Evidence map›Paper›PMID 24809473›Full record

ArticlePloS one2014

Strengths and limitations of period estimation methods for circadian data.

Tomasz Zielinski, Anne M Moore, Eilidh Troup, Karen J Halliday, Andrew J Millar

Abstract readComparative Study
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 212 papers.

0numbers the graph read from it
0cells of the map it votes in
212citing 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

212 citing papers in PubMed.

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  4. Dim green light enables day-and-night monitoring of leaf movements.The Plant journal : for cell and molecular biology · 2026
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  7. Thermal imaging as a tool for studying circadian rhythms in roots.The Plant journal : for cell and molecular biology · 2026
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  17. Circadian clock proteins KaiB and Rbp2 ofMicrobiology spectrum · 2026
    Article
  18. Article
  19. Hypertension (Dallas, Tex. : 1979) · 2026
    Article
  20. Article

152 more citing papers are in PubMed but not listed here.

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

5 authors.

Tomasz ZielinskiSynthSys, University of Edinburgh, Edinburgh, United Kingdom.
Anne M MooreSynthSys, University of Edinburgh, Edinburgh, United Kingdom.
Eilidh TroupEPCC, University of Edinburgh, Edinburgh, United Kingdom.
Karen J HallidaySynthSys, University of Edinburgh, Edinburgh, United Kingdom.
Andrew J MillarSynthSys, University of Edinburgh, Edinburgh, United Kingdom.

Funding

Biotechnology and Biological Sciences Research Council BB/D019621Biotechnology and Biological Sciences Research Council BB/D019621/1Biotechnology and Biological Sciences Research Council BB/F005237Biotechnology and Biological Sciences Research Council BB/F005237/1Biotechnology and Biological Sciences Research Council BB/F005296/1Biotechnology and Biological Sciences Research Council BB/J009423
6 · The paper itself

Abstract

A key step in the analysis of circadian data is to make an accurate estimate of the underlying period. There are many different techniques and algorithms for determining period, all with different assumptions and with differing levels of complexity. Choosing which algorithm, which implementation and which measures of accuracy to use can offer many pitfalls, especially for the non-expert. We have developed the BioDare system, an online service allowing data-sharing (including public dissemination), data-processing and analysis. Circadian experiments are the main focus of BioDare hence performing period analysis is a major feature of the system. Six methods have been incorporated into BioDare: Enright and Lomb-Scargle periodograms, FFT-NLLS, mFourfit, MESA and Spectrum Resampling. Here we review those six techniques, explain the principles behind each algorithm and evaluate their performance. In order to quantify the methods' accuracy, we examine the algorithms against artificial mathematical test signals and model-generated mRNA data. Our re-implementation of each method in Java allows meaningful comparisons of the computational complexity and computing time associated with each algorithm. Finally, we provide guidelines on which algorithms are most appropriate for which data types, and recommendations on experimental design to extract optimal data for analysis.

Indexed as

AlgorithmsModels, TheoreticalPeriodicityCircadian RhythmHumans

Identifiers

PMID24809473
PMCPMC4014635

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.