Exome Datasets Research Articles

VCF2CNA: A tool for efficiently detecting copy-number alterations in VCF genotype data and tumor purity

VCF2CNA is a tool (Linux commandline or web-interface) for copy-number alteration (CNA) analysis and tumor purity estimation of paired tumor-normal VCF variant file formats. It operates on whole genome and whole exome datasets. To benchmark its performance, we applied it to 46 adult glioblastoma and 146 pediatric neuroblastoma samples sequenced by Illumina and Complete Genomics (CGI) platforms respectively. VCF2CNA was highly consistent with a state-of-the-art algorithm using raw sequencing data (mean F1-score = 0.994) in high-quality whole genome glioblastoma samples and was robust to uneven coverage introduced by library artifacts. In the whole genome neuroblastoma set, VCF2CNA identified MYCN high-level amplifications in 31 of 32 clinically validated samples compared to 15 found by CGI’s HMM-based CNA model. Moreover, VCF2CNA achieved highly consistent CNA profiles between WGS and WXS platforms (mean F1 score 0.97 on a set of 15 rhabdomyosarcoma samples). In addition, VCF2CNA provides accurate tumor purity estimates for samples with sufficient CNAs. These results suggest that VCF2CNA is an accurate, efficient and platform-independent tool for CNA and tumor purity analyses without accessing raw sequence data.

Proteomic genotyping of fingermark donors with genetically variant peptides

Proteomic genotyping detects single amino acid polymorphisms to infer the genotype of corresponding non-synonymous SNPs. Like any DNA genotype, these inferences can be used to estimate random match probability. Fingermarks are a common source of biological evidence that is sample limited and a highly variable source of identifying DNA. Genetically variant peptides from fingermarks, that contain single amino acid polymorphisms, are an additional source of identifying genetic information. To discover these peptide biomarkers epidermal corneocytes from 9 subjects were isolated, processed, digested with trypsin and applied to mass spectrometry. The resulting proteomic and matching exome datasets were used to discover, characterize and validate 60 genetically variant peptides. An average of 28.8 ± 4.4 genetically variant peptides were detected from each subject resulting in a total of 264 SNP allele inferences with 260 true and 4 false positives, a false discovery rate of 1.5%. Random match probabilities were estimated using the genotype frequencies from the matching major populations in the 1000 Genomes Project. Estimates ranged up to a value of 1 in 1.7 × 108, with a median probability of 1 in 2.4 × 106. Furthermore, the proteomically-inferred genotypes are likely to be compatible with the STR-based random match probability estimates since the closest STR locus was 2.2 Mb from the nearest GVP-inferred SNP. This project represents a novel mode of genetic information that can be obtained from fingermarks and has the potential to complement other methods of human identification including analysis of ridge patterns or touch DNA.

Abstract P5-10-04: Exome sequencing in high breast and ovarian cancer incidence families which lack detectable mutations in established cancer susceptibility genes

Abstract At our center, a standard 25 gene panel has proved useful for the identification of pathogenic germline mutations in many families with a high penetrance of breast and ovarian cancer, yet there remain several families with very high cancer incidence but from whom these assays have not identified pathogenic alleles. To attempt to define the genetic mechanism of susceptibility in such cases, are conducting germline whole exome sequencing in affected individuals. Here we describe the identification of strong candidate susceptibility variants in two such families. Both variants cause loss-of-function of DNA repair genes not previously implicated in breast cancer, but which share properties with genes with known roles in maintenance of genome integrity. In one individual, with a personal history of ovarian cancer and two independent breast tumors, we identified a novel point mutation in POLD2 which introduces a mutation at a residue which is biochemically conserved in all animal species thus far sequenced. In the second individual, a frameshift mutation was detected in ERCC4. In both cases, the variants found have not been observed in large exome datasets (e.g. ExAC) or reported in ClinVar. Our study highlights the potential utility of whole exome sequencing as a discovery tool in those families with high cancer incidence yet which lack mutations in known hereditary cancer predisposition genes. Citation Format: Kenny PA, Girtman MA, Deviley JA, Meier DR, Vos CR, Richmond CS, Lofgren KA. Exome sequencing in high breast and ovarian cancer incidence families which lack detectable mutations in established cancer susceptibility genes [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P5-10-04.

Evaluation of whole exome sequencing as an alternative to BeadChip and whole genome sequencing in human population genetic analysis

BackgroundUnderstanding the underlying genetic structure of human populations is of fundamental interest to both biological and social sciences. Advances in high-throughput genotyping technology have markedly improved our understanding of global patterns of human genetic variation. The most widely used methods for collecting variant information at the DNA-level include whole genome sequencing, which remains costly, and the more economical solution of array-based techniques, as these are capable of simultaneously genotyping a pre-selected set of variable DNA sites in the human genome. The largest publicly accessible set of human genomic sequence data available today originates from exome sequencing that comprises around 1.2% of the whole genome (approximately 30 million base pairs).ResultsTo unbiasedly compare the effect of SNP selection strategies in population genetic analysis we subsampled the variants of the same highly curated 1 K Genome dataset to mimic genome, exome sequencing and array data in order to eliminate the effect of different chemistry and error profiles of these different approaches. Next we compared the application of the exome dataset to the array-based dataset and to the gold standard whole genome dataset using the same population genetic analysis methods.ConclusionsOur results draw attention to some of the inherent problems that arise from using pre-selected SNP sets for population genetic analysis. Additionally, we demonstrate that exome sequencing provides a better alternative to the array-based methods for population genetic analysis. In this study, we propose a strategy for unbiased variant collection from exome data and offer a bioinformatics protocol for proper data processing.

Enabling genome-wide association testing with multiple diseases and no healthy controls

MotivationWhile large-scale whole genome sequencing is feasible the high costs compel investigators to focus on disease subjects. As a result large sequencing datasets of samples with different diseases are often readily available, but not healthy controls to contrast them with. While it is possible to perform an association study using only diseases, the associations could be driven by a disease acting as a control and not the focal disease. MethodsWe developed a genotype-on-phenotype reverse regression with a Bayesian spike and slab prior to enable association testing in datasets with multiple diseases. This method, referred to as revreg, flagged associations (both common and rare) that were driven by diseases that were not of primary interest. ResultsBased on simulations, revreg had 80% power to detect an odds ratio of 1.74 for common variants (3500 samples total) and 3.73 for rare variants (14,000 samples total), with minimal type I error. For common variants, we tested this method on 3657 whole genome sequenced samples aimed at discovering variants associated with disease risk of Chronic Obstructive Pulmonary Disease using three other diseases as controls. We demonstrated detection of six highly significant associations likely due to Age-Related Macular Degeneration. In an exome dataset of 8836 samples aimed at characterizing rare variants associated with disease risk of Asthma, using five other diseases as controls, we detected and removed genic regions due to AMD (C3, CFH, CFHR5, CFI, and DNMT3A) and RA (KRTAP13-4).

MACARON: a python framework to identify and re-annotate multi-base affected codons in whole genome/exome sequence data.

Predicted deleteriousness of coding variants is a frequently used criterion to filter out variants detected in next-generation sequencing projects and to select candidates impacting on the risk of human diseases. Most available dedicated tools implement a base-to-base annotation approach that could be biased in presence of several variants in the same genetic codon. We here proposed the MACARON program that, from a standard VCF file, identifies, re-annotates and predicts the amino acid change resulting from multiple single nucleotide variants (SNVs) within the same genetic codon. Applied to the whole exome dataset of 573 individuals, MACARON identifies 114 situations where multiple SNVs within a genetic codon induce an amino acid change that is different from those predicted by standard single SNV annotation tool. Such events are not uncommon and deserve to be studied in sequencing projects with inconclusive findings. MACARON is written in python with codes available on the GENMED website (www.genmed.fr). Supplementary data are available at Bioinformatics online.

Deep learning of genomic variation and regulatory network data.

The human genome is now investigated through high-throughput functional assays, and through the generation of population genomic data. These advances support the identification of functional genetic variants and the prediction of traits (e.g. deleterious variants and disease). This review summarizes lessons learned from the large-scale analyses of genome and exome data sets, modeling of population data and machine-learning strategies to solve complex genomic sequence regions. The review also portrays the rapid adoption of artificial intelligence/deep neural networks in genomics; in particular, deep learning approaches are well suited to model the complex dependencies in the regulatory landscape of the genome, and to provide predictors for genetic variant calling and interpretation.

Incidental and clinically actionable genetic variants in 1005 whole exomes and genomes from Qatar.

Incidental findings in genomic data have been studied in great detail in the recent years, especially from population-scale data sets. However, little is known about the frequency of such findings in ethnic groups, specifically the Middle East, which were not previously covered in global sequencing studies. The availability of whole exome and genome data sets for a highly consanguineous Arab population from Qatar motivated us to explore the incidental findings in this population-scale data. The sequence data of 1005 Qatari individuals were systematically analyzed for incidental genetic variants in the 59 genes suggested by the American College of Medical Genetics and Genomics. We identified four genetic variants which were pathogenic or likely pathogenic. These variants occurred in six individuals, suggesting a frequency of 0.59% in the population, much lesser than that previously reported from European and African populations. Our analysis identified a variant in RYR1 gene associated with Malignant Hyperthermia that has significantly higher frequency in the population compared to global frequencies. Evaluation of the allele frequencies of these variants suggested enrichment in sub-populations, especially in individuals of Sub-Saharan African ancestry. The present study thereby provides the information on pathogenicity and frequency, which could aid in genomic medicine. To the best of our knowledge, this is the first comprehensive analysis of incidental genetic findings in any Arab population and suggests ethnic differences in incidental findings.

Nimbus: a design-driven analyses suite for amplicon-based NGS data.

MotivationPCR-based DNA enrichment followed by massively parallel sequencing is a straightforward and cost effective method to sequence genes up to high depth. The full potential of amplicon-based sequencing assays is currently not achieved as analysis methods do not take into account the source amplicons of the detected variants. Tracking the source amplicons has the potential to identify systematic biases, enhance variant calling and improve the designs of future assays.ResultsWe present Nimbus, a software suite for the analysis of amplicon-based sequencing data. Nimbus includes tools for data pre-processing, alignment, single nucleotide polymorphism (SNP), insertion and deletion calling, quality control and visualization. Nimbus can detect SNPs in its alignment seeds and reduces alignment issues by the usage of decoy amplicons. Tracking the amplicons throughout analysis allows easy and fast design optimization by amplicon performance comparison. It enables detection of probable false positive variants present in a single amplicon from real variants present in multiple amplicons and provides multiple sample visualization. Nimbus was tested using HaloPlex Exome datasets and outperforms other callers for low-frequency variants. The variants called by Nimbus were highly concordant between twin samples and SNP-arrays. The Nimbus suite provides an end-to-end solution for variant calling, design optimization and visualization of amplicon-derived next-generation sequencing datasets.Availability and implementation https://github.com/erasmus-center-for-biomics/Nimbus.Supplementary information Supplementary data are available at Bioinformatics online.

Large-scale exome datasets reveal a new class of adaptor-related protein complex 2 sigma subunit (AP2σ) mutations, located at the interface with the AP2 alpha subunit, that impair calcium-sensing receptor signalling.

Mutations of the sigma subunit of the heterotetrameric adaptor-related protein complex 2 (AP2σ) impair signalling of the calcium-sensing receptor (CaSR), and cause familial hypocalciuric hypercalcaemia type 3 (FHH3). To date, FHH3-associated AP2σ mutations have only been identified at one residue, Arg15. We hypothesized that additional rare AP2σ variants may also be associated with altered CaSR function and hypercalcaemia, and sought for these by analysing >111 995 exomes (>60 706 from ExAc and dbSNP, and 51 289 from the Geisinger Health System-Regeneron DiscovEHR dataset, which also contains clinical data). This identified 11 individuals to have 9 non-synonymous AP2σ variants (Arg3His, Arg15His (x3), Ala44Thr, Phe52Tyr, Arg61His, Thr112Met, Met117Ile, Glu122Gly and Glu142Lys) with 3 of the 4 individuals who had Arg15His and Met117Ile AP2σ variants having mild hypercalcaemia, thereby indicating a prevalence of FHH3-associated AP2σ mutations of ∼7.8 per 100 000 individuals. Structural modelling of the novel eight AP2σ variants (Arg3His, Ala44Thr, Phe52Tyr, Arg61His, Thr112Met, Met117Ile, Glu122Gly and Glu142Lys) predicted that the Arg3His, Thr112Met, Glu122Gly and Glu142Lys AP2σ variants would disrupt polar contacts within the AP2σ subunit or affect the interface between the AP2σ and AP2α subunits. Functional analyses of all eight AP2σ variants in CaSR-expressing cells demonstrated that the Thr112Met, Met117Ile and Glu142Lys variants, located in the AP2σ α4-α5 helical region that forms an interface with AP2α, impaired CaSR-mediated intracellular calcium () signalling, consistent with a loss of function, and this was rectified by treatment with the CaSR positive allosteric modulator cinacalcet. Thus, our studies demonstrate another potential class of FHH3-causing AP2σ mutations located at the AP2σ-AP2α interface.

Protein-altering variants of PTPN2 in childhood-onset Type 1A diabetes.

To examine the contribution of PTPN2 coding variants to the risk of childhood-onset Type 1A diabetes. PTPN2 mutation analysis was carried out for 169 unrelated Japanese people with childhood-onset Type 1A diabetes. We searched for coding variants that were absent or extremely rare in the general population and were scored as damaging by multiple in silico programs. We performed mRNA analysis and three-dimensional structural prediction of the detected variants, when possible. We also examined possible physical links between these variants and previously reported risk SNPs as well as clinical information from variant-positive children. One frameshift variant (p.Q286Yfs*24) and two probably damaging missense substitutions (p.C232W and p.R350Q) were identified in one child each. Of these, p.Q286Yfs*24 and p.C232W were hitherto unreported, while p.R350Q accounted for 2/121,122 alleles of the exome datasets. The p.Q286Yfs*24 variant did not encode stable mRNA, and p.C232W appeared to affect the structure of the tyrosine-protein phosphatase domain. The three variants were physically unrelated to known risk SNPs. The variant-positive children manifested Type 1A diabetes without additional clinical features and invariably carried risk human leukocyte antigen alleles. The results provide the first indication that PTPN2 variants contribute to the risk of Type 1A diabetes, independently of known risk SNPs. PTPN2 coding variants possibly induce non-specific Type 1A diabetes phenotypes in individuals with human leukocyte antigen-mediated disease susceptibility. Our findings warrant further validation.

A Bioinformatics Pipeline for Whole Exome Sequencing: Overview of the Processing and Steps from Raw Data to Downstream Analysis

Abstract Recent advances in next generation sequencing (NGS) technologies have given an impetus to find causality for rare genetic disorders. Since 2005 and aftermath of the human genome project, efforts have been made to understand the rare variants of genetic disorders. Benchmarking the bioinformatics pipeline for whole exome sequencing (WES) has always been a challenge. In this protocol, we discuss detailed steps from quality check to analysis of the variants using a WES pipeline comparing them with reposited public NGS data and survey different techniques, algorithms and software tools used during each step. We observed that variant calling performed on exome and whole genome datasets have different metrics generated when compared to variant callers, GATK and VarScan with different parameters. Furthermore, we found that VarScan with strict parameters could recover 80-85% of high quality GATK SNPs with decreased sensitivity from NGS data. We believe our protocol in the form of pipeline can be used by researchers interested in performing WES analysis for genetic diseases and by large any clinical phenotypes.

Incidental and clinically actionable genetic variants in 1005 whole exomes and genomes from Qatar

Incidental findings in genomic data have been studied in great detail in the recent years, especially from population-scale data sets. However, little is known about the frequency of such findings in ethnic groups, specifically the Middle East, which were not previously covered in global sequencing studies. The availability of whole exome and genome data sets for a highly consanguineous Arab population from Qatar motivated us to explore the incidental findings in this population-scale data. The sequence data of 1005 Qatari individuals were systematically analyzed for incidental genetic variants in the 59 genes suggested by the American College of Medical Genetics and Genomics. We identified four genetic variants which were pathogenic or likely pathogenic. These variants occurred in six individuals, suggesting a frequency of 0.59% in the population, much lesser than that previously reported from European and African populations. Our analysis identified a variant in RYR1 gene associated with Malignant Hyperthermia that has significantly higher frequency in the population compared to global frequencies. Evaluation of the allele frequencies of these variants suggested enrichment in sub-populations, especially in individuals of Sub-Saharan African ancestry. The present study thereby provides the information on pathogenicity and frequency, which could aid in genomic medicine. To the best of our knowledge, this is the first comprehensive analysis of incidental genetic findings in any Arab population and suggests ethnic differences in incidental findings.

Inference of Distribution of Fitness Effects and Proportion of Adaptive Substitutions from Polymorphism Data.

The distribution of fitness effects (DFE) encompasses the fraction of deleterious, neutral, and beneficial mutations. It conditions the evolutionary trajectory of populations, as well as the rate of adaptive molecular evolution (α). Inferring DFE and α from patterns of polymorphism, as given through the site frequency spectrum (SFS) and divergence data, has been a longstanding goal of evolutionary genetics. A widespread assumption shared by previous inference methods is that beneficial mutations only contribute negligibly to the polymorphism data. Hence, a DFE comprising only deleterious mutations tends to be estimated from SFS data, and α is then predicted by contrasting the SFS with divergence data from an outgroup. We develop a hierarchical probabilistic framework that extends previous methods to infer DFE and α from polymorphism data alone. We use extensive simulations to examine the performance of our method. While an outgroup is still needed to obtain an unfolded SFS, we show that both a DFE, comprising both deleterious and beneficial mutations, and α can be inferred without using divergence data. We also show that not accounting for the contribution of beneficial mutations to polymorphism data leads to substantially biased estimates of the DFE and α. We compare our framework with one of the most widely used inference methods available and apply it on a recently published chimpanzee exome data set.

Lightning-fast genome variant detection with GROM.

Current human whole genome sequencing projects produce massive amounts of data, often creating significant computational challenges. Different approaches have been developed for each type of genome variant and method of its detection, necessitating users to run multiple algorithms to find variants. We present Genome Rearrangement OmniMapper (GROM), a novel comprehensive variant detection algorithm accepting aligned read files as input and finding SNVs, indels, structural variants (SVs), and copy number variants (CNVs). We show that GROM outperforms state-of-the-art methods on 7 validated benchmarks using 2 whole genome sequencing (WGS) data sets. Additionally, GROM boasts lightning-fast run times, analyzing a 50× WGS human data set (NA12878) on commonly available computer hardware in 11 minutes, more than an order of magnitude (up to 72 times) faster than tools detecting a similar range of variants. Addressing the needs of big data analysis, GROM combines in 1 algorithm SNV, indel, SV, and CNV detection, providing superior speed, sensitivity, and precision. GROM is also able to detect CNVs, SNVs, and indels in non-paired-read WGS libraries, as well as SNVs and indels in whole exome or RNA sequencing data sets.

TruXy: Trusted Storage Cloud for Scientific Workflows

A wide array of clouds have been built and adopted by business and research communities. Similarly, many research communities use workflow environments and tools such as Taverna and Galaxy to model the execution of tasks to support and expedite the reenactment of complex processes, and ultimately support the repeatability of science. As a result, a number of systems that integrate clouds with these workflow systems have emerged including CloudMap, CloudMan, Galaxy cloud, BioBlend, etc. Though these systems have proven to be successful in service and data integration, they have not dealt with the data security issue inherent in cloud-based systems with the outsourced models of infrastructure provisioning. For many domains, e.g., health, this poses serious challenges regarding the adoption of cloud infrastructures. Yet such domains also have much to gain from clouds especially given the explosion of genomics data and opportunities for personalized medicine in the big data era. This paper addresses this problem by presenting a trusted storage cloud for scientific workflows, called TruXy. The paper describes the TruXy architecture, the corresponding protocols and illustrates the adoption of TruXy to support collaborative bioinformatics research in endocrine genomics. A range of experiments have been performed to measure the performance of TruXy for processing of exome data sets on individuals with a rare genetic disorders: disorders of sex development (DSD). Our results show that the performance of TruXy is comparable to that of using a standalone workflow tool and that it can handle the big data security challenges required.

A pipeline combining multiple strategies for prioritizing heterozygous variants for the identification of candidate genes in exome datasets

BackgroundThe identification of disease-causing variants in autosomal dominant diseases using exome-sequencing data remains a difficult task in small pedigrees. We combined several strategies to improve filtering and prioritizing of heterozygous variants using exome-sequencing datasets in familial Meniere disease: an in-house Pathogenic Variant (PAVAR) score, the Variant Annotation Analysis and Search Tool (VAAST-Phevor), Exomiser-v2, CADD, and FATHMM. We also validated the method by a benchmarking procedure including causal mutations in synthetic exome datasets.ResultsPAVAR and VAAST were able to select the same sets of candidate variants independently of the studied disease. In contrast, Exomiser V2 and VAAST-Phevor had a variable correlation depending on the phenotypic information available for the disease on each family. Nevertheless, all the selected diseases ranked a limited number of concordant variants in the top 10 ranking, using the three systems or other combined algorithm such as CADD or FATHMM.Benchmarking analyses confirmed that the combination of systems with different approaches improves the prediction of candidate variants compared with the use of a single method. The overall efficiency of combined tools ranges between 68 and 71% in the top 10 ranked variants.ConclusionsOur pipeline prioritizes a short list of heterozygous variants in exome datasets based on the top 10 concordant variants combining multiple systems.

Semantic prioritization of novel causative genomic variants.

Discriminating the causative disease variant(s) for individuals with inherited or de novo mutations presents one of the main challenges faced by the clinical genetics community today. Computational approaches for variant prioritization include machine learning methods utilizing a large number of features, including molecular information, interaction networks, or phenotypes. Here, we demonstrate the PhenomeNET Variant Predictor (PVP) system that exploits semantic technologies and automated reasoning over genotype-phenotype relations to filter and prioritize variants in whole exome and whole genome sequencing datasets. We demonstrate the performance of PVP in identifying causative variants on a large number of synthetic whole exome and whole genome sequences, covering a wide range of diseases and syndromes. In a retrospective study, we further illustrate the application of PVP for the interpretation of whole exome sequencing data in patients suffering from congenital hypothyroidism. We find that PVP accurately identifies causative variants in whole exome and whole genome sequencing datasets and provides a powerful resource for the discovery of causal variants.

Complex interaction between HNRNPD mutations and risk polymorphisms is associated with discordant Crohn’s disease in monozygotic twins

Crohn’s disease (CD) is a chronic inflammatory bowel disease (IBD) affecting the lining of digestive tracts of the colon and ileum. To investigate the reasons behind the presence of CD phenotype in one of the monozygotic (MZ) twins, we utilized the whole exome sequence (WES) datasets of CD tissue biopsy and CD blood of affected twin and the exome dataset of blood from healthy twin. We report the presence of discordant and rare damaging mutation in HNRNPD and other risk polymorphisms such as, rs12103, rs2241880, rs3810936, rs7076156, rs1042058 and rs1292053. HNRNPD was found carrying two novel heterozygous mutations – a stop gain mutation that truncated the protein at 249th and 268th amino acid position and a single base missense mutation replacing Aspartate with Valine at 300th amino acid. The identified risk polymorphisms were found conferring susceptibility to CD and IBD. Discordant deleterious and damaging mutation was detected in HNRNPD that have been implicated in inflammatory pathways. Integrating these variants led to the elucidation of pathophysiology of CD in the affected twin involving the causal processes of macrophage activation, tissue death, autophagy, immune response, cell-migration and T-cell activation.

A computational method for estimating the PCR duplication rate in DNA and RNA-seq experiments

BackgroundPCR amplification is an important step in the preparation of DNA sequencing libraries prior to high-throughput sequencing. PCR amplification introduces redundant reads in the sequence data and estimating the PCR duplication rate is important to assess the frequency of such reads. Existing computational methods do not distinguish PCR duplicates from “natural” read duplicates that represent independent DNA fragments and therefore, over-estimate the PCR duplication rate for DNA-seq and RNA-seq experiments.ResultsIn this paper, we present a computational method to estimate the average PCR duplication rate of high-throughput sequence datasets that accounts for natural read duplicates by leveraging heterozygous variants in an individual genome. Analysis of simulated data and exome sequence data from the 1000 Genomes project demonstrated that our method can accurately estimate the PCR duplication rate on paired-end as well as single-end read datasets which contain a high proportion of natural read duplicates. Further, analysis of exome datasets prepared using the Nextera library preparation method indicated that 45–50% of read duplicates correspond to natural read duplicates likely due to fragmentation bias. Finally, analysis of RNA-seq datasets from individuals in the 1000 Genomes project demonstrated that 70–95% of read duplicates observed in such datasets correspond to natural duplicates sampled from genes with high expression and identified outlier samples with a 2-fold greater PCR duplication rate than other samples.ConclusionsThe method described here is a useful tool for estimating the PCR duplication rate of high-throughput sequence datasets and for assessing the fraction of read duplicates that correspond to natural read duplicates. An implementation of the method is available at https://github.com/vibansal/PCRduplicates.