A comprehensive performance evaluation, comparison, and integration of computational methods for detecting and estimating cross-contamination of human samples in cancer next-generation sequencing analysis

Abstract

Cross-sample contamination is one of the major issues in next-generation sequencing (NGS)-based molecular assays. This type of contamination, even at very low levels, can significantly impact the results of an analysis, especially in the detection of somatic alterations in tumor samples. Several contamination identification tools have been developed and implemented as a crucial quality-control step in the routine NGS bioinformatic pipeline. However, no study has been published to comprehensively and systematically investigate, evaluate, and compare these computational methods in the cancer NGS analysis. In this study, we comprehensively investigated nine state-of-the-art computational methods for detecting cross-sample contamination. To explore their application in cancer NGS analysis, we further compared the performance of five representative tools by qualitative and quantitative analyses using in silico and simulated experimental NGS data. The results showed that Conpair achieved the best performance for identifying contamination and predicting the level of contamination in solid tumors NGS analysis. Moreover, based on Conpair, we developed a Python script, Contamination Source Predictor (ConSPr), to identify the source of contamination. We anticipate that this comprehensive survey and the proposed tool for predicting the source of contamination will assist researchers in selecting appropriate cross-contamination detection tools in cancer NGS analysis and inspire the development of computational methods for detecting sample cross-contamination and identifying its source in the future.