False Discovery Rate Estimation in Proteomics

摘要: With the advancement in proteomics separation techniques and improvements mass analyzers, data generated a mass-spectrometry based experiment is rising exponentially. Such voluminous datasets necessitate automated computational tools for high-throughput analysis appropriate statistical control. The searched using one or more of several popular database search algorithms. matches assigned by these can have false positives validation necessary before making any biological interpretations. Without such procedures, inferences do not hold true may be outright misleading. There considerable overlap between positives. To control amongst set accepted matches, there need some estimate that reflect amount present processed. False discovery rate (FDR) metric global confidence assessment large-scale dataset. This chapter covers basics FDR, its application proteomics, methods to FDR.