Combined statistical modeling enables accurate mining of circadian transcription
| Autor: | Iván Ballesteros, Andrea Rubio-Ponce, Salvador Aznar Benitah, Andrés Hidalgo, Juan A. Quintana, Guiomar Solanas, Fátima Sánchez-Cabo |
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| Rok vydání: | 2021 |
| Předmět: |
AcademicSubjects/SCI01140
0303 health sciences AcademicSubjects/SCI01060 Computer science In silico AcademicSubjects/SCI00030 Statistical model Standard Article Computational biology AcademicSubjects/SCI01180 03 medical and health sciences Variable (computer science) Identification (information) 0302 clinical medicine AcademicSubjects/SCI00980 Circadian rhythm Transcription (software) 030217 neurology & neurosurgery Tissue homeostasis Function (biology) 030304 developmental biology |
| Zdroj: | NAR Genomics and Bioinformatics |
| ISSN: | 2631-9268 |
| Popis: | Circadian-regulated genes are essential for tissue homeostasis and organismal function, and are therefore common targets of scrutiny. Detection of rhythmic genes using current analytical tools requires exhaustive sampling, a demand that is costly and raises ethical concerns, making it unfeasible in certain mammalian systems. Several non-parametric methods have been commonly used to analyze short-term (24 h) circadian data, such as JTK_cycle and MetaCycle. However, algorithm performance varies greatly depending on various biological and technical factors. Here, we present CircaN, an ad-hoc implementation of a non-linear mixed model for the identification of circadian genes in all types of omics data. Based on the variable but complementary results obtained through several biological and in silico datasets, we propose a combined approach of CircaN and non-parametric models to dramatically improve the number of circadian genes detected, without affecting accuracy. We also introduce an R package to make this approach available to the community. |
| Databáze: | OpenAIRE |
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