Quantitative Data Integration Analysis Method for Cross-Studies: Obstructive Sleep Apnea as an Example.

Objective: In recent years, the prevalence of obstructive sleep apnea (OSA) has gradually increased. The diagnosis of this multiphenotypic disorder requires a combination of several indicators. The objective of this study was to find significant apnea monitor indicators of OSA by developing a strategy for cross-study screening and integration of quantitative data. Methods: Articles related to sleep disorders were obtained from the PubMed database. A sleep disorder dataset and an OSA dataset were manually curated from these articles. Two evaluation indexes, the indicator coverage ratio (ICR) and the study integrity ratio (SIR), were used to filter out OSA indicators from the OSA dataset and create profiles including different numbers of indicators and studies for analysis. Data were analyzed by the meta 4.18-0 package of R, and the p value and standard mean difference (SMD) values were calculated to evaluate the change of each indicator. Results: The sleep disorder dataset was constructed based on 178 studies from 119 publications, the OSA dataset was extracted from 89 studies, 284 sleep-related indicators were filtered out, and 22 profiles were constructed. Apnea hypopnea index was significantly decreased in all 22 profiles. Total sleep time (TST) (min) showed no significant differences in 21 profiles. There were significant increases in rapid eye movement (REM) (%TST) in 18 profiles, minimum arterial oxygen saturation (SaO2) in 9 profiles, REM duration in 3 profiles, and slow wave sleep duration (%TST) and pulse oximetry lowest point in 2 profiles. There were significant decreases in apnea index (AI) in 14 profiles; arousal index and SaO2 < 90 (%TST) in 8 profiles; N1 stage (%TST) in 7 profiles; and hypopnea index, N1 stage (% sleep period time (%SPT)), N2 stage (%SPT), respiratory arousal index, and respiratory disorder index in 2 profiles. Conclusion: The proposed data integration strategy successfully identified multiple significant OSA indicators.

TransCirc: an interactive database for translatable circular RNAs based on multi-omics evidence.

TransCirc (https://www.biosino.org/transcirc/) is a specialized database that provide comprehensive evidences supporting the translation potential of circular RNAs (circRNAs). This database was generated by integrating various direct and indirect evidences to predict coding potential of each human circRNA and the putative translation products. Seven types of evidences for circRNA translation were included: (i) ribosome/polysome binding evidences supporting the occupancy of ribosomes onto circRNAs; (ii) experimentally mapped translation initiation sites on circRNAs; (iii) internal ribosome entry site on circRNAs; (iv) published N-6-methyladenosine modification data in circRNA that promote translation initiation; (v) lengths of the circRNA specific open reading frames; (vi) sequence composition scores from a machine learning prediction of all potential open reading frames; (vii) mass spectrometry data that directly support the circRNA encoded peptides across back-splice junctions. TransCirc provides a user-friendly searching/browsing interface and independent lines of evidences to predicte how likely a circRNA can be translated. In addition, several flexible tools have been developed to aid retrieval and analysis of the data. TransCirc can serve as an important resource for investigating the translation capacity of circRNAs and the potential circRNA-encoded peptides, and can be expanded to include new evidences or additional species in the future.