Evidence map›Paper›PMID 32672832›Full record

ArticleBriefings in bioinformatics2021

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

Wenwen Mei, Zhiwen Jiang, Yang Chen, Li Chen, Aziz Sancar, Yuchao Jiang

Abstract readEvaluation Study
In one paragraph

Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed.

  1. Article
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  5. Single-Cell Multiomic Analysis of Circadian Rhythmicity in Mouse Liver.bioRxiv : the preprint server for biology · 2025
    Article
  6. Article
  7. Article
  8. Article
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  10. Fibroblasts as an in vitro model of circadian genetic and genomic studies.Mammalian genome : official journal of the International Mammalian Genome Society · 2024
    Article
  11. Detecting Rhythmic Gene Expression in Single Cell Transcriptomics.bioRxiv : the preprint server for biology · 2024
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Wenwen MeiDepartment of Biostatistics, University of North Carolina at Chapel Hill.
Zhiwen JiangDepartment of Biostatistics, University of North Carolina at Chapel Hill.
Yang ChenDepartment of Statistics and the Michigan Institute of Data Science, University of Michigan.
Li ChenDepartment of Medicine and a member of the Center for Computational Biology and Bioinformatics, Indiana University School of Medicine.ORCID 0000-0001-9372-5606
Aziz SancarBiochemistry and Biophysics at the University of North Carolina School of Medicine.
Yuchao JiangDepartment of Biostatistics and the Department of Genetics, University of North Carolina at Chapel Hill and a member of UNC Lineberger Comprehensive Cancer Center.ORCID 0000-0001-6905-6377

Funding

Re-Entry Supplement: Investigation of Oral Microbial Enzymes for the Detection and Treatment of Periodontal DiseaseUL1TR002489 · NCATS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BUSE, JOHN BERNARD, SHAHEEN, NICHOLAS J · 2018 to 2022
$48.6M
UNC-CH CENTER FOR ENVIRONMENTAL HEALTH &SUSCEPTIBILITYP30ES010126 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Stephanie Engel · 2001 to 2026
$36.3M
Statistical Methods for Cancer Clinical TrialsP01CA142538 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KOSOROK, MICHAEL R · 2010 to 2019
$21.3M
Molecular Mechanism of Mammalian DNA Excision Repair, DNA Damage Checkpoints and the Circadian ClockR35GM118102 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI AZIZ SANCAR · 2016 to 2026
$10.6M
Single Nucleotide Resolution Map of Formation and Repair of Bulky Adducts in the Human GenomeR01ES027255 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI SANCAR, AZIZ · 2016 to 2020
$2.3M
NCATS NIH HHS UL1 TR002489NCI NIH HHS P01 CA142538NIEHS NIH HHS P30 ES010126NIEHS NIH HHS R01 ES027255NIGMS NIH HHS R35 GM118102
6 · The paper itself

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.

Indexed as

AlgorithmsCircadian RhythmDatabases, Nucleic AcidGene Expression ProfilingGene Expression RegulationGenome-Wide Association StudyAnimalsComputer SimulationGuidelines as TopicMicebenchmarkingbiological rhythmcircadian rhythm detectionomicsprecision and recallreproducibility

Identifiers

PMID32672832
PMCPMC8138819

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.