Genotype and SNP calling from next-generation sequencing data

By Rasmus Nielsen, Joshua S. Paul, Anders Albrechtsen, Yun S. Song. Nature reviews genetics, 2011

1,450 citations recorded by the source corpus.

Meaningful analysis of next-generation sequencing (NGS) data, which are produced extensively by genetics and genomics studies, relies crucially on the accurate calling of SNPs and genotypes. Recently developed statistical methods both improve and quantify the considerable uncertainty associated with genotype calling, and will especially benefit the growing number of studies using low- to medium-coverage data. We review these methods and provide a guide for their use in NGS studies.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c9adccf5682ce6e85a92b215f3aef8940289092a

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Citations in the corpus

Design and Analysis of Single-Cell Sequencing Experiments. (2015, background): “A comprehensive overview and a comparative analysis of existing software tools for SNP calling from next-generation sequencing data can be found in the literature (Nielsen et al., 2011).”
NextGenMap: fast and accurate read mapping in highly polymorphic genomes (2013, methodology): “At the same time, NextGenMap outperforms current map- ping methods with respect to runtime and to the number of correctly mapped reads.”
Dsuite - fast D-statistics and related admixture evidence from VCF files (2020, methodology): “Programs for calculating Patterson's D and related statistics include admixtools (Patterson et al., 2012), hyde (Blischak et al., 2018), angsd (Paul et al., 2011; Soraggi et al., 2018), popgenome (Pfeifer & Kapan, 2019; Pfeifer et al., 2014), and comp - d (Mussmann et al., 2020).”
Next-generation sequencing data interpretation: enhancing reproducibility and accessibility (2012, methodology): “A typical variant-calling analysis involves removing PCR duplicates, recalibrating quality scores and refining alignment, followed by genotyping, refining calls and annotating obtained variants 10 .”
Exome sequencing as a tool for Mendelian disease gene discovery (2011, neutral): “Although methods for calling single nucleotide substitutions are maturing 80 , there is considerable room for improvement in detecting small insertion–deletions and especially copy number changes from short-read exome sequence data 81 (for example, detecting a heterozygous, single-exon deletion…”
A survey of tools for variant analysis of next-generation genome sequencing data (2013, methodology): “Alignment After reads have been processed to meet a certain quality standard, they are usually aligned to an existing reference genome [25].”
Restriction site‐associated DNA sequencing, genotyping error estimation and de novo assembly optimization for population genetic inference (2015, methodology): “…uncertainty could be incorporated into the data analysis itself (Nielsen et al. 2011; Buerkle & Gompert 2013), using Bayesian hierarchical models and genotype probabilities rather than genotypes per se (Gompert & Buerkle 2011; Nielsen et al. 2011; Buerkle & Gompert 2013; Gautier et al. 2013a).”
LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets (2012, background): “While approaches to correct for these have been studied, the majority of variant-calling methods have focused on low-coverage human re-sequencing data and diploid calls (14) (15) (16) with discrete frequencies of interest (i.e. 0, 0.5 and 1; a related set of methods are those tailored for calling di”
An amplicon-based sequencing framework for accurately measuring intrahost virus diversity using PrimalSeq and iVar (2018, methodology): “To investigate if the false discovery rate could be reduced using additional data within the sequencing reads, we trained a logistic regression model incorporating iSNV frequency and strand bias [42] as features, and the presence or absence of known iSNVs as the response variable ( Fig.”
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An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge (2014, background): “While GATK and SAMtools are the most popular variant callers used today and reporte d in his survey, their relative performance has been shown to vary with the sequencing dept h [35,36], and direct comparison of variant calls resulting from parallel analysis of the same raw data by different variant”
Robust Demographic Inference from Genomic and SNP Data (2013, methodology): “Several approaches have been proposed to correct for this bias [32,35], either during the process of genotype calling itself [e.g. 31,36,37] or later by applying quality filters on called genotypes [e.g. 38].”
Genotyping‐by‐Sequencing for Plant Breeding and Genetics (2012, background): “…are sequenced (Xu et al., 2011; Wang et al., 2011), polymorphisms are discovered (Mardis, 2008; Futschik and Schlötterer, 2010; You et al., 2011; Nielsen et al., 2011), gene expression is analyzed (Geraldes et al., 2011; Harper et al., 2012), and populations are genotyped (Baird et al., 2008;…”
Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016, methodology): “Bioinformatic procedures transform the raw data from NGS technology into aligned reads, call genotypes from samples (single or multiple), and perform quality control of individual genotype calls (Nielsen, Paul, Albrechtsen, & Song, 2011).”
A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae (2012, methodology): “cerevisiae strain S288c using a high accuracy mapper Stampy (34) as recommended for NGS data (42).”