Comparison of computational methods for the identification of cell cycle-regulated genes

TLDR

DNA microarrays have been extensively used to study the cell‑cycle transcription program, and Saccharomyces cerevisiae data have been analyzed by many bioinformatics methods to identify periodically expressed genes. The study aims to introduce a simple permutation‑based method that outperforms most existing approaches. The method employs a permutation‑based strategy to identify cell‑cycle‑regulated genes. Benchmarking revealed that many advanced methods perform worse than the original analysis, particularly those that model only the expression shape without considering regulation magnitude. Supplementary results and benchmark sets are available at http://www.cbs.dtu.dk/cellcycle.

Abstract

Abstract Motivation: DNA microarrays have been used extensively to study the cell cycle transcription programme in a number of model organisms. The Saccharomyces cerevisiae data in particular have been subjected to a wide range of bioinformatics analysis methods, aimed at identifying the correct and complete set of periodically expressed genes. Results: Here, we provide the first thorough benchmark of such methods, surprisingly revealing that most new and more mathematically advanced methods actually perform worse than the analysis published with the original microarray data sets. We show that this loss of accuracy specifically affects methods that only model the shape of the expression profile without taking into account the magnitude of regulation. We present a simple permutation-based method that performs better than most existing methods. Supplementary information: Results and benchmark sets are available at http://www.cbs.dtu.dk/cellcycle Contact: [e-mail]