A p-value for proteins

Scientists take the power of a p-value for granted. With a simple threshold, researchers can determine whether an observation is likely due to chance. By contrast, determining significance in proteomics has been more challenging. Searching for new proteins in the literature often produces false positives, and existing search engines cannot reliably filter results.

Tsuyoshi Tabata, Yasushi Ishihama and a team of researchers at Kyoto University created a new solution to searching through proteomic information: UniScore. They published these results in Molecular & Cellular Proteomics.

UniScore uses a target-decoy approach to filter false positives. It compares one dataset of real proteomic information with another created from shuffled protein results as a decoy. This allows researchers to compare matches to true versus fictitious data and calculate the false discovery rate.

Researchers can then set thresholds for acceptable false-positive rates, providing a clear benchmark for analyzing proteomic results. UniScore outperformed traditional search engines, demonstrating that it is a powerful tool for proteomic research moving forward.