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.

Review of 12 months of copy number variant calling on a clinical next generation sequencing pipeline

Pathology ◽  
2020 ◽  
Vol 52 ◽  
pp. S108
Author(s):  
Dylan A. Mordaunt ◽  
Julien Soubrier ◽  
Song Gao ◽  
Lesley Rawlings ◽  
Jillian Nicholl ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Copy Number ◽  
Variant Calling ◽  
Copy Number Variant ◽  
Next Generation ◽  
Generation Sequencing

scholarly journals Detection of False-Positive Deletions from the Database of Genomic Variants

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Junbo Duan ◽  
Han Liu ◽  
Lanling Zhao ◽  
Xiguo Yuan ◽  
Yu-Ping Wang ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
False Positive ◽  
Gc Content ◽  
Variant Calling ◽  
Emerging Technology ◽  
False Positives ◽  
Read Mapping ◽  
Genomic Variants ◽  
Public Repositories ◽  
Generation Sequencing

Next generation sequencing is an emerging technology that has been widely used in the detection of genomic variants. However, since its depth of coverage, a main signature used for variant calling, is affected greatly by biases such as GC content and mappability, some callings are false positives. In this study, we utilized paired-end read mapping, another signature that is not affected by the aforementioned biases, to detect false-positive deletions in the database of genomic variants. We first identified 1923 suspicious variants that may be false positives and then conducted validation studies on each suspicious variant, which detected 583 false-positive deletions. Finally we analysed the distribution of these false positives by chromosome, sample, and size. Hopefully, incorrect documentation and annotations in downstream studies can be avoided by correcting these false positives in public repositories.

scholarly journals Validation of an ultra-fast CNV calling tool for Next Generation Sequencing data using MLPA-verified copy number alterations

2018 ◽  
Author(s):  
Bas Tolhuis ◽  
Hans Karten
Keyword(s):  
Next Generation Sequencing ◽  
Copy Number ◽  
False Positive Rate ◽  
Copy Number Variations ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Single Node ◽  
Generation Sequencing ◽  
Cnv Detection

AbstractDNA Copy Number Variations (CNVs) are an important source for genetic diversity and pathogenic variants. Next Generation Sequencing (NGS) methods have become increasingly more popular for CNV detection, but its data analysis is a growing bottleneck. Genalice CNV is a novel tool for detection of CNVs. It takes care of turnaround time, scalability and cost issues associated with NGS computational analysis. Here, we validate Genalice CNV with MLPA-verified exon CNVs and genes with normal copy numbers. Genalice CNV detects 61 out of 62 exon CNVs and its false positive rate is less than 1%. It analyzes 96 samples from a targeted NGS assay in less than 45 minutes, including read alignment and CNV detection, using a single node. Furthermore, we describe data quality measures to minimize false discoveries. In conclusion, Genalice CNV is highly sensitive and specific, as well as extremely fast, which will be beneficial for clinical detection of CNVs.

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.

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%.

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