Literature DB >> 32672832

Genome-wide circadian rhythm detection methods: systematic evaluations and practical guidelines.

Wenwen Mei1, Zhiwen Jiang1, Yang Chen2, Li Chen3, Aziz Sancar4, Yuchao Jiang5.   

Abstract

Circadian rhythms are oscillations of behavior, physiology and metabolism in many organisms. Recent advancements in omics technology make it possible for genome-wide profiling of circadian rhythms. Here, we conducted a comprehensive analysis of seven existing algorithms commonly used for circadian rhythm detection. Using gold-standard circadian and non-circadian genes, we systematically evaluated the accuracy and reproducibility of the algorithms on empirical datasets generated from various omics platforms under different experimental designs. We also carried out extensive simulation studies to test each algorithm's robustness to key variables, including sampling patterns, replicates, waveforms, signal-to-noise ratios, uneven samplings and missing values. Furthermore, we examined the distributions of the nominal $P$-values under the null and raised issues with multiple testing corrections using traditional approaches. With our assessment, we provide method selection guidelines for circadian rhythm detection, which are applicable to different types of high-throughput omics data.
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Keywords:  benchmarking; biological rhythm; circadian rhythm detection; omics; precision and recall; reproducibility

Year:  2021        PMID: 32672832     DOI: 10.1093/bib/bbaa135

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  4 in total

Review 1.  Roles of peripheral clocks: lessons from the fly.

Authors:  Evrim Yildirim; Rachel Curtis; Dae-Sung Hwangbo
Journal:  FEBS Lett       Date:  2021-12-16       Impact factor: 4.124

2.  RhythmicDB: A Database of Predicted Multi-Frequency Rhythmic Transcripts.

Authors:  Stefano Castellana; Tommaso Biagini; Francesco Petrizzelli; Andrea Cabibbo; Gianluigi Mazzoccoli; Tommaso Mazza
Journal:  Front Genet       Date:  2022-06-14       Impact factor: 4.772

3.  Likelihood-based tests for detecting circadian rhythmicity and differential circadian patterns in transcriptomic applications.

Authors:  Haocheng Ding; Lingsong Meng; Andrew C Liu; Michelle L Gumz; Andrew J Bryant; Colleen A Mcclung; George C Tseng; Karyn A Esser; Zhiguang Huo
Journal:  Brief Bioinform       Date:  2021-11-05       Impact factor: 13.994

4.  TimeCycle: Topology Inspired MEthod for the Detection of Cycling Transcripts in Circadian Time-Series Data.

Authors:  Elan Ness-Cohn; Rosemary Braun
Journal:  Bioinformatics       Date:  2021-06-27       Impact factor: 6.937

  4 in total

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