SLiM-Enrich: computational assessment of protein–protein interaction data as a source of domain-motif interactions

Many important cellular processes involve protein–protein interactions (PPIs) mediated by a Short Linear Motif (SLiM) in one protein interacting with a globular domain in another. Despite their significance, these domain-motif interactions (DMIs) are typically low affinity, which makes them challenging to identify by classical experimental approaches, such as affinity pulldown mass spectrometry (AP-MS) and yeast two-hybrid (Y2H). DMIs are generally underrepresented in PPI networks as a result. A number of computational methods now exist to predict SLiMs and/or DMIs from experimental interaction data but it is yet to be established how effective different PPI detection methods are for capturing these low affinity SLiM-mediated interactions. Here, we introduce a new computational pipeline (SLiM-Enrich) to assess how well a given source of PPI data captures DMIs and thus, by inference, how useful that data should be for SLiM discovery. SLiM-Enrich interrogates a PPI network for pairs of interacting proteins in which the first protein is known or predicted to interact with the second protein via a DMI. Permutation tests compare the number of known/predicted DMIs to the expected distribution if the two sets of proteins are randomly associated. This provides an estimate of DMI enrichment within the data and the false positive rate for individual DMIs. As a case study, we detect significant DMI enrichment in a high-throughput Y2H human PPI study. SLiM-Enrich analysis supports Y2H data as a source of DMIs and highlights the high false positive rates associated with naïve DMI prediction. SLiM-Enrich is available as an R Shiny app. The code is open source and available via a GNU GPL v3 license at: https://github.com/slimsuite/SLiMEnrich. A web server is available at: http://shiny.slimsuite.unsw.edu.au/SLiMEnrich/.

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PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽

10.3390/electronics9111894 ◽

2020 ◽

Vol 9 (11) ◽

pp. 1894

Author(s):

Chun Guo ◽

Zihua Song ◽

Yuan Ping ◽

Guowei Shen ◽

Yuhei Cui ◽

...

Keyword(s):

False Positive ◽

Detection Method ◽

False Positive Rate ◽

True Positive Rate ◽

Remote Access ◽

Detection Methods ◽

Security Threats ◽

True Positive ◽

Trojan Detection ◽

Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

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XlinkCyNET: a Cytoscape application for visualization of protein interaction networks based on cross-linking mass-spectrometry identifications

10.1101/2020.12.20.423654 ◽

2020 ◽

Author(s):

Diogo Borges Lima ◽

Ying Zhu ◽

Fan Liu

Keyword(s):

Mass Spectrometry ◽

Protein Interaction ◽

Large Scale ◽

Interaction Network ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Cross Linking ◽

Protein Interaction Data ◽

Interaction Data ◽

Link Type

ABSTRACTSoftware tools that allow visualization and analysis of protein interaction networks are essential for studies in systems biology. One of the most popular network visualization tools in biology is Cytoscape, which offers a large selection of plugins for interpretation of protein interaction data. Chemical cross-linking coupled to mass spectrometry (XL-MS) is an increasingly important source for such interaction data, but there are currently no Cytoscape tools to analyze XL-MS results. In light of the suitability of Cytoscape platform but also to expand its toolbox, here we introduce XlinkCyNET, an open-source Cytoscape Java plugin for exploring large-scale XL-MS-based protein interaction networks. XlinkCyNET offers rapid and easy visualization of intra and intermolecular cross-links and the locations of protein domains in a rectangular bar style, allowing subdomain-level interrogation of the interaction network. XlinkCyNET is freely available from the Cytoscape app store: http://apps.cytoscape.org/apps/xlinkcynet and at https://www.theliulab.com/software/xlinkcynet.

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Identification of Influential Variants in Significant Aggregate Rare Variant Tests

Human Heredity ◽

10.1159/000513290 ◽

2021 ◽

pp. 1-13

Author(s):

Rachel Z. Blumhagen ◽

David A. Schwartz ◽

Carl D. Langefeld ◽

Tasha E. Fingerlin

Keyword(s):

Outlier Detection ◽

Rare Variant ◽

False Positive ◽

Rare Variants ◽

False Positive Rate ◽

Experimental Studies ◽

Detection Methods ◽

True Positive ◽

Adaptive Combination ◽

Positive Rate

Introduction: Studies that examine the role of rare variants in both simple and complex disease are increasingly common. Though the usual approach of testing rare variants in aggregate sets is more powerful than testing individual variants, it is of interest to identify the variants that are plausible drivers of the association. We present a novel method for prioritization of rare variants after a significant aggregate test by quantifying the influence of the variant on the aggregate test of association. Methods: In addition to providing a measure used to rank variants, we use outlier detection methods to present the computationally efficient Rare Variant Influential Filtering Tool (RIFT) to identify a subset of variants that influence the disease association. We evaluated several outlier detection methods that vary based on the underlying variance measure: interquartile range (Tukey fences), median absolute deviation, and SD. We performed 1,000 simulations for 50 regions of size 3 kb and compared the true and false positive rates. We compared RIFT using the Inner Tukey to 2 existing methods: adaptive combination of p values (ADA) and a Bayesian hierarchical model (BeviMed). Finally, we applied this method to data from our targeted resequencing study in idiopathic pulmonary fibrosis (IPF). Results: All outlier detection methods observed higher sensitivity to detect uncommon variants (0.001 < minor allele frequency, MAF > 0.03) compared to very rare variants (MAF <0.001). For uncommon variants, RIFT had a lower median false positive rate compared to the ADA. ADA and RIFT had significantly higher true positive rates than that observed for BeviMed. When applied to 2 regions found previously associated with IPF including 100 rare variants, we identified 6 polymorphisms with the greatest evidence for influencing the association with IPF. Discussion: In summary, RIFT has a high true positive rate while maintaining a low false positive rate for identifying polymorphisms influencing rare variant association tests. This work provides an approach to obtain greater resolution of the rare variant signals within significant aggregate sets; this information can provide an objective measure to prioritize variants for follow-up experimental studies and insight into the biological pathways involved.

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Incomplete and noisy network data as a percolation process

Journal of The Royal Society Interface ◽

10.1098/rsif.2010.0044 ◽

2010 ◽

Vol 7 (51) ◽

pp. 1411-1419 ◽

Cited By ~ 8

Author(s):

Michael P. H. Stumpf ◽

Carsten Wiuf

Keyword(s):

False Positive Rate ◽

Protein Interaction Data ◽

Network Data ◽

Interaction Data ◽

Connected Component ◽

Percolation Process ◽

Protein Protein Interaction ◽

Incomplete Observations ◽

Positive Rate ◽

Percolation Thresholds

We discuss the ramifications of noisy and incomplete observations of network data on the existence of a giant connected component (GCC). The existence of a GCC in a random graph can be described in terms of a percolation process, and building on general results for classes of random graphs with specified degree distributions we derive percolation thresholds above which GCCs exist. We show that sampling and noise can have a profound effect on the perceived existence of a GCC and find that both processes can destroy it. We also show that the absence of a GCC puts a theoretical upper bound on the false-positive rate and relate our percolation analysis to experimental protein–protein interaction data.

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Feasibility of using template-based and object-based automated detection methods for quantifying black and hybrid imported fire ant (Solenopsis invicta and S. invicta×richteri) mounds in aerial digital imagery

The Rangeland Journal ◽

10.1071/rj08007 ◽

2008 ◽

Vol 30 (3) ◽

pp. 291 ◽

Cited By ~ 3

Author(s):

James T. Vogt ◽

Bradley Wallet

Keyword(s):

False Positive ◽

False Positive Rate ◽

Bare Soil ◽

Automated Detection ◽

Probability Of Detection ◽

Fire Ant ◽

Detection Methods ◽

Object Based ◽

Positive Rate ◽

A Site

Imported fire ants construct earthen nests (mounds) that exhibit many characteristics which make them potentially good targets for remote sensing programs, including geographical orientation, topography, and bare soil surrounded by actively growing vegetation. Template-based features and object-based features extracted from aerial multispectral imagery of fire ant infested pastures were used to construct classifiers for automated fire ant mound detection. A classifier constructed using template-based features alone yielded a 79% probability of detection with a corresponding false positive rate of 9%. Addition of object-based features (compactness and symmetry) to the classifier yielded a 79% probability of detection with a corresponding false positive rate of 4%. Maintaining a 79% detection rate when applying the classifier to a second, unique pasture dataset with different seasonal and other environmental factors resulted in a false positive rate of 17.5%. Data demonstrate that automated detection of mounds with classifiers incorporating template- and object-based features is feasible, but it may be necessary to construct unique classifiers on a site-specific basis.

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Identification of Influential Variants in Significant Aggregate Rare Variant Tests

10.1101/2020.10.01.322644 ◽

2020 ◽

Author(s):

Rachel Z. Blumhagen ◽

David A. Schwartz ◽

Carl D. Langefeld ◽

Tasha E. Fingerlin

Keyword(s):

Outlier Detection ◽

Rare Variant ◽

False Positive ◽

Rare Variants ◽

False Positive Rate ◽

Experimental Studies ◽

Detection Methods ◽

True Positive ◽

Adaptive Combination ◽

Positive Rate

AbstractIntroductionStudies that examine the role of rare variants in both simple and complex disease are increasingly common. Though the usual approach of testing rare variants in aggregate sets is more powerful than testing individual variants, it is of interest to identify the variants that are plausible drivers of the association. We present a novel method for prioritization of rare variants after a significant aggregate test by quantifying the influence of the variant on the aggregate test of association.MethodsIn addition to providing a measure used to rank variants, we use outlier detection methods to present the computationally efficient Rare Variant Influential Filtering Tool (RIFT) to identify a subset of variants that influence the disease association. We evaluated several outlier detection methods that vary based on the underlying variance measure: interquartile range (Tukey fences), median absolute deviation and standard deviation. We performed 1000 simulations for 50 regions of size 3kb and compared the true and false positive rates. We compared RIFT using the Inner Tukey to two existing methods: adaptive combination of p-values (ADA) and a Bayesian hierarchical model (BeviMed). Finally, we applied this method to data from our targeted resequencing study in idiopathic pulmonary fibrosis (IPF).ResultsAll outlier detection methods observed higher sensitivity to detect uncommon variants (0.001 < MAF > 0.03) compared to very rare variants (MAF < 0.001). For uncommon variants, RIFT had a lower median false positive rate compared to the ADA. ADA and RIFT had significantly higher true positive rates than that observed for BeviMed. When applied to two regions found previously associated with IPF including 100 rare variants, we identified six polymorphisms with the greatest evidence for influencing the association with IPF.DiscussionIn summary, RIFT has a high true positive rate while maintaining a low false positive rate for identifying polymorphisms influencing rare variant association tests. This work provides an approach to obtain greater resolution of the rare variant signals within significant aggregate sets; this information can provide an objective measure to prioritize variants for follow-up experimental studies and insight into the biological pathways involved.

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

Nuklearmedizin ◽

10.1055/s-0038-1623994 ◽

2002 ◽

Vol 41 (01) ◽

pp. 37-41 ◽

Cited By ~ 3

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.

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

Methods of Information in Medicine ◽

10.1055/s-0038-1634910 ◽

1993 ◽

Vol 32 (02) ◽

pp. 175-179 ◽

Cited By ~ 7

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.

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Identification of and Correction for Publication Bias: Comment

10.31222/osf.io/dh87m ◽

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 & 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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Characteristics of false-positive alerts on transcranial motor evoked potential monitoring during pediatric scoliosis and adult spinal deformity surgery: an “anesthetic fade” phenomenon

Journal of Neurosurgery Spine ◽

10.3171/2019.9.spine19814 ◽

2020 ◽

Vol 32 (3) ◽

pp. 423-431 ◽

Cited By ~ 3

Author(s):

Hiroki Ushirozako ◽

Go Yoshida ◽

Tomohiko Hasegawa ◽

Yu Yamato ◽

Tatsuya Yasuda ◽

...

Keyword(s):

Spinal Deformity ◽

False Positive ◽

Adult Spinal Deformity ◽

False Positive Rate ◽

Multivariate Logistic Regression Analysis ◽

Motor Evoked Potential ◽

Scoliosis Surgery ◽

True Negative ◽

Duration Of Surgery ◽

Spinal Deformity Surgery

OBJECTIVETranscranial motor evoked potential (TcMEP) monitoring may be valuable for predicting postoperative neurological complications with a high sensitivity and specificity, but one of the most frequent problems is the high false-positive rate. The purpose of this study was to clarify the differences in the risk factors for false-positive TcMEP alerts seen when performing surgery in patients with pediatric scoliosis and adult spinal deformity and to identify a method to reduce the false-positive rate.METHODSThe authors retrospectively analyzed 393 patients (282 adult and 111 pediatric patients) who underwent TcMEP monitoring while under total intravenous anesthesia during spinal deformity surgery. They defined their cutoff (alert) point as a final TcMEP amplitude of ≤ 30% of the baseline amplitude. Patients with false-positive alerts were classified into one of two groups: a group with pediatric scoliosis and a group with adult spinal deformity.RESULTSThere were 14 cases of false-positive alerts (13%) during pediatric scoliosis surgery and 62 cases of false-positive alerts (22%) during adult spinal deformity surgery. Compared to the true-negative cases during adult spinal deformity surgery, the false-positive cases had a significantly longer duration of surgery and greater estimated blood loss (both p < 0.001). Compared to the true-negative cases during pediatric scoliosis surgery, the false-positive cases had received a significantly higher total fentanyl dose and a higher mean propofol dose (0.75 ± 0.32 mg vs 0.51 ± 0.18 mg [p = 0.014] and 5.6 ± 0.8 mg/kg/hr vs 5.0 ± 0.7 mg/kg/hr [p = 0.009], respectively). A multivariate logistic regression analysis revealed that the duration of surgery (1-hour difference: OR 1.701; 95% CI 1.364–2.120; p < 0.001) was independently associated with false-positive alerts during adult spinal deformity surgery. A multivariate logistic regression analysis revealed that the mean propofol dose (1-mg/kg/hr difference: OR 3.117; 95% CI 1.196–8.123; p = 0.020), the total fentanyl dose (0.05-mg difference; OR 1.270; 95% CI 1.078–1.497; p = 0.004), and the duration of surgery (1-hour difference: OR 2.685; 95% CI 1.131–6.377; p = 0.025) were independently associated with false-positive alerts during pediatric scoliosis surgery.CONCLUSIONSLonger duration of surgery and greater blood loss are more likely to result in false-positive alerts during adult spinal deformity surgery. In particular, anesthetic doses were associated with false-positive TcMEP alerts during pediatric scoliosis surgery. The authors believe that false-positive alerts during pediatric scoliosis surgery, in particular, are caused by “anesthetic fade.”

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