Topic
Transcription error
About: Transcription error is a research topic. Over the lifetime, 37 publications have been published within this topic receiving 377 citations. The topic is also known as: keystroke error.
Papers
TL;DR: It is discovered that all species had remarkably similar transcription error rates, which is unexpected given that both endosymbiotic species lack orthologs of several E. coli RNA fidelity factors and that lifestyle differences among these species have led to vast differences in their mutation and substitution rates.
Abstract: Errors that occur during transcription have received much less attention than the mutations that occur in DNA because transcription errors are not heritable and usually result in a very limited number of altered proteins. However, transcription error rates are typically several orders of magnitude higher than the mutation rate. Also, individual transcripts can be translated multiple times, so a single error can have substantial effects on the pool of proteins. Transcription errors can also contribute to cellular noise, thereby influencing cell survival under stressful conditions, such as starvation or antibiotic stress. Implementing a method that captures transcription errors genome-wide, we measured the rates and spectra of transcription errors in Escherichia coli and in endosymbionts for which mutation and/or substitution rates are greatly elevated over those of E. coli Under all tested conditions, across all species, and even for different categories of RNA sequences (mRNA and rRNAs), there were no significant differences in rates of transcription errors, which ranged from 2.3 × 10(-5) per nucleotide in mRNA of the endosymbiont Buchnera aphidicola to 5.2 × 10(-5) per nucleotide in rRNA of the endosymbiont Carsonella ruddii The similarity of transcription error rates in these bacterial endosymbionts to that in E. coli (4.63 × 10(-5) per nucleotide) is all the more surprising given that genomic erosion has resulted in the loss of transcription fidelity factors in both Buchnera and Carsonella.
79 citations
Journal Article•
TL;DR: The evaluation clearly showed that errors at transcription stage were not infrequent, and implementation of surveillance systems, which might help to decrease medication errors.
Abstract: Medication errors are among the most common medical errors in the hospitals. Transcription error is a specific type of medication errors and is due to data entry error that is commonly made by the human operators. This study was designed to detect transcription errors in a teaching hospital in Tehran. Direct observational method was used in this study. Error was defined as any deviation in transcribing medication order from the previous step (order on the order sheet, administration nursing note and/or cardex, documentation of the order in the pharmacy database). A total of 287 charts with 558 opportunities for error were reviewed. Of those opportunities for error 167 (29.9%) resulted in an error. Omission (the patient did not receive the medication that was ordered) was the highest (52%) transcription error type seen in this study. The evaluation clearly showed that errors at transcription stage were not infrequent. To cut these errors down we suggest implementation of surveillance systems, which might help to decrease medication errors.
64 citations
1 Jun 2014
TL;DR: Richer typesetting models are presented that extend the unsupervised historical document recognition system of BergKirkpatrick et al. (2013) and achieve a relative word error reduction of 22% compared to state-of-the-art results on a dataset of historical newspapers.
Abstract: We present richer typesetting models that extend the unsupervised historical document recognition system of BergKirkpatrick et al. (2013). The first model breaks the independence assumption between vertical offsets of neighboring glyphs and, in experiments, substantially decreases transcription error rates. The second model simultaneously learns multiple font styles and, as a result, is able to accurately track italic and nonitalic portions of documents. Richer models complicate inference so we present a new, streamlined procedure that is over 25x faster than the method used by BergKirkpatrick et al. (2013). Our final system achieves a relative word error reduction of 22% compared to state-of-the-art results on a dataset of historical newspapers.
32 citations
TL;DR: In at least some sequence contexts, Rpb9 appears to enhance TFIIS-mediated error excision by facilitating efficient formation of a conformation necessary for RNA cleavage.
Abstract: The role of the small RNA polymerase II subunit Rpb9 in transcriptional proofreading was assessed in vitro. Transcription elongation complexes in which the 3' end of the RNA is not complementary to the DNA template have a dramatically reduced rate of elongation, which provides a fidelity checkpoint at which the error can be removed. The efficiency of such proofreading depends on competing rates of error propagation (extending the RNA chain without removing the error) and error excision, a process that is facilitated by TFIIS. In the absence of Rpb9, the rate of error propagation is increased by 2- to 3-fold in numerous sequence contexts, compromising the efficiency of proofreading. In addition, the rate and extent of TFIIS-mediated error excision is also significantly compromised in the absence of Rpb9. In at least some sequence contexts, Rpb9 appears to enhance TFIIS-mediated error excision by facilitating efficient formation of a conformation necessary for RNA cleavage. If a transcription error is propagated by addition of a nucleotide to the mismatched 3' end, then the rate of further elongation increases but remains much slower than that of a complex with a fully base-paired RNA, which provides a second potential fidelity checkpoint. The absence of Rpb9 also affects both error propagation and TFIIS-mediated error excision at this potential checkpoint in a manner that compromises transcriptional fidelity. In contrast, no effects of Rpb9 on NTP selectivity were observed.
26 citations
TL;DR: In this article, a method integrating deep-neural-network-based multipitch detection and statistical-model-based rhythm quantization was proposed to convert polyphonic audio recordings into musical scores.
25 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 3 |
| 2020 | 1 |
| 2019 | 1 |
| 2018 | 2 |
| 2017 | 3 |
| 2016 | 4 |