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Author(s):  
Akram Jaddoa Khalaf ◽  
Samir Jasim Mohammed

<span lang="EN-US">The ECG signal processing methods are tested and evaluated based on many databases. The most ECG database used for many researchers is the MIT-BIH arrhythmia database. The QRS-detection algorithms are essential for ECG analyses to detect the beats for the ECG signal. There is no standard number of beats for this database that are used from numerous researches. Different beat numbers are calculated for the researchers depending on the difference in understanding the annotation file. In this paper, the beat numbers for existing methods are studied and compared to find the correct beat number that should be used. We propose a simple function to standardize the beats number for any ECG PhysioNet database to improve the waveform database toolbox (WFDB) for the MATLAB program. This function is based on the annotation's description from the databases and can be added to the Toolbox. The function is removed the non-beats annotation without any errors. The results show a high percentage of 71% from the reviewed methods used an incorrect number of beats for this database.</span>


mSphere ◽  
2020 ◽  
Vol 5 (4) ◽  
Author(s):  
Hailee M. Sorensen ◽  
Rebecca A. Keogh ◽  
Marcus A. Wittekind ◽  
Andrew R. Caillet ◽  
Richard E. Wiemels ◽  
...  

ABSTRACT Regulatory small RNAs (sRNAs) are known to play important roles in the Gram-positive bacterial pathogen Staphylococcus aureus; however, their existence is often overlooked, primarily because sRNA genes are absent from genome annotation files. Consequently, transcriptome sequencing (RNA-Seq)-based experimental approaches, performed using standard genome annotation files as a reference, have likely overlooked data for sRNAs. Previously, we created an updated S. aureus genome annotation file, which included annotations for 303 known sRNAs in USA300. Here, we utilized this updated reference file to reexamine publicly available RNA-Seq data sets in an attempt to recover lost information on sRNA expression, stability, and potential to encode peptides. First, we used transcriptomic data from 22 studies to identify how the expression of 303 sRNAs changed under 64 different experimental conditions. Next, we used RNA-Seq data from an RNA stability assay to identify highly stable/unstable sRNAs. We went on to reanalyze a ribosome profiling (Ribo-seq) data set to identify sRNAs that have the potential to encode peptides and to experimentally confirm the presence of three of these peptides in the USA300 background. Interestingly, one of these sRNAs/peptides, encoded at the tsr37 locus, influences the ability of S. aureus cells to autoaggregate. Finally, we reexamined two recently published in vivo RNA-Seq data sets, from the cystic fibrosis (CF) lung and a murine vaginal colonization study, and identified 29 sRNAs that may play a role in vivo. Collectively, these results can help inform future studies of these important regulatory elements in S. aureus and highlight the need for ongoing curating and updating of genome annotation files. IMPORTANCE Regulatory small RNAs (sRNAs) are a class of RNA molecules that are produced in bacterial cells but that typically do not encode proteins. Instead, they perform a variety of critical functions within the cell as RNA. Most bacterial genomes do not include annotations for sRNA genes, and any type of analysis that is performed using a bacterial genome as a reference will therefore overlook data for sRNAs. In this study, we reexamined hundreds of previously generated S. aureus RNA-Seq data sets and reanalyzed them to generate data for sRNAs. To do so, we utilized an updated S. aureus genome annotation file, previously generated by our group, which contains annotations for 303 sRNAs. The data generated (which were previously discarded) shed new light on sRNAs in S. aureus, most of which are unstudied, and highlight certain sRNAs that are likely to play important roles in the cell.


2018 ◽  
Author(s):  
Anica Scholz ◽  
Florian Eggenhofer ◽  
Rick Gelhausen ◽  
Björn Grüning ◽  
Kathi Zarnack ◽  
...  

AbstractRibosome profiling (ribo-seq) provides a means to analyze active translation by determining ribosome occupancy in a transcriptome-wide manner. The vast majority of ribosome protected fragments (RPFs) resides within the protein-coding sequence of mRNAs. However, commonly reads are also found within the transcript leader sequence (TLS) (aka 5’ untranslated region) preceding the main open reading frame (ORF), indicating the translation of regulatory upstream ORFs (uORFs). Here, we present a workflow for the identification of translation-regulatory uORFs. Specifically, uORF-Tools identifies uORFs within a given dataset and generates a uORF annotation file. In addition, a comprehensive human uORF annotation file, based on 35 ribo-seq files, is provided, which can serve as an alternative input file for the workflow. To assess the translation-regulatory activity of the uORFs, stimulus-induced changes in the ratio of the RPFs residing in the main ORFs relative to those found in the associated uORFs are determined. The resulting output file allows for the easy identification of candidate uORFs, which have translation-inhibitory effects on their associated main ORFs. uORF-Tools is available as a free and open Snakemake workflow at https://github.com/Biochemistry1-FFM/uORF-Tools. It is easily installed and all necessary tools are provided in a version-controlled manner, which also ensures lasting usability. uORF-Tools is designed for intuitive use and requires only limited computing times and resources.


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