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
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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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