ArticleGenetic epidemiology2017
PhredEM: a phred-score-informed genotype-calling approach for next-generation sequencing studies.
Article in Genetic epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Influence of farm composition and management on the microbiome of the camel tick (Hyalomma dromedarii).Scientific reports · 2025Article
- Next generation sequencing and beyond: a review of genomic sequencing methods.Functional & integrative genomics · 2025Article
- Microsatellite development for Theridion evexum (Araneae: Theridiidae) using low-coverage genome sequencing and the MiMi script.PloS one · 2025Article
- Unveiling Hidden Genetic Architectures: Molecular Diagnostic Yield of Whole Exome Sequencing in 50 Children With Autism Spectrum Disorder Negative for Copy Number Variations.Genetics research · 2025Article
- Using nanopore sequencing to identify bacterial infection in joint replacements: a preliminary study.Briefings in functional genomics · 2024Article
- PNNGS, a multi-convolutional parallel neural network for genomic selection.Frontiers in plant science · 2024Article
- GRHL2-controlled gene expression networks in luminal breast cancer.Cell communication and signaling : CCS · 2023Article
- Genomic divergence, local adaptation, and complex demographic history may inform management of a popular sportfish species complex.Ecology and evolution · 2022Article
- Metagenomic analysis of primary colorectal carcinomas and their metastases identifies potential microbial risk factors.Molecular oncology · 2021Article
- ngsComposer: an automated pipeline for empirically based NGS data quality filtering.Briefings in bioinformatics · 2021Article
- Origin-independent analysis links SARS-CoV-2 local genomes with COVID-19 incidence and mortality.Briefings in bioinformatics · 2021Article
- Article
- VikNGS: a C++ variant integration kit for next generation sequencing association analysis.Bioinformatics (Oxford, England) · 2020Article
- Barcode-free next-generation sequencing error validation for ultra-rare variant detection.Nature communications · 2019Article
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3 authors.
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Abstract
A fundamental challenge in analyzing next-generation sequencing (NGS) data is to determine an individual's genotype accurately, as the accuracy of the inferred genotype is essential to downstream analyses. Correctly estimating the base-calling error rate is critical to accurate genotype calls. Phred scores that accompany each call can be used to decide which calls are reliable. Some genotype callers, such as GATK and SAMtools, directly calculate the base-calling error rates from phred scores or recalibrated base quality scores. Others, such as SeqEM, estimate error rates from the read data without using any quality scores. It is also a common quality control procedure to filter out reads with low phred scores. However, choosing an appropriate phred score threshold is problematic as a too high threshold may lose data, while a too low threshold may introduce errors. We propose a new likelihood-based genotype-calling approach that exploits all reads and estimates the per-base error rates by incorporating phred scores through a logistic regression model. The approach, which we call PhredEM, uses the expectation-maximization (EM) algorithm to obtain consistent estimates of genotype frequencies and logistic regression parameters. It also includes a simple, computationally efficient screening algorithm to identify loci that are estimated to be monomorphic, so that only loci estimated to be nonmonomorphic require application of the EM algorithm. Like GATK, PhredEM can be used together with a linkage-disequilibrium-based method such as Beagle, which can further improve genotype calling as a refinement step. We evaluate the performance of PhredEM using both simulated data and real sequencing data from the UK10K project and the 1000 Genomes project. The results demonstrate that PhredEM performs better than either GATK or SeqEM, and that PhredEM is an improved, robust, and widely applicable genotype-calling approach for NGS studies. The relevant software is freely available.
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