RawBeans: A Simple, Vendor-Independent, Raw-Data Quality-Control Tool.
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ABSTRACT:
SUBMITTER: Morgenstern D
PROVIDER: S-EPMC8041395 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Every laboratory performing mass spectrometry based proteomics strives to generate high quality data. Among the many factors that influence the outcome of any experiment in proteomics is performance of the LC-MS system, which should be monitored continuously. This process is termed quality control (QC). We present an easy to use, rapid tool, which produces a visual, HTML based report that includes the key parameters needed to monitor LC-MS system perfromance. The tool, named RawBeans, can generate a report for individual files, or for a set of samples from a whole experiment. We anticipate it will help proteomics users and experts evaluate raw data quality, independent of data processing. The tool is available here: https://bitbucket.org/incpm/prot-qc/downloads.
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Project description:RawTools is a software that provides parsing and quantification of raw Thermo Orbitrap mass spectrometer data. RawTools software was used to process a set of injections (n = 140) from a prepared HeLa digest that were analyzed on an Orbitrap Velos to get summarized instrument performance metrics for quality control.
2018-09-18 | PXD011069 | Pride
Project description:Recent advances in the sensitivity and speed of mass spectrometers coupled with improved sample preparation methods have enabled the field of single cell proteomics to proliferate. While heavy development is occurring in the label free space, dramatic improvements in throughput are provided by multiplexing with tandem mass tags. Hundreds or thousands of single cells can be analyzed with this method, yielding large data sets which may contain poor data arising from loss of material during cell sorting or poor digestion, labeling, and lysis. To date, no tools have been described that can assess data quality prior to data processing. We present herein a lightweight python script and accompanying graphic user interface that can rapidly quantify reporter ion peaks within each MS/MS spectrum in a file. With simple summary reports, we can identify single cell samples that fail to pass a set quality threshold, thus reducing analysis time waste. In addition, this tool, Diagnostic Ion Data Analysis Reduction (DIDAR), will create reduced MGF files containing only spectra possessing a user-specified number of single cell reporter ions. By reducing the number of spectra that have excessive zero values, we can speed up sample processing with little loss in data completeness as these spectra are removed in later stages in data processing workflows. DIDAR and the DIDAR GUI are compatible with all modern operating systems and are available at: https://github.com/orsburn/DIDARSCPQC. All files described in this study are available at www.massive.ucsd.edu as accession MSV000088887.
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Project description:BackgroundTwo-dimensional speckle-tracking echocardiography (2D-STE) enables objective assessment of left atrial (LA) deformation through the analysis of myocardial strain, which can be measured by different speckle-tracking software. The aim of this study was to compare the consistency of 3 different commercially available software, which include vendor-specific software for measuring left ventricle (VSSLV), vendor-independent software packages for measuring LV strain (VISLV) and vendor-independent software packages for measuring LA strain (VISLA).MethodsSixty-four subjects (mean age: 44 ± 16 years, 50% males) underwent conventional echocardiograms using a GE Vivid 9 (GE Ultrasound, Horten, Norway) cardiac ultrasound system. Standard apical 4 and 2 chamber views of the left atrium were obtained in each subject with a frame-rate range of 40-71 frames/s. LA strain during the contraction phase (Sct), conduit phase (Scd), reservoir phase (Sr = Sct + Scd) were analyzed by 2 independent observers and 3 different software.ResultsSct, Scd, Sr were, respectively, - 11.26 ± 2.45%, - 16.77 ± 7.06%, and 28.03 ± 7.58% with VSSLV, - 14.77 ± 3.59%, - 23.17 ± 10.33%, and 38.23 ± 10.99% with VISLV, and - 14.80 ± 3.88%, - 23.94 ± 10.48%, and 38.73 ± 11.56% when VISLA was used. A comparison of strain measurements between VSSLV and VIS (VISLV and VISLA) showed VIS had significantly smaller mean differences and narrower limits of agreement. Similar results were observed in the coefficient of variation (CV) for measurements between VSSLV and VIS (VISLV and VISLA). Comparison of the intra-class correlation coefficients (ICCs) indicated that measurement reliability was weaker with VSSLV (ICC < 0.6) than with VIS (VISLV and VISLA) (ICC > 0.9). For intra-observer ICCs, VISLA > VSSLV = VISLV. For inter-observer ICCs, VSSLV > VISLA > VISLV.ConclusionsSoftware measurement results of LA strain vary considerably. We recommended not measuring LA strain across vendor platforms.
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Project description:BACKGROUND:Right ventricular peak systolic longitudinal strain (RVLS) has emerged as an approach for quantifying right ventricular function in diseases such as pulmonary hypertension and congenital heart disease. A major limitation in applying RVLS is that strain imaging and analysis are proprietary, which may result in systematic differences from vendor to vendor. The goal of this study was to test the reproducibility of right ventricular strain analysis among selected vendor-specific software (VSS) and vendor-independent software (VIS) on images obtained from different ultrasound scanners, as would be common in clinical practice or in a multicenter clinical trial. METHODS:In this prospective, single-center study, 35 patients (5 healthy subjects and 30 with pulmonary hypertension) each underwent two echocardiographic scans, one using GE (Vivid E9) and the other using Philips (iE33) ultrasound systems. Images were analyzed using both VSS and VIS (TomTec) software for determination of RVLS. A repeated-measures analysis of variance was used to assess for any systematic differences among methods, as well as effects of scanner and software and a possible interaction between scanner and software for each strain measurement. RESULTS:Differences for global strains were not statistically significant among VSS packages (P ≥ .05), but some differences were noted between VSS and VIS. Wide variability between regional peak strain measurements was noted, but no systematic differences were found. CONCLUSIONS:Global RVLS values between VSS systems are not significantly different but may differ slightly from VIS. When comparing regional strain between VSS and VIS analyses, there is widespread variability without clear systematic differences.
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Project description:We propose LongQC as an easy and automated quality control tool for genomic datasets generated by third generation sequencing (TGS) technologies such as Oxford Nanopore technologies (ONT) and SMRT sequencing from Pacific Bioscience (PacBio). Key statistics were optimized for long read data, and LongQC covers all major TGS platforms. LongQC processes and visualizes those statistics automatically and quickly.
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Project description:We describe a protocol to perform fast and non-arbitrary quality control of single-cell RNA sequencing (scRNA-seq) raw data using scKB and COPILOT. scKB is a wrapper script of kallisto and bustools for accelerated alignment and transcript count matrix generation, which runs significantly faster than the popular tool Cell Ranger. COPILOT then offers non-arbitrary background noise removal by comparing distributions of low-quality and high-quality cells. Together, this protocol streamlines the processing workflow and provides an easy entry for new scRNA-seq users. For complete details on the use and execution of this protocol, please refer to Shahan et al. (2022).
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Project description:In recent years, the number of sequences of diverse species submitted to GenBank has grown explosively and not infrequently the data contain errors. This problem is extensively recognized but not for invalid or incorrectly identified species, sample mixed-up, and contamination. DNA barcoding is a powerful tool for identifying and confirming species and one very important application involves forensics. In this study, we use DNA barcoding to detect erroneous sequences in GenBank by evaluating deep intraspecific and shallow interspecific divergences to discover possible taxonomic problems and other sources of error. We use the mitochondrial DNA gene encoding cytochrome b (Cytb) from turtles to test the utility of barcoding for pinpointing potential errors. This gene is widely used in phylogenetic studies of the speciose group. Intraspecific variation is usually less than 2.0% and in most cases it is less than 1.0%. In comparison, most species differ by more than 10.0% in our dataset. Overlapping intra- and interspecific percentages of variation mainly involve problematic identifications of species and outdated taxonomies. Further, we detect identical problems in Cytb from Insectivora and Chiroptera. Upon applying this strategy to 47,524 mammalian CoxI sequences, we resolve a suite of potentially problematic sequences. Our study reveals that erroneous sequences are not rare in GenBank and that the DNA barcoding can serve to confirm sequencing accuracy and discover problems such as misidentified species, inaccurate taxonomies, contamination, and potential errors in sequencing.
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Project description:PurposeThere is an increasing availability of large imaging cohorts [such as through The Cancer Imaging Archive (TCIA)] for computational model development and imaging research. To ensure development of generalizable computerized models, there is a need to quickly determine relative quality differences in these cohorts, especially when considering MRI datasets which can exhibit wide variations in image appearance. The purpose of this study is to present a quantitative quality control tool, MRQy, to help interrogate MR imaging datasets for: (a) site- or scanner-specific variations in image resolution or image contrast, and (b) imaging artifacts such as noise or inhomogeneity; which need correction prior to model development.MethodsUnlike existing imaging quality control tools, MRQy has been generalized to work with images from any body region to efficiently extract a series of quality measures (e.g., noise ratios, variation metrics) and MR image metadata (e.g., voxel resolution and image dimensions). MRQy also offers a specialized HTML5-based front-end designed for real-time filtering and trend visualization of quality measures.ResultsMRQy was used to evaluate (a) n = 133 brain MRIs from TCIA (7 sites) and (b) n = 104 rectal MRIs (3 local sites). MRQy measures revealed significant site-specific variations in both cohorts, indicating potential batch effects. Before processing, MRQy measures could be used to identify each of the seven sites within the TCIA cohort with 87.5%, 86.4%, 90%, 93%, 90%, 60%, and 92.9% accuracy and the three sites within the rectal cohort with 91%, 82.8%, and 88.9% accuracy using unsupervised clustering. After processing, none of the sites could be distinctively clustered via MRQy measures in either cohort; suggesting that batch effects had been largely accounted for. Marked differences in specific MRQy measures were also able to identify outlier MRI datasets that needed to be corrected for common acquisition artifacts.ConclusionsMRQy is designed to be a standalone, unsupervised tool that can be efficiently run on a standard desktop computer. It has been made freely accessible and open-source at http://github.com/ccipd/MRQy for community use and feedback.
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