A community effort to identify and correct mislabeled samples in proteogenomic studies

Sample mislabeling or misannotation hasbeen a long-standing problemin scientific research, particularly prevalent in large-scale, multi-omic studies due to the complexity of multi-omic workflows. There exists an urgent need for implementing quality controls to automatically screen for and correct sample mislabels or misannotations inmulti-omic studies. Here, we describe a crowdsourced precisionFDA NCI-CPTAC Multi-omics Enabled SampleMislabeling Correction Challenge, which provides a framework for systematic benchmarking and evaluation of mislabel identification and correction methods for integrative proteogenomic studies. The challenge received a large number of submissions from domestic and international data scientists, with highly variable performance observed across the submitted methods. Post-challenge collaboration between the top-performing teamsandthe challenge organizers has createdanopen-source software, COSMO, withdemonstrated high accuracy and robustness in mislabeling identification and correction in simulated and real multi-omic datasets.

Automated summary

Sample mislabeling or misannotation is a common problem in scientific research, especially in large-scale, multi-omic studies. A crowdsourced challenge was organized to identify and correct mislabels, resulting in the development of an open-source software named COSMO with high accuracy and robustness in multi-omic datasets.