A method performed by a computing device for identifying structural variants in genomes comprising: obtaining structural variant candidates identified from whole-genome sequencing data including a pair of tumor genome data and normal tissue genome data; modifying an estimated position of a first structural variant candidate based on supplementary alignment tag information associated with the first structural variant candidate among the structural variant candidates, extracting features of each structural variant candidate including the first structural variant candidate based on data located within a predetermined range from each structural variant candidate including the first structural variant candidate in the whole-genome sequencing data; labeling each structural variant candidate including the first structural variant candidate with classification information based on a list of known structural variants, wherein each structural variant candidate including the first structural variant candidate is labeled as positive if said structural variant candidate exists in the list of known structural variants or otherwise said structural variant candidate is labeled as negative; and training a machine learning model by using a dataset, in which the extracted features of each structural variant candidate including the first structural variant candidate and the labeled classification information are stored, wherein the machine learning model receives an identification target structural variant candidate and outputs a classification of the identification target structural variant candidate, wherein the machine learning model receives features of the identification target structural variant candidate and outputs a probability value for classifying the identification target structural variant candidate as positive or negative; wherein extracting features of each structural variant candidate including the first structural variant candidate comprises: obtaining data of a first length or more in a direction, in which variant-supporting reads of said structural variant candidate are located, from a breakpoint associated with said structural variant candidate among the structural variant candidates in the whole-genome sequencing data, and wherein the first length is an average insert size of the whole-genome sequencing data. obtaining structural variant candidates identified from whole-genome sequencing data including a pair of tumor genome data and normal tissue genome data; modifying an estimated position of a first structural variant candidate based on supplementary alignment tag information associated with the first structural variant candidate among the structural variant candidates, extracting features of each structural variant candidate including the first structural variant candidate based on data located within a predetermined range from each structural variant candidate including the first structural variant candidate in the whole-genome sequencing data; labeling each structural variant candidate including the first structural variant candidate with classification information based on a list of known structural variants, wherein each structural variant candidate including the first structural variant candidate is labeled as positive if said structural variant candidate exists in the list of known structural variants or otherwise said structural variant candidate is labeled as negative; and training a machine learning model by using a dataset, in which the extracted features of each structural variant candidate including the first structural variant candidate and the labeled classification information are stored, wherein the machine learning model receives an identification target structural variant candidate and outputs a classification of the identification target structural variant candidate, wherein the machine learning model receives features of the identification target structural variant candidate and outputs a probability value for classifying the identification target structural variant candidate as positive or negative; wherein extracting features of each structural variant candidate including the first structural variant candidate comprises: obtaining data of a first length or more in a direction, in which variant-supporting reads of said structural variant candidate are located, from a breakpoint associated with said structural variant candidate among the structural variant candidates in the whole-genome sequencing data, and wherein the first length is an average insert size of the whole-genome sequencing data. obtaining data of a first length or more in a direction, in which variant-supporting reads of said structural variant candidate are located, from a breakpoint associated with said structural variant candidate among the structural variant candidates in the whole-genome sequencing data, and wherein the first length is an average insert size of the whole-genome sequencing data.