{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T05:37:57Z","timestamp":1740980277203,"version":"3.38.0"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Summary: We propose a three-step periodicity detection algorithm named LSPR. Our method first preprocesses the raw time-series by removing the linear trend and filtering noise. In the second step, LSPR employs a Lomb\u2013Scargle periodogram to estimate the periodicity in the time-series. Finally, harmonic regression is applied to model the cyclic components. Inferred periodic transcripts are selected by a false discovery rate procedure. We have applied LSPR to unevenly sampled synthetic data and two Arabidopsis diurnal expression datasets, and compared its performance with the existing well-established algorithms. Results show that LSPR is capable of identifying periodic transcripts more accurately than existing algorithms.<\/jats:p><jats:p>Availability: LSPR algorithm is implemented as MATLAB software and is available at http:\/\/bioinformatics.cau.edu.cn\/LSPR<\/jats:p><jats:p>Contact: \u00a0zhensu@cau.edu.cn<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btr041","type":"journal-article","created":{"date-parts":[[2011,2,5]],"date-time":"2011-02-05T01:12:51Z","timestamp":1296868371000},"page":"1023-1025","source":"Crossref","is-referenced-by-count":19,"title":["LSPR: an integrated periodicity detection algorithm for unevenly sampled temporal microarray data"],"prefix":"10.1093","volume":"27","author":[{"given":"Rendong","family":"Yang","sequence":"first","affiliation":[{"name":"1 Division of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193 and 2Department of Applied Mathematics, College of Science, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Zhang","sequence":"additional","affiliation":[{"name":"1 Division of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193 and 2Department of Applied Mathematics, College of Science, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Su","sequence":"additional","affiliation":[{"name":"1 Division of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193 and 2Department of Applied Mathematics, College of Science, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2011,2,3]]},"reference":[{"key":"2023012512164743000_B1","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1109\/TAC.1974.1100705","article-title":"A new look at the statistical model identification","volume":"19","author":"Akaike","year":"1974","journal-title":"IEEE Trans. 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