Method and system for analyzing gene sequencing data

Abstract

The invention discloses a method and system for analyzing gene sequencing data. According to the method, all detected variation information can be utilized; meanwhile, a visual concept is fused, unprocessed variation information is simulated into an image, variation distribution and intensity can be visually seen, an image comparison or image recognition technology is utilized to directly processthe image to search for differences. Therefore, the analysis complexity can be reduced to the great extent, the analysis time cost is lowered, the analysis time is multiplied compared with a conventional analysis method, and the analysis process is simpler and more visual.

Description

technical field [0001] The invention relates to the field of biological information analysis, in particular to a method and system for analyzing gene sequencing data. Background technique [0002] With the advancement of technology, the cost of gene sequencing has dropped rapidly, resulting in a large amount of gene sequencing data information, and the analysis requirements for these data are getting higher and higher and more refined. As a result, the application of sequencing technology to detect biomarkers in cancer has become more and more normalized and personalized. At present, the most widely used solutions focus on next generation sequencing (next generation sequencing) technology, such as whole genome sequencing, whole exome sequencing, high-depth target region sequencing, transcriptome sequencing, methylation sequencing and other technologies Real-time monitoring and targeted drug treatment of cancer patients can also be applied to large cohort data to discover ne...

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Problems solved by technology

[0004] However, the current analysis of sequencing data based on machine learning is basically the variation results (SNV / Indel / SV / CNV, etc.) obtained by analysis software or devices, combined with certain filter conditions to obtain filtered results for downstream use For modeling analysis, since the number of sites is exponentially related to the modeling complexity, the general modeling site does not require too many, otherwise it will consume a lot of computing resources and time costs; at the same time, the filter conditions set are generally All are based on the experience of the analysts, so more subjective factors will be introduced, and if the filter conditions are too strict or loose, the results will also introduce more false positives or false negatives, resulting in inaccurate results

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