ArticleBioinformatics (Oxford, England)2010
Analyzing circadian expression data by harmonic regression based on autoregressive spectral estimation.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 125 papers, 1 of them a synthesis that pooled it.
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Who cites it
125 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for Genome-Scale Analysis of Biological Rhythms.Journal of biological rhythms · 2017Guideline
- Effects of insufficient sleep on circadian rhythmicity and expression amplitude of the human blood transcriptome.Proceedings of the National Academy of Sciences of the United States of America · 2013Trial
- Intrinsic circadian organization of the spinal cord and dorsal root ganglia.Pain reports · 2026Article
- Metabolic Trajectory and Metabolic Robustness Assessment of Grazing Dairy Cows Through an Integrative Approach.Journal of animal physiology and animal nutrition · 2026Article
- Article
- DCPR: a deep learning framework for circadian phase reconstruction.BMC bioinformatics · 2025Article
- Optimization of experimental designs for biological rhythm discovery.PLoS computational biology · 2025Article
- Rhythmic Dynamics of Stress Granules inInternational journal of molecular sciences · 2025Article
- A Bayesian framework for genome-wide circadian rhythmicity biomarker detection.Briefings in bioinformatics · 2025Article
- Site- and cell-type-specific miRNA and mRNA genes and networks across the cortex, striatum, and hypothalamus.Communications biology · 2025Article
- Single-Cell Multiomic Analysis of Circadian Rhythmicity in Mouse Liver.bioRxiv : the preprint server for biology · 2025Article
- Upstream open reading frames dynamically modulate CLOCK protein translation to regulate circadian rhythms and sleep.PLoS biology · 2025Article
- Parasite and vector circadian clocks mediate efficient malaria transmission.Nature microbiology · 2025Article
- Designing and Evaluating Circadian Experiments on Mouse Skin.The Journal of investigative dermatology · 2025Review
- A curated tissue-specific proteome, phosphoproteome, and kinome map of Drosophila melanogaster with an integrated outlook in circadian physiology.Functional & integrative genomics · 2025Article
- Time to start taking time seriously: how to investigate unexpected biological rhythms within infectious disease research.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- Detecting Rhythmic Gene Expression in Single-cell Transcriptomics.Journal of biological rhythms · 2024Article
- Circadian regulation of endoplasmic reticulum calcium response in cultured mouse astrocytes.eLife · 2024Article
- Fibroblasts as an in vitro model of circadian genetic and genomic studies.Mammalian genome : official journal of the International Mammalian Genome Society · 2024Article
- Misaligned feeding uncouples daily rhythms within brown adipose tissue and between peripheral clocks.Cell reports · 2024Article
65 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationCircadian rhythms are prevalent in most organisms. Identification of circadian-regulated genes is a crucial step in discovering underlying pathways and processes that are clock-controlled. Such genes are largely detected by searching periodic patterns in microarray data. However, temporal gene expression profiles usually have a short time-series with low sampling frequency and high levels of noise. This makes circadian rhythmic analysis of temporal microarray data very challenging.
resultsWe propose an algorithm named ARSER, which combines time domain and frequency domain analysis for extracting and characterizing rhythmic expression profiles from temporal microarray data. ARSER employs autoregressive spectral estimation to predict an expression profile's periodicity from the frequency spectrum and then models the rhythmic patterns by using a harmonic regression model to fit the time-series. ARSER describes the rhythmic patterns by four parameters: period, phase, amplitude and mean level, and measures the multiple testing significance by false discovery rate q-value. When tested on well defined periodic and non-periodic short time-series data, ARSER was superior to two existing and widely-used methods, COSOPT and Fisher's G-test, during identification of sinusoidal and non-sinusoidal periodic patterns in short, noisy and non-stationary time-series. Finally, analysis of Arabidopsis microarray data using ARSER led to identification of a novel set of previously undetected non-sinusoidal periodic transcripts, which may lead to new insights into molecular mechanisms of circadian rhythms. AVAILABILITY: ARSER is implemented by Python and R. All source codes are available from http://bioinformatics.cau.edu.cn/ARSER.
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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.