A community effort to identify and correct mislabeled samples in proteogenomic studies
In a community effort to combat sample mislabeling in multi-omic studies, computational solutions received show a wide range of accuracy. The final collaborative product, COSMO, achieves high performance. Applying COSMO to published datasets demonstrates biological impact of the tool.Sample mislabeling or misannotation has been a long-standing problem in 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 in multi-omic studies. Here, we describe a crowdsourced precision