3 research outputs found
Evidence for abundant transcription of non-coding regions in the Saccharomyces cerevisiae genome
Background: Recent studies in a growing number of organisms have yielded
accumulating evidence that a significant portion of the non-coding region in
the genome is transcribed. We address this issue in the yeast Saccharomyces
cerevisiae.
Results: Taking into account the absence of a significantly large yeast EST
database, we use microarray expression data collected for genomic regions
erroneously believed to be coding to study the expression pattern of non-coding
regions in the Saccharomyces cerevisiae genome. We find that at least 164 out
of 589 (28%) such regions are expressed under specific biological conditions.
In particular, looking at the probes that are located opposing other known
genes at the same genomic locus, we find that 88 out of 341 (26%) of these
genes support antisense transcription. The expression patterns of these
antisense genes are positively correlated. We validate these results using
RT-PCR on a sample of 6 non-coding transcripts.
Conclusions: 1. The yeast genome is transcribed on a scale larger than
previously assumed. 2. Correlated transcription of antisense genes is abundant
in the yeast genome. 3. Antisense genes in yeast are non-coding.Comment: Journal version available at
http://www.biomedcentral.com/1471-2164/6/93/abstrac
Noninvasive Vocal Biomarker is Associated With Severe Acute Respiratory Syndrome Coronavirus 2 Infection
Objective: To investigate the association of voice analysis with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Patients and Methods: A vocal biomarker, a unitless scalar with a value between 0 and 1, was developed based on 434 voice samples. The biomarker training was followed by a prospective, multicenter, observational study. All subjects were tested for SARS-CoV-2, had their voice recorded to a smartphone application, and gave their informed consent to participate in the study. The association of SARS-CoV-2 infection with the vocal biomarker was evaluated. Results: The final study population included 80 subjects with a median age of 29 [range, 23 to 36] years, of whom 68% were men. Forty patients were positive for SARS-CoV-2. Infected patients were 12 times more likely to report at least one symptom (odds ratio, 11.8; P<.001). The vocal biomarker was significantly higher among infected patients (OR, 0.11; 95% CI, 0.06 to 0.17 vs OR, 0.19; 95% CI, 0.12 to 0.3; P=.001). The area under the receiver operating characteristic curve evaluating the association of the vocal biomarker with SARS-CoV-2 status was 72%. With a biomarker threshold of 0.115, the results translated to a sensitivity and specificity of 85% (95% CI, 70% to 94%) and 53% (95% CI, 36% to 69%), respectively. When added to a self-reported symptom classifier, the area under the curve significantly improved from 0.775 to 0.85. Conclusion: Voice analysis is associated with SARS-CoV-2 status and holds the potential to improve the accuracy of self-reported symptom-based screening tools. This pilot study suggests a possible role for vocal biomarkers in screening for SARS-CoV-2–infected subjects