Dataset Information

Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions.

ABSTRACT: Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and current computational annotation methods ignore intermediate states because the classification features become ambiguous, even though intermediate states contain important information regarding vigilance state dynamics. To address this problem, we have developed "Somnotate"-a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). First we demonstrate that Somnotate sets new standards in polysomnography, exhibiting annotation accuracies that exceed human experts on mouse electrophysiological data, remarkable robustness to errors in the training data, compatibility with different recording configurations, and an ability to maintain high accuracy during experimental interventions. However, the key feature of Somnotate is that it quantifies and reports the certainty of its annotations. We leverage this feature to reveal that many intermediate vigilance states cluster around state transitions, whereas others correspond to failed attempts to transition. This enables us to show for the first time that the success rates of different types of transition are differentially affected by experimental manipulations and can explain previously observed sleep patterns. Somnotate is open-source and has the potential to both facilitate the study of sleep stage transitions and offer new insights into the mechanisms underlying sleep-wake dynamics.

SUBMITTER: Brodersen PJN

PROVIDER: S-EPMC10824458 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions.

Brodersen Paul J N PJN Alfonsa Hannah H Krone Lukas B LB Blanco-Duque Cristina C Fisk Angus S AS Flaherty Sarah J SJ Guillaumin Mathilde C C MCC Huang Yi-Ge YG Kahn Martin C MC McKillop Laura E LE Milinski Linus L Taylor Lewis L Thomas Christopher W CW Yamagata Tomoko T Foster Russell G RG Vyazovskiy Vladyslav V VV Akerman Colin J CJ

PLoS computational biology 20240117 1

Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and current computational annotation methods ignore intermediate states because the classification features become ambiguou ...[more]

PMID: 38232122

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Project description:BackgroundOlfactory receptors (ORs), the largest mammalian gene superfamily (900-1400 genes), has >50% pseudogenes in humans. While most of these inactive genes are identified via coding frame (nonsense) disruptions, seemingly intact genes may also be inactive due to other deleterious (missense) mutations. An ultimate assessment of the actual size of the functional human OR repertoire thus requires an accurate distinction between genes and pseudogenes.ResultsTo characterize inactive ORs with intact open reading frame, we have developed a probabilistic Classifier for Olfactory Receptor Pseudogenes (CORP). This algorithm is based on deviations from a functionally crucial consensus, constituting sixty highly conserved positions identified by a comparison of two evolutionarily-constrained OR repertoires (mouse and dog) with a small pseudogene fraction. We used a logistic regression analysis to assign appropriate coefficients to the conserved position and thus achieving maximal separation between active and inactive ORs. Consequently, the algorithms identified only 5% of the mouse functional ORs as pseudogenes, setting an upper limit of 0.05 to the false positive detection. Finally we used this algorithm to classify the 384 purportedly intact human OR genes. Of these, 135 were predicted as likely encoding non-functional proteins, and 38 were segregating between active and inactive forms due to missense polymorphisms.ConclusionWe demonstrated that the CORP algorithm is capable to distinguish between functional and non-functional OR genes with high precision even when the encoded protein would differ by a single amino acid. Using the CORP algorithm, we predict that approximately 70% of human OR genes are likely non-functional pseudogenes, a much higher number than hitherto suspected. The method we present may be employed for better annotation of inactive members in other gene families as well. CORP algorithm is available at: http://bioportal.weizmann.ac.il/HORDE/CORP/

Dataset Information

Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions.

Publications

Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions.

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