Integrated Proteomic Pipeline Using Multiple Search Engines for a Proteogenomic Study with a Controlled Protein False Discovery Rate

2016 ◽  
Vol 15 (11) ◽  
pp. 4082-4090 ◽  
Author(s):  
Gun Wook Park ◽  
Heeyoun Hwang ◽  
Kwang Hoe Kim ◽  
Ju Yeon Lee ◽  
Hyun Kyoung Lee ◽  
...  
Keyword(s):  
False Discovery Rate ◽  
Search Engines ◽  
False Discovery

scholarly journals A synthetic peptide library for benchmarking crosslinking mass spectrometry search engines

2019 ◽  
Author(s):  
Rebecca Beveridge ◽  
Johannes Stadlmann ◽  
Josef M. Penninger ◽  
Karl Mechtler
Keyword(s):  
Mass Spectrometry ◽  
False Discovery Rate ◽  
Search Engines ◽  
Synthetic Peptide ◽  
External Validation ◽  
Data Interpretation ◽  
Peptide Libraries ◽  
Mass Spectrometry Data ◽  
Biological Interactions ◽  
False Discovery

We have created synthetic peptide libraries to benchmark crosslinking mass spectrometry search engines for different types of crosslinker. The unique benefit of using a library is knowing which identified crosslinks are true and which are false. Here we have used mass spectrometry data generated from measurement of the peptide libraries to evaluate the most frequently applied search algorithms in crosslinking mass-spectrometry. When filtered to an estimated false discovery rate of 5%, false crosslink identification ranged from 5.2% to 11.3% for search engines with inbuilt validation strategies for error estimation. When different external validation strategies were applied to one single search output, false crosslink identification ranged from 2.4% to a surprising 32%, despite being filtered to an estimated 5% false discovery rate. Remarkably, the use of MS-cleavable crosslinkers did not reduce the false discovery rate compared to non-cleavable crosslinkers, results from which have far-reaching implications in structural biology. We anticipate that the datasets acquired during this research will further drive optimisation and development of search engines and novel data-interpretation technologies, thereby advancing our understanding of vital biological interactions.

scholarly journals False Discovery Rate in Linkage and Association Genome Screens for Complex Disorders

Genetics ◽  
2003 ◽  
Vol 164 (2) ◽  
pp. 829-833
Author(s):  
Chiara Sabatti ◽  
Susan Service ◽  
Nelson Freimer
Keyword(s):  
Gene Mapping ◽  
False Discovery Rate ◽  
Disease Gene ◽  
Susceptibility Genes ◽  
Complex Disorders ◽  
Disease Gene Mapping ◽  
False Discovery ◽  
Simple Step ◽  
Multiple Comparison Procedure ◽  
Step Down

Abstract We explore the implications of the false discovery rate (FDR) controlling procedure in disease gene mapping. With the aid of simulations, we show how, under models commonly used, the simple step-down procedure introduced by Benjamini and Hochberg controls the FDR for the dependent tests on which linkage and association genome screens are based. This adaptive multiple comparison procedure may offer an important tool for mapping susceptibility genes for complex diseases.

scholarly journals P14.21 Tehila Kaisman-Elbaz MD/PhD

2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii71-iii71
Author(s):  
T Kaisman-Elbaz ◽  
Y Elbaz ◽  
V Merkin ◽  
L Dym ◽  
A Noy ◽  
...  
Keyword(s):  
Overall Survival ◽  
Multivariate Analysis ◽  
False Discovery Rate ◽  
Medical Center ◽  
Tumor Resection ◽  
Multiple Hypothesis Testing ◽  
Hemoglobin Level ◽  
Distribution Width ◽  
False Discovery ◽  
Dismal Prognosis

Abstract BACKGROUND Glioblastoma is known for its dismal prognosis though its dependency on patients’ readily available RBCs parameters defining the patient’s anemic status such as hemoglobin level and Red blood cells distribution Width (RDW) is not fully established. Several works demonstrated a connection between low hemoglobin level or high RDW values to overall glioblastoma patient’s survival, but in other works, a clear connection was not found. This study addresses this unclarity. MATERIAL AND METHODS In this work, 170 glioblastoma patients, diagnosed and treated in Soroka University Medical Center (SUMC) in the last 12 years were retrospectively inspected for their survival dependency on pre-operative RBCs parameters using multivariate analysis followed by false discovery rate procedure due to the multiple hypothesis testing. A survival stratification tree and Kaplan-Meier survival curves that indicate the patient’s prognosis according to these parameters were prepared. RESULTS Beside KPS>70 and tumor resection supplemented by oncological treatment, age<70 (HR=0.4, 95% CI 0.24–0.65), low hemoglobin level (HR=1.79, 95% CI 1.06–2.99) and RDW<14% (HR=0.57, 95% CI 0.37–0.88) were found to be prognostic to patients’ overall survival in multivariate analysis, accounting for false discovery rate of less than 5%. CONCLUSION A survival stratification highlighted a non-anemic subgroup of nearly 30% of the cohort’s patients whose median overall survival was 21.1 months (95% CI 16.2–27.2) - higher than the average Stupp protocol overall median survival of about 15 months. A discussion on the beneficial or detrimental effect of RBCs parameters on glioblastoma prognosis and its possible causes is given.

scholarly journals Associations Between Genetically Predicted Protein Levels and COVID-19 Severity

2020 ◽  
Vol 223 (1) ◽  
pp. 19-22
Author(s):  
Jingjing Zhu ◽  
Chong Wu ◽  
Lang Wu
Keyword(s):  
False Discovery Rate ◽  
Drug Repurposing ◽  
Bonferroni Correction ◽  
Host Genetics ◽  
Protein Levels ◽  
False Discovery

Abstract It is critical to identify potential causal targets for SARS-CoV-2, which may guide drug repurposing options. We assessed the associations between genetically predicted protein levels and COVID-19 severity. Leveraging data from the COVID-19 Host Genetics Initiative comparing 6492 hospitalized COVID-19 patients and 1 012 809 controls, we identified 18 proteins with genetically predicted levels to be associated with COVID-19 severity at a false discovery rate of &lt;0.05, including 12 that showed an association even after Bonferroni correction. Of the 18 proteins, 6 showed positive associations and 12 showed inverse associations. In conclusion, we identified 18 candidate proteins for COVID-19 severity.

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