Correcting genomic deletion calls with complex boundaries from next generation sequencing data

摘要

Along with tumor growth, somatic alternations are continually accumulating, some of which leads to the formations of clonal populations. Genomic deletion is a major type of such genomic alternations. Although tens of computational methods were published, in the past decade, for detecting genomic deletions from next generation sequencing data, the existing algorithms often suffer an accuracy loss when they encounter the cases of deletion calls with complex boundaries. It is reported that a genomic deletion that occurs in different sub-clones may present nearby boundaries. Such deletion is considered as a deletion with complex boundaries. The existing approaches either ignore the complex-boundary cases by reporting the pair of boundaries with the largest numbers of supporting reads, or even provide incorrect results due to the interference data signals. To overcome this weakness, in this paper, we propose a heuristic method, SV-Del, to help the popular methods correct the detection errors, which are introduced by complex boundaries. The results of an existing method are the given candidate calls. SV-Del filters these calls and identifies the ones with complex boundaries. The proposed method first adopts a segmented extension algorithm and utilizes the longest variable splitting-read strategy to detect the possible pairs of boundaries in each candidate region. Then, it uses the longest variable splitting-reads to correct the detection errors which may introduced by clonal SNVs. To differentiate the detection errors from possible pairs of deletion boundaries, SV-Del estimates the numbers of sub-clones across sampled candidate regions, and then it uses a gradually separating algorithm to attain and refine the candidate calls. We applied SV-Del on a series of simulated datasets which are generated by different settings. The experiment results demonstrate that the detection accuracy is significantly improved comparing to the original results. SV-Del is also shown robust. The source codes and software package of SV-Del are uploaded at https://github.com/Hope523/SV-Del for academic uses only.