Context-aware transcript quantification from long-read RNA-seq data with Bambu.

Most approaches to transcript quantification rely on fixed reference annotations; however, the transcriptome is dynamic and depending on the context, such static annotations contain inactive isoforms for some genes, whereas they are incomplete for others. Here we present Bambu, a method that performs machine-learning-based transcript discovery to enable quantification specific to the context of interest using long-read RNA-sequencing. To identify novel transcripts, Bambu estimates the novel discovery rate, which replaces arbitrary per-sample thresholds with a single, interpretable, precision-calibrated parameter. Bambu retains the full-length and unique read counts, enabling accurate quantification in presence of inactive isoforms. Compared to existing methods for transcript discovery, Bambu achieves greater precision without sacrificing sensitivity. We show that context-aware annotations improve quantification for both novel and known transcripts. We apply Bambu to quantify isoforms from repetitive HERVH-LTR7 retrotransposons in human embryonic stem cells, demonstrating the ability for context-specific transcript expression analysis.

Prevalence of Burnout in Medical and Surgical Residents: A Meta-Analysis.

The burnout syndrome is characterized by emotional exhaustion, depersonalization, and reduced personal achievement. Uncertainty exists about the prevalence of burnout among medical and surgical residents. Associations between burnout and gender, age, specialty, and geographical location of training are unclear. In this meta-analysis, we aimed to quantitatively summarize the global prevalence rates of burnout among residents, by specialty and its contributing factors. We searched PubMed, PsycINFO, Embase, and Web of Science to identify studies that examined the prevalence of burnout among residents from various specialties and countries. The primary outcome assessed was the aggregate prevalence of burnout among all residents. The random effects model was used to calculate the aggregate prevalence, and heterogeneity was assessed by I2 statistic and Cochran's Q statistic. We also performed meta-regression and subgroup analysis. The aggregate prevalence of burnout was 51.0% (95% CI: 45.0-57.0%, I2 = 97%) in 22,778 residents. Meta-regression found that the mean age (ß = 0.34, 95% CI: 0.28-0.40, p < 0.001) and the proportion of males (ß = 0.4, 95% CI = 0.10-0.69, p = 0.009) were significant moderators. Subgroup analysis by specialty showed that radiology (77.16%, 95% CI: 5.99-99.45), neurology (71.93%, 95% CI: 65.78-77.39), and general surgery (58.39%, 95% CI: 45.72-70.04) were the top three specialties with the highest prevalence of burnout. In contrast, psychiatry (42.05%, 95% CI: 33.09-51.58), oncology (38.36%, 95% CI: 32.69-44.37), and family medicine (35.97%, 95% CI: 13.89-66.18) had the lowest prevalence of burnout. Subgroup analysis also found that the prevalence of burnout in several Asian countries was 57.18% (95% CI: 45.8-67.85); in several European countries it was 27.72% (95% CI: 17.4-41.11) and in North America it was 51.64% (46.96-56.28). Our findings suggest a high prevalence of burnout among medical and surgical residents. Older and male residents suffered more than their respective counterparts.