Influence of sequencing depth on the fidelity and sensitivity of 1%-5% low-frequency mutation detection and recommendation for standardization of sequencing depth

2021 ◽  
Author(s):  
Zhe Liu ◽  
Weijin Qiu ◽  
Shujin Fu ◽  
Xia Zhao ◽  
Jun Xia ◽  
...  
Keyword(s):  
Binomial Distribution ◽  
False Positive ◽  
Mutation Detection ◽  
Low Frequency ◽  
Fixed Number ◽  
Sequencing Depth ◽  
Equal Proportion ◽  
True Positive ◽  
Positive Probability ◽  
Single Nucleotide Variants

Sequencing depth has always played an important role in the accurate detection of low-frequency mutations. The increase of sequencing depth and the reasonable setting of threshold can maximize the probability of true positive mutation, or sensitivity. Here, we found that when the threshold was set as a fixed number of positive mutated reads, the probability of both true and false-positive mutations increased with depth. However, When the number of positive mutated reads increased in an equal proportion with depth (the threshold was transformed from a fixed number to a fixed percentage of mutated reads), the true positive probability still increased while false positive probability decreased. Through binomial distribution simulation and experimental test, it is found that the "fidelity" of detected-VAFs is the cause of this phenomenon. Firstly, we used the binomial distribution to construct a model that can easily calculate the relationship between sequencing depth and probability of true positive (or false positive), which can standardize the minimum sequencing depth for different low-frequency mutation detection. Then, the effect of sequencing depth on the fidelity of NA12878 with 3% mutation frequency and circulating tumor DNA (ctDNA of 1%, 3% and 5%) showed that the increase of sequencing depth reduced the fluctuation range of detected-VAFs around the expected VAFs, that is, the fidelity was improved. Finally, based on our experiment result, the consistency of single-nucleotide variants (SNVs) between paired FF and FFPE samples of mice increased with increasing depth, suggesting that increasing depth can improve the precision and sensitivity of low-frequency mutations.

scholarly journals Integration of intra-sample contextual error modeling for improved detection of somatic mutations from deep sequencing

2020 ◽  
Vol 6 (50) ◽  
pp. eabe3722
Author(s):  
Sagi Abelson ◽  
Andy G. X. Zeng ◽  
Ido Nofech-Mozes ◽  
Ting Ting Wang ◽  
Stanley W. K. Ng ◽  
...  
Keyword(s):  
Mutation Detection ◽  
Residual Disease ◽  
Low Frequency ◽  
High Specificity ◽  
Error Modeling ◽  
Superior Performance ◽  
Precision Oncology ◽  
Single Nucleotide Variants ◽  
Local Sequence ◽  
Error Suppression

Sensitive mutation detection by next-generation sequencing is critical for early cancer detection, monitoring minimal/measurable residual disease (MRD), and guiding precision oncology. Nevertheless, because of artifacts introduced during library preparation and sequencing, the detection of low-frequency variants at high specificity is problematic. Here, we present Espresso, an error suppression method that considers local sequence features to accurately detect single-nucleotide variants (SNVs). Compared to other advanced error suppression techniques, Espresso consistently demonstrated lower numbers of false-positive mutation calls and greater sensitivity. We demonstrated Espresso’s superior performance in detecting MRD in the peripheral blood of patients with acute myeloid leukemia (AML) throughout their treatment course. Furthermore, we showed that accurate mutation calling in a small number of informative genomic loci might provide a cost-efficient strategy for pragmatic risk prediction of AML development in healthy individuals. More broadly, we aim for Espresso to aid with accurate mutation detection in many other research and clinical settings.

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

Electronics ◽  
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.

scholarly journals scSNV: accurate dscRNA-seq SNV co-expression analysis using duplicate tag collapsing

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Gavin W. Wilson ◽  
Mathieu Derouet ◽  
Gail E. Darling ◽  
Jonathan C. Yeung
Keyword(s):  
Genetic Variants ◽  
False Positive ◽  
Variant Calling ◽  
Call Rate ◽  
Rna Seq ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Variant Call ◽  
Two Samples ◽  
Co Detection

AbstractIdentifying single nucleotide variants has become common practice for droplet-based single-cell RNA-seq experiments; however, presently, a pipeline does not exist to maximize variant calling accuracy. Furthermore, molecular duplicates generated in these experiments have not been utilized to optimally detect variant co-expression. Herein, we introduce scSNV designed from the ground up to “collapse” molecular duplicates and accurately identify variants and their co-expression. We demonstrate that scSNV is fast, with a reduced false-positive variant call rate, and enables the co-detection of genetic variants and A>G RNA edits across twenty-two samples.

scholarly journals A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller

2018 ◽  
Vol 10 (9) ◽  
pp. 83 ◽  
Author(s):  
Wentao Wang ◽  
Xuan Ke ◽  
Lingxia Wang
Keyword(s):  
Data Center ◽  
False Positive ◽  
Data Flow ◽  
Denial Of Service ◽  
True Positive ◽  
Ddos Attacks ◽  
Data Center Network ◽  
Data Packets ◽  
Positive Rate ◽  
Sdn Controller

A data center network is vulnerable to suffer from concealed low-rate distributed denial of service (L-DDoS) attacks because its data flow has the characteristics of data flow delay, diversity, and synchronization. Several studies have proposed addressing the detection of L-DDoS attacks, most of them are only detect L-DDoS attacks at a fixed rate. These methods cause low true positive and high false positive in detecting multi-rate L-DDoS attacks. Software defined network (SDN) is a new network architecture that can centrally control the network. We use an SDN controller to collect and analyze data packets entering the data center network and calculate the Renyi entropies base on IP of data packets, and then combine them with the hidden Markov model to get a probability model HMM-R to detect L-DDoS attacks at different rates. Compared with the four common attack detection algorithms (KNN, SVM, SOM, BP), HMM-R is superior to them in terms of the true positive rate, the false positive rate, and the adaptivity.

Evaluation of the Diagnostic Criteria for Anti-NMDA Receptor Encephalitis in Japanese Children

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000011789
Author(s):  
Hiroya NISHIDA ◽  
Kuniko KOHYAMA ◽  
Satoko KUMADA ◽  
Jun-ichi TAKANASHI ◽  
Akihisa OKUMURA ◽  
...  
Keyword(s):  
Nmda Receptor ◽  
Diagnostic Criteria ◽  
Predictive Value ◽  
False Positive ◽  
Pediatric Population ◽  
True Positive ◽  
Positive Group ◽  
Nmdar Encephalitis ◽  
Clinical Diagnostic ◽  
Clinical Diagnostic Criteria

OBJECTIVE:To evaluate the validity of the 2016 clinical diagnostic criteria proposed for probable anti-NMDA receptor (NMDAR) encephalitis in children, we tested the criteria in a Japanese pediatric cohort.METHODS:We retrospectively reviewed clinical information of patients with neurological symptoms whose CSF were analyzed for NMDAR antibodies (Abs) in our laboratory from January 1, 2015, to March 31, 2019.RESULTS:Overall, 137 cases were included. Of the 41 cases diagnosed as probable anti-NMDAR encephalitis (“criteria-positive”) according to the 2016 criteria, 13 were positive and 28 were negative for anti-NMDAR Abs. Of the 96 criteria-negative cases, three were positive and 93 were negative for anti-NMDAR Abs. The sensitivity of the criteria was 81.2%, specificity was 76.9%, positive predictive value (PPV) was 31.7%, and negative predictive value was 96.9%. Compared with the true-positive group, the false-positive group contained more male than female patients (male:female, 4:9 in the true-positive vs. 19:9 in the false-positive group, p = 0.0425). The majority of the cases with false-positive diagnoses were associated with neurological autoimmunity.CONCLUSION:The clinical diagnostic criteria are reliable for deciding to start immunomodulatory therapy in the criteria-positive cases. Low PPV may be caused by a lower prevalence of NMDAR encephalitis and/or lower level of suspicion for encephalitis in the pediatric population. Physicians should therefore continue differential diagnosis, focusing especially on other forms of encephalitis.Classification of Evidence:This study provides Class IV evidence that the proposed diagnostic criteria for anti-NMDAR encephalitis in children has a sensitivity of 81.2% and a specificity of 76.9%.

The performance of delta check methods.

1979 ◽  
Vol 25 (12) ◽  
pp. 2034-2037 ◽  
Author(s):  
L B Sheiner ◽  
L A Wheeler ◽  
J K Moore
Keyword(s):  
False Positive ◽  
False Positive Rate ◽  
True Positive Rate ◽  
True Positive ◽  
Discriminant Functions ◽  
Linear Discriminant ◽  
Operating Rate ◽  
Positive Rate ◽  
The Relationship

Abstract The percentage of mislabeled specimens detected (true-positive rate) and the percentage of correctly labeled specimens misidentified (false-positive rate) were computed for three previously proposed delta check methods and two linear discriminant functions. The true-positive rate was computed from a set of pairs of specimens, each having one member replaced by a member from another pair chosen at random. The relationship between true-positive and false-positive rates was similar among the delta check methods tested, indicating equal performance for all of them over the range of false-positive rate of interest. At a practical false-positive operating rate of about 5%, delta check methods detect only about 50% of mislabeled specimens; even if the actual mislabeling rate is moderate (e.g., 1%), only abot 10% of specimens flagged a by a delta check will actually have been mislabeled.

scholarly journals True and false positive rates for different criteria of evaluating statistical evidence from clinical trials

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Don van Ravenzwaaij ◽  
John P. A. Ioannidis
Keyword(s):  
Clinical Trials ◽  
Confidence Intervals ◽  
False Positive ◽  
Evaluation Criteria ◽  
Small Sample ◽  
Statistical Evidence ◽  
Bayes Factors ◽  
True Positive ◽  
Small Sample Sizes ◽  
Meta Analyses

Abstract Background Until recently a typical rule that has often been used for the endorsement of new medications by the Food and Drug Administration has been the existence of at least two statistically significant clinical trials favoring the new medication. This rule has consequences for the true positive (endorsement of an effective treatment) and false positive rates (endorsement of an ineffective treatment). Methods In this paper, we compare true positive and false positive rates for different evaluation criteria through simulations that rely on (1) conventional p-values; (2) confidence intervals based on meta-analyses assuming fixed or random effects; and (3) Bayes factors. We varied threshold levels for statistical evidence, thresholds for what constitutes a clinically meaningful treatment effect, and number of trials conducted. Results Our results show that Bayes factors, meta-analytic confidence intervals, and p-values often have similar performance. Bayes factors may perform better when the number of trials conducted is high and when trials have small sample sizes and clinically meaningful effects are not small, particularly in fields where the number of non-zero effects is relatively large. Conclusions Thinking about realistic effect sizes in conjunction with desirable levels of statistical evidence, as well as quantifying statistical evidence with Bayes factors may help improve decision-making in some circumstances.

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