Background: In an effort to identify the best practice for finding genes in prokaryotic genomes and propose it as a standard for automated annotation pipelines, 1,004,576 peptides were collected from various publicly available resources, and were used as a basis to evaluate various gene-calling methods. The peptides came from 45 bacterial replicons with an average GC content from 31 % to 74 %, biased toward higher GC content genomes. Automated, manual, and semi-manual methods were used to tally errors in three widely used gene calling methods, as evidenced by peptides mapped outside the boundaries of called genes.
Results: We found that the consensus set of identical genes predicted by the three methods constitutes only about 70 % of the genes predicted by each individual method (with start and stop required to coincide). Peptide data was useful for evaluating some of the differences between gene callers, but not reliable enough to make the results conclusive, due to limitations inherent in any proteogenomic study.
Conclusions: A single, unambiguous, unanimous best practice did not emerge from this analysis, since the available proteomics data were not adequate to provide an objective measurement of differences in the accuracy between these methods. However, as a result of this study, software, reference data, and procedures have been better matched among participants, representing a step toward a much-needed standard. In the absence of sufficient amount of exprimental data to achieve a universal standard, our recommendation is that any of these methods can be used by the community, as long as a single method is employed across all datasets to be compared.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4572445 | PMC |
http://dx.doi.org/10.1186/s40793-015-0034-9 | DOI Listing |
Nurs Res Pract
January 2025
Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Unlabelled: Artificial intelligence (AI) is constantly improving the quality of medical procedures. Despite the application of AI in the healthcare industry, there are conflicting opinions among professionals, and limited research on its practical application in Saudi Arabia was conducted.
Aim: To assess the nurses' knowledge regarding the application of AI in practice at one of the Ministry of Health hospitals in Saudi Arabia.
Perspect ASHA Spec Interest Groups
August 2023
School of Speech, Language, and Hearing Sciences, San Diego State University, San Diego, CA.
Purpose: Speech-language pathologists (SLPs) are tasked with integrating the principles of evidence-based practice (EBP) to provide effective and efficient assessment and intervention services that best support clients and their families. As new research, technologies, and perspectives emerge, SLPs are required to adapt their clinical practices to meet these changes while maintaining high-quality evidence-based services. Through an illustrative case study, we aim to demonstrate the process of applying EBP principles - including research evidence, client and family perspectives, and clinical expertise - to a complexity-based speech sound intervention delivered via telepractice.
View Article and Find Full Text PDFBackground: Statistical significance currently defines superiority in phase III oncology trials. However, this practice is increasingly questioned. Here, we estimated the fragility of phase III oncology trials.
View Article and Find Full Text PDFCan Pharm J (Ott)
January 2025
Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB.
Purpose: As the scope of practice continues to evolve for pharmacists, professional abstinence is being observed by students in workplaces and practicums. Professional abstinence is defined as "consciously choosing not to provide the full scope of patient care activities". Exposure of students to professional abstinence may cause cognitive dissonance, as they are challenged by practices that do not match what they are taught in school.
View Article and Find Full Text PDFHeliyon
January 2025
HUN-REN-PE Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, Egyetem u. 10, P.O. BOX 158, Veszprém, H-8200, Hungary.
This paper introduces a methodology for handling different types of uncertainties during robust optimization. In real-world industrial optimization problems, many types of uncertainties emerge, e.g.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!