Abstract: With the application and development of next generation sequencing technology,methods of calling genomic deletions based on sequencing have proliferated.However,using a single method to call deletions has limitation in application and insufficiency of precision and sensitivity.To solve these problems,an integrated approach for calling deletions was proposed based on feature mining according to combining multiple theory and machine learning algorithm.First,different callers are used for calling deletions.These results are merged as aninitial result set of deletions.Then,according to variety of detection strategies,features of the initial result set of deletions are extracted based on next generation sequencing data.Finally,to obtain the final result set of calling deletions,a machine learning model is trained to distinguish false positive deletions from initial call set.The experimental results show that compared with a single caller such as Pindel and SVseq2,the proposed approach has higher precision and sensitivity simultaneously.Compared with directly merging multiple deletion call sets,the proposed approach can significantly improve the precision with slight loss of sensitivity.

Key words: Deletion,Feature mining,Integrated detection