scholarly journals ProtyQuant: Comparing Label-Free Shotgun Proteomics Datasets Using Accumulated Peptide Probabilities

2020 ◽  
Author(s):  
Robert Winkler
Keyword(s):  
False Positive Rate ◽  
Shotgun Proteomics ◽  
Label Free ◽  
Plain Text ◽  
Protein Inference ◽  
Positive Rate ◽  
Yeast Lysate ◽  
Free Search ◽  
Spectrum Matching ◽  
Free Quantification

<p>Comparing multiple label-free shotgun proteomics datasets requires various data processing and formatting steps, including peptide-spectrum matching, protein inference, and quantification. Finally, the compilation of results files into a format that allows for downstream analyses. ProtyQuant performs protein inference and quantification calculations, and combines the results of individual datasets into plain text tables. These are lightweight, human-readable, and easy to import into databases or statistical software. ProtyQuant reads validated pepXML from proteomic workflows such as the Trans-Proteomic Pipeline (TPP), which makes it compatible with many commercial and free search engines. For protein inference and quantification, a modified version of the PIPQ program (He et al. 2016) was integrated. In contrast to simple spectral-counting, PIPQ sums up peptide probabilities. For assigning peptides to proteins, three algorithms are available: Multiple Counting, Equal Division, and Linear Programming. The accumulated peptide probabilities (app) are used for both tasks, protein probability estimation, and quantification. ProtyQuant was tested using a reference dataset for label-free shotgun proteomics, obtained from different concentrations of 48 human UPS proteins spiked into yeast lysate. Compared to ProteinProphet, ProtyQuant detected up to 126 (15%) more proteins in the mixture, applying an equal false positive rate (FPR). Using the app values for label-free quantification showed suitable sensitivity and linearity. Strikingly, the app values represent a realistic measure of ‘Protein Presence,’ an integral concept of protein probability and quantity. ProtyQuant provides a graphical user interface (GUI) and scripts for console-based processing. It is available (GNU GLP v3) for Windows, Linux, and Docker from <a href="https://bitbucket.org/lababi/protyquant/">https://bitbucket.org/lababi/protyquant/</a>.</p>

scholarly journals ProtyQuant: Comparing Label-Free Shotgun Proteomics Datasets Using Accumulated Peptide Probabilities

2020 ◽  
Author(s):  
Robert Winkler
Keyword(s):  
False Positive Rate ◽  
Shotgun Proteomics ◽  
Label Free ◽  
Plain Text ◽  
Protein Inference ◽  
Positive Rate ◽  
Yeast Lysate ◽  
Free Search ◽  
Spectrum Matching ◽  
Free Quantification

<p>Comparing multiple label-free shotgun proteomics datasets requires various data processing and formatting steps, including peptide-spectrum matching, protein inference, and quantification. Finally, the compilation of results files into a format that allows for downstream analyses. ProtyQuant performs protein inference and quantification calculations, and combines the results of individual datasets into plain text tables. These are lightweight, human-readable, and easy to import into databases or statistical software. ProtyQuant reads validated pepXML from proteomic workflows such as the Trans-Proteomic Pipeline (TPP), which makes it compatible with many commercial and free search engines. For protein inference and quantification, a modified version of the PIPQ program (He et al. 2016) was integrated. In contrast to simple spectral-counting, PIPQ sums up peptide probabilities. For assigning peptides to proteins, three algorithms are available: Multiple Counting, Equal Division, and Linear Programming. The accumulated peptide probabilities (app) are used for both tasks, protein probability estimation, and quantification. ProtyQuant was tested using a reference dataset for label-free shotgun proteomics, obtained from different concentrations of 48 human UPS proteins spiked into yeast lysate. Compared to ProteinProphet, ProtyQuant detected up to 126 (15%) more proteins in the mixture, applying an equal false positive rate (FPR). Using the app values for label-free quantification showed suitable sensitivity and linearity. Strikingly, the app values represent a realistic measure of ‘Protein Presence,’ an integral concept of protein probability and quantity. ProtyQuant provides a graphical user interface (GUI) and scripts for console-based processing. It is available (GNU GLP v3) for Windows, Linux, and Docker from <a href="https://bitbucket.org/lababi/protyquant/">https://bitbucket.org/lababi/protyquant/</a>.</p>

Comparative evaluation of label-free quantification methods for shotgun proteomics

2017 ◽  
Vol 31 (7) ◽  
pp. 606-612 ◽  
Author(s):  
Julia A. Bubis ◽  
Lev I. Levitsky ◽  
Mark V. Ivanov ◽  
Irina A. Tarasova ◽  
Mikhail V. Gorshkov
Keyword(s):  
Comparative Evaluation ◽  
Shotgun Proteomics ◽  
Label Free ◽  
Label Free Quantification ◽  
Free Quantification

scholarly journals Comparison of Different Label-Free Techniques for the Semi-Absolute Quantification of Protein Abundance

Proteomes ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 2
Author(s):  
Aarón Millán-Oropeza ◽  
Mélisande Blein-Nicolas ◽  
Véronique Monnet ◽  
Michel Zivy ◽  
Céline Henry
Keyword(s):  
Shotgun Proteomics ◽  
Absolute Quantification ◽  
Biological Data ◽  
Protein Quantification ◽  
Protein Abundance ◽  
Label Free ◽  
Absolute Abundance ◽  
Yeast Saccharomyces Cerevisiae ◽  
Genome Scale ◽  
Free Quantification

In proteomics, it is essential to quantify proteins in absolute terms if we wish to compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allow protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the “gold standard” for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.

scholarly journals PeptideWitch–A Software Package to Produce High-Stringency Proteomics Data Visualizations from Label-Free Shotgun Proteomics Data

Proteomes ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 21 ◽  
Author(s):  
David C. L. Handler ◽  
Flora Cheng ◽  
Abdulrahman M. Shathili ◽  
Paul A. Haynes
Keyword(s):  
Protein Identification ◽  
Shotgun Proteomics ◽  
Label Free ◽  
Proteomics Data ◽  
Proteomics Analysis ◽  
High Stringency ◽  
The Difference ◽  
Data Quality Metrics ◽  
Data Visualizations ◽  
Spectrum Matching

PeptideWitch is a python-based web module that introduces several key graphical and technical improvements to the Scrappy software platform, which is designed for label-free quantitative shotgun proteomics analysis using normalised spectral abundance factors. The program inputs are low stringency protein identification lists output from peptide-to-spectrum matching search engines for ‘control’ and ‘treated’ samples. Through a combination of spectral count summation and inner joins, PeptideWitch processes low stringency data, and outputs high stringency data that are suitable for downstream quantitation. Data quality metrics are generated, and a series of statistical analyses and graphical representations are presented, aimed at defining and presenting the difference between the two sample proteomes.

scholarly journals Proteomic profiling of archaeological human bone

2017 ◽  
Vol 4 (6) ◽  
pp. 161004 ◽  
Author(s):  
Rikai Sawafuji ◽  
Enrico Cappellini ◽  
Tomohito Nagaoka ◽  
Anna K. Fotakis ◽  
Rosa Rakownikow Jersie-Christensen ◽  
...  
Keyword(s):  
Human Life ◽  
Shotgun Proteomics ◽  
Label Free ◽  
Proteomic Profiling ◽  
Strong Negative Correlation ◽  
Age Dependent ◽  
Ancient Proteins ◽  
Ancient Times ◽  
First Time ◽  
Free Quantification

Ancient protein analysis provides clues to human life and diseases from ancient times. Here, we performed shotgun proteomics of human archeological bones for the first time, using rib bones from the Hitotsubashi site (AD 1657–1683) in Tokyo, called Edo in ancient times. The output data obtained were analysed using Gene Ontology and label-free quantification. We detected leucocyte-derived proteins, possibly originating from the bone marrow of the rib. Particularly prevalent and relatively high expression of eosinophil peroxidase suggests the influence of infectious diseases. This scenario is plausible, considering the overcrowding and unhygienic living conditions of the Edo city described in the historical literature. We also observed age-dependent differences in proteome profiles, particularly for proteins involved in developmental processes. Among them, alpha-2-HS-glycoprotein demonstrated a strong negative correlation with age. These results suggest that analysis of ancient proteins could provide a useful indicator of stress, disease, starvation, obesity and other kinds of physiological and pathological information.

scholarly journals Mass Dynamics 1.0: A streamlined, web-based environment for analyzing, sharing and integrating Label-Free Data

2021 ◽  
Author(s):  
Joseph Bloom ◽  
Aaron Triantafyllidis ◽  
Paula Burton (Ngov) ◽  
Giuseppe Infusini ◽  
Andrew Webb
Keyword(s):  
Dynamic Range ◽  
Shotgun Proteomics ◽  
Label Free ◽  
Proteomics Data ◽  
Web Based ◽  
Free Data ◽  
Benchmark Datasets ◽  
Free Quantification ◽  
Analysis Environment

AbstractLabel Free Quantification (LFQ) of shotgun proteomics data is a popular and robust method for the characterization of relative protein abundance between samples. Many analytical pipelines exist for the automation of this analysis and some tools exist for the subsequent representation and inspection of the results of these pipelines. Mass Dynamics 1.0 (MD 1.0) is a web based analysis environment that can analyze and visualize LFQ data produced by software such as Maxquant. Unlike other tools, MD 1.0 utilizes cloud-based architecture to enable researchers to store their data, enabling researchers to not only automatically process and visualize their LFQ data but annotate and share their findings with collaborators and, if chosen, to easily publish results to the community. With a view toward increased reproducibility and standardisation in proteomics data analysis and streamlining collaboration between researchers, MD 1.0 requires minimal parameter choices and automatically generates quality control reports to verify experiment integrity. Here, we demonstrate that MD 1.0 provides reliable results for protein expression quantification, emulating Perseus on benchmark datasets over a wide dynamic range.The MD 1.0 platform is available globally via: https://app.massdynamics.com/[email protected]

scholarly journals Comparison of Different Label-free Techniques for the Semi-absolute Quantification of Protein Abundance

2021 ◽  
Author(s):  
Aarón Millán-Oropeza ◽  
Mélisande Blein-Nicolas ◽  
Véronique Monnet ◽  
Michel Zivy ◽  
Céline Henry
Keyword(s):  
Shotgun Proteomics ◽  
Absolute Quantification ◽  
Biological Data ◽  
Protein Quantification ◽  
Protein Abundance ◽  
Label Free ◽  
Absolute Abundance ◽  
Yeast Saccharomyces Cerevisiae ◽  
Genome Scale ◽  
Free Quantification

In proteomics, it is essential to quantify proteins in absolute terms if we wish compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allows protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the &ldquo;gold standard&rdquo; for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.

Improving diagnosis of acute appendicitis with atypical findings by Tc-99m HMPAO leukocyte scan

2002 ◽  
Vol 41 (01) ◽  
pp. 37-41 ◽  
Author(s):  
S. Shung-Shung ◽  
S. Yu-Chien ◽  
Y. Mei-Due ◽  
W. Hwei-Chung ◽  
A. Kao
Keyword(s):  
Acute Appendicitis ◽  
False Positive ◽  
False Positive Rate ◽  
Accurate Method ◽  
Clinical Findings ◽  
Pathological Findings ◽  
Lower Quadrant ◽  
Predictive Values ◽  
Positive Rate

Summary Aim: Even with careful observation, the overall false-positive rate of laparotomy remains 10-15% when acute appendicitis was suspected. Therefore, the clinical efficacy of Tc-99m HMPAO labeled leukocyte (TC-WBC) scan for the diagnosis of acute appendicitis in patients presenting with atypical clinical findings is assessed. Patients and Methods: Eighty patients presenting with acute abdominal pain and possible acute appendicitis but atypical findings were included in this study. After intravenous injection of TC-WBC, serial anterior abdominal/pelvic images at 30, 60, 120 and 240 min with 800k counts were obtained with a gamma camera. Any abnormal localization of radioactivity in the right lower quadrant of the abdomen, equal to or greater than bone marrow activity, was considered as a positive scan. Results: 36 out of 49 patients showing positive TC-WBC scans received appendectomy. They all proved to have positive pathological findings. Five positive TC-WBC were not related to acute appendicitis, because of other pathological lesions. Eight patients were not operated and clinical follow-up after one month revealed no acute abdominal condition. Three of 31 patients with negative TC-WBC scans received appendectomy. They also presented positive pathological findings. The remaining 28 patients did not receive operations and revealed no evidence of appendicitis after at least one month of follow-up. The overall sensitivity, specificity, accuracy, positive and negative predictive values for TC-WBC scan to diagnose acute appendicitis were 92, 78, 86, 82, and 90%, respectively. Conclusion: TC-WBC scan provides a rapid and highly accurate method for the diagnosis of acute appendicitis in patients with equivocal clinical examination. It proved useful in reducing the false-positive rate of laparotomy and shortens the time necessary for clinical observation.

Predicting Fetal Chromosome Anomalies in the First Trimester Using Pregnancy Associated Plasma Protein-A: A Comparison of Statistical Methods

1993 ◽  
Vol 32 (02) ◽  
pp. 175-179 ◽  
Author(s):  
B. Brambati ◽  
T. Chard ◽  
J. G. Grudzinskas ◽  
M. C. M. Macintosh
Keyword(s):  
Logistic Regression ◽  
General Population ◽  
Likelihood Ratio ◽  
False Positive ◽  
False Positive Rate ◽  
Ratio Method ◽  
Detection Rates ◽  
Gaussian Distributions ◽  
Positive Rate ◽  
Likelihood Ratio Method

Abstract:The analysis of the clinical efficiency of a biochemical parameter in the prediction of chromosome anomalies is described, using a database of 475 cases including 30 abnormalities. A comparison was made of two different approaches to the statistical analysis: the use of Gaussian frequency distributions and likelihood ratios, and logistic regression. Both methods computed that for a 5% false-positive rate approximately 60% of anomalies are detected on the basis of maternal age and serum PAPP-A. The logistic regression analysis is appropriate where the outcome variable (chromosome anomaly) is binary and the detection rates refer to the original data only. The likelihood ratio method is used to predict the outcome in the general population. The latter method depends on the data or some transformation of the data fitting a known frequency distribution (Gaussian in this case). The precision of the predicted detection rates is limited by the small sample of abnormals (30 cases). Varying the means and standard deviations (to the limits of their 95% confidence intervals) of the fitted log Gaussian distributions resulted in a detection rate varying between 42% and 79% for a 5% false-positive rate. Thus, although the likelihood ratio method is potentially the better method in determining the usefulness of a test in the general population, larger numbers of abnormal cases are required to stabilise the means and standard deviations of the fitted log Gaussian distributions.

Identification of and Correction for Publication Bias: Comment

2019 ◽  
Author(s):  
Amanda Kvarven ◽  
Eirik Strømland ◽  
Magnus Johannesson
Keyword(s):  
Publication Bias ◽  
False Positive ◽  
Large Scale ◽  
Meta Analysis ◽  
False Positive Rate ◽  
Effect Sizes ◽  
Replication Studies ◽  
Moderate Reduction ◽  
Positive Rate ◽  
Meta Analyses

Andrews &amp; Kasy (2019) propose an approach for adjusting effect sizes in meta-analysis for publication bias. We use the Andrews-Kasy estimator to adjust the result of 15 meta-analyses and compare the adjusted results to 15 large-scale multiple labs replication studies estimating the same effects. The pre-registered replications provide precisely estimated effect sizes, which do not suffer from publication bias. The Andrews-Kasy approach leads to a moderate reduction of the inflated effect sizes in the meta-analyses. However, the approach still overestimates effect sizes by a factor of about two or more and has an estimated false positive rate of between 57% and 100%.

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