scholarly journals Comparing multi- and single-sample variant calls to improve variant call sets from deep coverage whole-genome sequencing data

2016 ◽  
Author(s):  
Suyash S. Shringarpure ◽  
Rasika A. Mathias ◽  
Ryan D. Hernandez ◽  
Timothy D. O’Connor ◽  
Zachary A. Szpiech ◽  
...  
Keyword(s):  
False Positive ◽  
African Ancestry ◽  
False Positive Rate ◽  
Variant Calling ◽  
Single Sample ◽  
Whole Genome Sequencing Data ◽  
Sequencing Data ◽  
Public Data ◽  
Positive Rate ◽  
Ngs Data

ABSTRACTMotivationVariant calling from next-generation sequencing (NGS) data is susceptible to false positive calls due to sequencing, mapping and other errors. To better distinguish true from false positive calls, we present a method that uses genotype array data from the sequenced samples, rather than public data such as HapMap or dbSNP, to train an accurate classifier using Random Forests. We demonstrate our method on a set of variant calls obtained from 642 African-ancestry genomes from the The Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA), sequenced to high depth (30X).ResultsWe have applied our classifier to compare call sets generated with different calling methods, including both single-sample and multi-sample callers. At a False Positive Rate of 5%, our method determines true positive rates of 97.5%, 95% and 99% on variant calls obtained using Illumina’s single-sample caller CASAVA, Real Time Genomics’ multisample variant caller, and the GATK Unified Genotyper, respectively. Since most NGS sequencing data is accompanied by genotype data for the same samples, our method can be trained on each dataset to provide a more accurate computational validation of site calls compared to generic methods. Moreover, our method allows for adjustment based on allele frequency (e.g., a different set of criteria to determine quality for rare vs. common variants) and thereby provides insight into sequencing characteristics that indicate data quality for variants of different frequencies.AvailabilityCode will be made available prior to publication on Github.

scholarly journals VariFAST: a variant filter by automated scoring based on tagged-signatures

2019 ◽  
Vol 20 (S22) ◽  
Author(s):  
Hang Zhang ◽  
Ke Wang ◽  
Juan Zhou ◽  
Jianhua Chen ◽  
Yizhou Xu ◽  
...  
Keyword(s):  
Best Practices ◽  
False Positive ◽  
Variant Calling ◽  
Sequencing Data ◽  
Novel Approach ◽  
Variant Filtering ◽  
Ngs Data Analysis ◽  
High Consistency ◽  
Ngs Data ◽  
Manual Review

Abstract Background Variant calling and refinement from whole genome/exome sequencing data is a fundamental task for genomics studies. Due to the limited accuracy of NGS sequencing and variant callers, IGV-based manual review is required for further false positive variant filtering, which costs massive labor and time, and results in high inter- and intra-lab variability. Results To overcome the limitation of manual review, we developed a novel approach for Variant Filter by Automated Scoring based on Tagged-signature (VariFAST), and also provided a pipeline integrating GATK Best Practices with VariFAST, which can be easily used for high quality variants detection from raw data. Using the bam and vcf files, VariFAST calculates a v-score by sum of weighted metrics causing false positive variations, and marks tags in the manner of keeping high consistency with manual review, for each variant. We validated the performance of VariFAST for germline variant filtering using the benchmark sequencing data from GIAB, and also for somatic variant filtering using sequencing data of both malignant carcinoma and benign adenomas as well. VariFAST also includes a predictive model trained by XGBOOST algorithm for germline variants refinement, which reveals better MCC and AUC than the state-of-the-art VQSR, especially outcompete in INDEL variant filtering. Conclusion VariFAST can assist researchers efficiently and conveniently to filter the false positive variants, including both germline and somatic ones, in NGS data analysis. The VariFAST source code and the pipeline integrating with GATK Best Practices are available at https://github.com/bioxsjtu/VariFAST.

scholarly journals Investigating the Evolutionary Importance of Denisovan Introgressions in Papua New Guineans and Australians

2015 ◽  
Author(s):  
Ya Hu ◽  
Qiliang Ding ◽  
Yi Wang ◽  
Shuhua Xu ◽  
Yungang He ◽  
...  
Keyword(s):  
False Positive Rate ◽  
Whole Genome Sequencing Data ◽  
Phase Method ◽  
Sequencing Data ◽  
Male Gonad ◽  
Two Phase ◽  
Genome Wide ◽  
A Genome ◽  
Positive Rate ◽  
Olfactory Pit

Previous research reported that Papua New Guineans (PNG) and Australians contain introgressions from Denisovans. Here we present a genome-wide analysis of Denisovan introgressions in PNG and Australians. We firstly developed a two-phase method to detect Denisovan introgressions from whole-genome sequencing data. This method has relatively high detection power (79.74%) and low false positive rate (2.44%) based on simulations. Using this method, we identified 1.34 Gb of Denisovan introgressions from sixteen PNG and four Australian genomes, in which we identified 38,877 Denisovan introgressive alleles (DIAs). We found that 78 Denisovan introgressions were under positive selection. Genes located in the 78 introgressions are related to evolutionarily important functions, such as spermatogenesis, fertilization, cold acclimation, circadian rhythm, development of brain, neural tube, face, and olfactory pit, immunity, etc. We also found that 121 DIAs are missense. Genes harboring the 121 missense DIAs are also related to evolutionarily important functions, such as female pregnancy, development of face, lung, heart, skin, nervous system, and male gonad, visual and smell perception, response to heat, pain, hypoxia, and UV, lipid transport, metabolism, blood coagulation, wound healing, aging, etc. Taken together, this study suggests that Denisovan introgressions in PNG and Australians are evolutionarily important, and may help PNG and Australians in local adaptation. In this study, we also proposed a method that could efficiently identify archaic hominin introgressions in modern non-African genomes.

scholarly journals Same-species Contamination Detection With Variant Calling Information From Next-generation Sequencing

2021 ◽  
Author(s):  
Tao Jiang ◽  
Martin Buchkovich ◽  
Alison Motsinger-Reif
Keyword(s):  
Quality Control ◽  
Next Generation Sequencing ◽  
False Positive ◽  
Copy Number ◽  
False Positive Rate ◽  
Variant Calling ◽  
Degraded Samples ◽  
Svm Model ◽  
Positive Rate ◽  
Generation Sequencing

Abstract Background: Same-species contamination detection is an important quality control step in genetic data analysis. Due to a scarcity of methods to detect and correct for this quality control issue, same-species contamination is more difficult to detect than cross-species contamination. We introduce a novel machine learning algorithm to detect same-species contamination in next-generation sequencing (NGS) data using a support vector machine (SVM) model. Our approach uniquely detects contamination using variant calling information stored in variant call format (VCF) files for DNA or RNA. Importantly, it can differentiate between same-species contamination and mixtures of tumor and normal cells.In the first stage, a change-point detection method is used to identify copy number variations (CNVs) and copy number aberrations (CNAs) for filtering. Next, single nucleotide polymorphism (SNP) data is used to test for same-species contamination using an SVM model. Based on the assumption that alternative allele frequencies in NGS follow the beta-binomial distribution, the deviation parameter ρ is estimated by the maximum likelihood method. All features of a radial basis function (RBF) kernel SVM are generated using publicly available or private training data. Results: We demonstrate our approach in simulation experiments. The datasets combine, in silico, exome sequencing data of DNA from two lymphoblastoid cell lines (NA12878 and NA10855). We generate VCF files using variants identified in these data and then evaluate the power and false-positive rate of our approach. Our approach can detect contamination levels as low as 5% with a reasonable false-positive rate. Results in real data have sensitivity above 99.99% and specificity of 90.24%, even in the presence of degraded samples with similar features as contaminated samples. We provide an R software implementation of our approach.Conclusions: Our approach addresses the gap in methods to test for same-species contamination in NGS. Due to its high sensitivity for degraded samples and tumor-normal samples, it represents an important tool that can be applied within the quality control process. Additionally, the user-friendly software has the unique ability to conduct quality control using the VCF format.

scholarly journals ClinSV: Clinical grade structural and copy number variant detection from whole genome sequencing data

2020 ◽  
Author(s):  
Andre E Minoche ◽  
Ben Lundie ◽  
Greg B Peters ◽  
Thomas Ohnesorg ◽  
Mark Pinese ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
False Positive ◽  
Copy Number ◽  
False Positive Rate ◽  
Copy Number Variants ◽  
Copy Number Variant ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data

AbstractWhole genome sequencing (WGS) has the potential to outperform clinical microarrays for the detection of structural variants (SV) including copy number variants (CNVs), but has been challenged by high false positive rates. Here we present ClinSV, a WGS based SV integration, annotation, prioritisation and visualisation method, which identified 99.8% of pathogenic ClinVar CNVs >10kb and 11/11 pathogenic variants from matched microarrays. The false positive rate was low (1.5–4.5%) and reproducibility high (95–99%). In clinical practice, ClinSV identified reportable variants in 22 of 485 patients (4.7%) of which 35–63% were not detectable by current clinical microarray designs.

scholarly journals Filtering de novo indels in parent-offspring trios

2020 ◽  
Vol 21 (S16) ◽  
Author(s):  
Yongzhuang Liu ◽  
Jian Liu ◽  
Yadong Wang
Keyword(s):  
False Positive ◽  
De Novo ◽  
False Positive Rate ◽  
Gradient Boosting ◽  
Whole Genome ◽  
Sequencing Data ◽  
Clinical Practices ◽  
Computational Approaches ◽  
Exome Sequencing Data ◽  
Positive Rate

Abstract Background Identification of de novo indels from whole genome or exome sequencing data of parent-offspring trios is a challenging task in human disease studies and clinical practices. Existing computational approaches usually yield high false positive rate. Results In this study, we developed a gradient boosting approach for filtering de novo indels obtained by any computational approaches. Through application on the real genome sequencing data, our approach showed it could significantly reduce the false positive rate of de novo indels without a significant compromise on sensitivity. Conclusions The software DNMFilter_Indel was written in a combination of Java and R and freely available from the website at https://github.com/yongzhuang/DNMFilter_Indel.

scholarly journals ClinSV: clinical grade structural and copy number variant detection from whole genome sequencing data

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Andre E. Minoche ◽  
Ben Lundie ◽  
Greg B. Peters ◽  
Thomas Ohnesorg ◽  
Mark Pinese ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
False Positive ◽  
Copy Number ◽  
False Positive Rate ◽  
Copy Number Variants ◽  
Copy Number Variant ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data

AbstractWhole genome sequencing (WGS) has the potential to outperform clinical microarrays for the detection of structural variants (SV) including copy number variants (CNVs), but has been challenged by high false positive rates. Here we present ClinSV, a WGS based SV integration, annotation, prioritization, and visualization framework, which identified 99.8% of simulated pathogenic ClinVar CNVs > 10 kb and 11/11 pathogenic variants from matched microarrays. The false positive rate was low (1.5–4.5%) and reproducibility high (95–99%). In clinical practice, ClinSV identified reportable variants in 22 of 485 patients (4.7%) of which 35–63% were not detectable by current clinical microarray designs. ClinSV is available at https://github.com/KCCG/ClinSV.

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 & 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%.

The false positive rate of P300-based concealed information test

2018 ◽  
Vol 30 (3) ◽  
pp. 241-259 ◽  
Author(s):  
엄진섭 ◽  
Jin-Hun Sohn ◽  
Hajung Jeon
Keyword(s):  
False Positive ◽  
False Positive Rate ◽  
Concealed Information Test ◽  
Positive Rate ◽  
Concealed Information ◽  
Information Test
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