A feature-based integrated scoring scheme for cell cycle-regulated genes prioritization. (14th December 2018)

• Record Type: Journal Article
• Authors: Farina, Lorenzo; Paci, Paola
• Is Part Of: Journal of theoretical biology. Volume 459(2018)
• Page Start: 130
• Page End: 141
• Publication Date: 2018-12-14
• Subjects: Budding yeast -- Gene expression -- Time-series -- Cell cycle; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2018.09.025
• Languages: English
• ISSNs: 0022-5193

Abstract

Highlights: A new prioritization algorithm, called PERLA, provides better performances than a state-of-art method in predicting cell cycle-regulated (CCR) genes from gene expression time profiles using budding yeast as a case-study. Performances are evaluated on highly reliable benchmarks and PERLA can recognize well-known cell cycle-regulated genesas top ranking ones. PERLA algorithm displays more common CCR genes among datasets and predicts a total number of about 450 CCR genes. PERLA can correctly rank genes coding for cyclins and histones and predicts new ones, as YNL058C, which is currently of unknown function. Abstract: Prioritization of cell cycle-regulated genes from expression time-profiles is still an open problem. The point at issue is the surprisingly poor overlap among ranked lists obtained from different experimental protocols. Instead of developing a general-purpose computational methodology for detecting periodic signals, we focus on the budding yeast mitotic cell cycle. The reason being that the current availability of a total of 12 datasets, produced by 6 independent groups using 4 different synchronization methods, permits a re-analysis and re-consideration of this problem in a more reliable and extensive data domain. Notably, budding yeast is a model organism for studying cancer and testing new drugs. Here we propose a novel multi-feature score (called PERLA, PERiodicity, Regulation and Lag-Autocorrelation) that integrates different features of cell cycle-regulated gene expression time-profiles. We obtained increased performances on a wide range of benchmarks and, most importantly, a substantially increased overlap of the top ranking genes among different datasets, thus proving the effectiveness of the proposed prioritization algorithm. Examples on how to use PERLA to gain new insight into the biology of the cell cycle, are provided in a final dedicated section.