A technology for structural variation and detection models, applied in instrumentation, genomics, proteomics, etc., can solve the problem of low accuracy of structural variation detection

Inactive Publication Date: 2021-04-02
北京橡鑫生物科技有限公司 +2
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0007] The main purpose of the present invention is to provide a structural variation detection model, its construction method and device, to solve the problem of relatively low accuracy in the detection of structural variation in the prior art

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  • Structural variation detection model and construction method and device thereof
  • Structural variation detection model and construction method and device thereof
  • Structural variation detection model and construction method and device thereof

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Embodiment 1

[0039] In a preferred embodiment of the present application, a method for constructing a structural variation detection model is provided, figure 1 is a flowchart of a method for constructing a structural variation detection model according to an embodiment of the present invention. As shown, the method includes:

[0040] Step S101, performing gene structure variation detection on the sequencing data of multiple positive samples to obtain the variation detection result;

[0041] Step S103, screening out the characteristics of gene structural variation from the variation detection results;

[0042] Step S105, constructing a machine learning model using the features of gene structural variation to obtain a structural variation detection model.

[0043] The above-mentioned method of the present application obtains reliable genetic structural variation events by detecting positive samples of structural variation, screens out features that may be related to genetic structural var...

Embodiment 2

[0055] In a preferred embodiment of the present application, a more specific method for constructing a structural variation detection model is provided, the method comprising:

[0056] 1. The input data is the raw data of next-generation sequencing off-machine, and the data format is fastq.

[0057] 1) Preprocess the original off-machine data, including removing library adapters and low-quality data.

[0058] 2) Compare and sort the processed raw off-machine data with the reference genome, and obtain the comparison results, and the data format is bam.

[0059] 3) Identify duplicate sequences (duplication reads) on the bam file and remove duplicate sequences.

[0060] 2. Structural variation detection based on the local assembly method for the processed comparison data.

[0061] 3. Model establishment of structural variation detection results

[0062] 1) Feature selection:

[0063] a. Structural variation position;

[0064] b. Structural variation length;

[0065] c. Sequ...

Embodiment 3

[0083] In an optional embodiment, a structural variation detection model is also provided, and the structural variation detection model is constructed by any of the above methods.

[0084] In another optional embodiment, a device for detecting structural variation is also provided, which device includes the above-mentioned structural variation detection model.

[0085] The structural variation detection model or structural variation detection device obtains reliable gene structure variation events by detecting structural variation positive samples, screens out features that may be related to gene structure variation from these structural variation sample events, and further utilizes these possible related features Features, construct a structural variation detection model through machine learning, so that the constructed model can perform quantitative detection of the variation results of the test samples relatively more accurately. Moreover, using the structural variation det...

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Abstract

The invention provides a structural variation detection model and a construction method and device thereof. The construction method comprises the following steps: performing gene structure variation detection on sequencing data of a plurality of positive samples to obtain variation detection results; screening out characteristics of gene structure variation from variation detection results; and constructing a machine learning model by utilizing the characteristics of the gene structural variation to obtain a structural variation detection model. A reliable gene structure variation event is obtained by detecting a structure variation positive sample, then possible related characteristics of gene structure variation are screened out, and a structure variation detection model is constructed through machine learning by utilizing the possible related characteristics. Therefore, the constructed model can be used for quantitatively detecting the variation result of the to-be-detected sample more accurately. By utilizing the model, the abundance of gene structure variation events of a known sample can be corrected, and an important research direction and clinical guidance significance areprovided for more accurate quantitative detection of structure variation.

Description

technical field [0001] The invention relates to the field of gene sequencing data analysis, in particular to a structural variation detection model, its construction method and device. Background technique [0002] Chromosomal structural variation is a type of chromosomal variation. Its main types are translocation, deletion, duplication, etc. Under the influence of natural or exogenous environmental factors, it may cause chromosome breakage. After the breakage of different segments of different chromosomes, the same Rejoining may occur in different ways on chromosomes or between different chromosomes, resulting in structural variation of chromosomes. [0003] For the detection of chromosomal structural variation, the existing detection methods include capillary electrophoresis, which can detect the region, length, and frequency of certain structural variations, and further information on the mutated sequence can be obtained through next-generation sequencing. Although this...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16B20/20G16B40/00
CPCG16B20/20G16B40/00
Inventor 曹善柏张萌萌周涛郭璟楼峰
Owner 北京橡鑫生物科技有限公司
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