Dataset Information

The risks of using the chi-square periodogram to estimate the period of biological rhythms.

ABSTRACT: The chi-square periodogram (CSP), developed over 40 years ago, continues to be one of the most popular methods to estimate the period of circadian (circa 24-h) rhythms. Previous work has indicated the CSP is sometimes less accurate than other methods, but understanding of why and under what conditions remains incomplete. Using simulated rhythmic time-courses, we found that the CSP is prone to underestimating the period in a manner that depends on the true period and the length of the time-course. This underestimation bias is most severe in short time-courses (e.g., 3 days), but is also visible in longer simulated time-courses (e.g., 12 days) and in experimental time-courses of mouse wheel-running and ex vivo bioluminescence. We traced the source of the bias to discontinuities in the periodogram that are related to the number of time-points the CSP uses to calculate the observed variance for a given test period. By revising the calculation to avoid discontinuities, we developed a new version, the greedy CSP, that shows reduced bias and improved accuracy. Nonetheless, even the greedy CSP tended to be less accurate on our simulated time-courses than an alternative method, namely the Lomb-Scargle periodogram. Thus, although our study describes a major improvement to a classic method, it also suggests that users should generally avoid the CSP when estimating the period of biological rhythms.

SUBMITTER: Tackenberg MC

PROVIDER: S-EPMC7815206 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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The risks of using the chi-square periodogram to estimate the period of biological rhythms.

Tackenberg Michael C MC Hughey Jacob J JJ

PLoS computational biology 20210106 1

The chi-square periodogram (CSP), developed over 40 years ago, continues to be one of the most popular methods to estimate the period of circadian (circa 24-h) rhythms. Previous work has indicated the CSP is sometimes less accurate than other methods, but understanding of why and under what conditions remains incomplete. Using simulated rhythmic time-courses, we found that the CSP is prone to underestimating the period in a manner that depends on the true period and the length of the time-course ...[more]

PMID: 33406069

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Project description:IntroductionPregnancy and new parenthood is an exciting time, but also a stressful life event that can predispose to mental health challenges. Perinatal anxiety is one such challenge, and is an important contributor to parental distress and other negative outcomes. Sleep and biological rhythms are often disrupted in the perinatal period. These disruptions have been associated with postpartum depression, and in some cases with perinatal anxiety. However, the literature concerning the association with perinatal anxiety is inconsistent and may be methodologically limited. To our knowledge, there has been no comprehensive review published characterising the relationships between sleep, biological rhythms, and perinatal anxiety and related disorders to date. In this systematic review, we will summarise the current state of the literature concerning these relationships, allowing us to highlight gaps and potentially inform clinical understanding of perinatal anxiety, sleep and biological rhythms.Methods and analysisPrimary research articles will be eligible for inclusion if they assess perinatal anxiety or related disorders using validated criteria (self-report or diagnostic), assess sleep and biological rhythms in the perinatal period, include >4 participants and meet other inclusion/exclusion criteria. We will conduct comprehensive searches of MEDLINE, PsycINFO, Embase and CINAHL, with coverage spanning from database conception to search date (August 1, 2021). Key search concepts include (1) the perinatal period, (2) sleep/biological rhythms and (3) anxiety. Risk of bias will be evaluated using the Cochrane Risk of Bias Tool. Data will be narratively synthesised, with quantitative synthesis included if possible. When relevant, strength of evidence will be assessed using Grading of Recommendations Assessment, Development and Evaluation criteria, and potential publication bias will be assessed.Ethics and disseminationResearch ethics approval is not required. Study results will be reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results will be disseminated to relevant stakeholders as conference presentation(s) and submitted for publication in a peer-reviewed journal.Prospero registration number200166.

Project description:BackgroundLysine succinylation is a type of protein post-translational modification which is widely involved in cell differentiation, cell metabolism and other important physiological activities. To study the molecular mechanism of succinylation in depth, succinylation sites need to be accurately identified, and because experimental approaches are costly and time-consuming, there is a great demand for reliable computational methods. Feature extraction is a key step in building succinylation site prediction models, and the development of effective new features improves predictive accuracy. Because the number of false succinylation sites far exceeds that of true sites, traditional classifiers perform poorly, and designing a classifier to effectively handle highly imbalanced datasets has always been a challenge.ResultsA new computational method, iSuc-ChiDT, is proposed to identify succinylation sites in proteins. In iSuc-ChiDT, chi-square statistical difference table encoding is developed to extract positional features, and has a higher predictive accuracy and fewer features compared to common position-based encoding schemes such as binary encoding and physicochemical property encoding. Single amino acid and undirected pair-coupled amino acid composition features are supplemented to improve the fault tolerance for residue insertions and deletions. After feature selection by Chi-MIC-share algorithm, the chi-square decision table (ChiDT) classifier is constructed for imbalanced classification. With a training set of 4748:50,551(true: false sites), ChiDT clearly outperforms traditional classifiers in predictive accuracy, and runs fast. Using an independent testing set of experimentally identified succinylation sites, iSuc-ChiDT achieves a sensitivity of 70.47%, a specificity of 66.27%, a Matthews correlation coefficient of 0.205, and a global accuracy index Q9 of 0.683, showing a significant improvement in sensitivity and overall accuracy compared to PSuccE, Success, SuccinSite, and other existing succinylation site predictors.ConclusionsiSuc-ChiDT shows great promise in predicting succinylation sites and is expected to facilitate further experimental investigation of protein succinylation.

Dataset Information

The risks of using the chi-square periodogram to estimate the period of biological rhythms.

Publications

The risks of using the chi-square periodogram to estimate the period of biological rhythms.

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