ANGSD: Analysis of Next Generation Sequencing Data

By Thorfinn Sand Korneliussen, Anders Albrechtsen, Rasmus Nielsen. BMC Bioinformatics, 2014

2,718 citations recorded by the source corpus.

High-throughput DNA sequencing technologies are generating vast amounts of data. Fast, flexible and memory efficient implementations are needed in order to facilitate analyses of thousands of samples simultaneously. We present a multithreaded program suite called ANGSD. This program can calculate various summary statistics, and perform association mapping and population genetic analyses utilizing the full information in next generation sequencing data by working directly on the raw sequencing data or by using genotype likelihoods. The open source c/c++ program ANGSD is available at http://www.popgen.dk/angsd. The program is tested and validated on GNU/Linux systems. The program facilitates multiple input formats including BAM and imputed beagle genotype probability files. The program allow the user to choose between combinations of existing methods and can perform analysis that is not implemented elsewhere.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/75eac4c0cece8e732e19ea2c95adaf27db0e96ad

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Citations in the corpus

Demographic History and Genetic Adaptation in the Himalayan Region Inferred from Genome-Wide SNP Genotypes of 49 Populations (2018, methodology): “From the published ancient BAMs, we randomly sampled (Korneliussen et al. 2014) a single sequence with a minimum quality of 20 to represent each SNP in our Himalayan data set, trimming 5 bp from both ends of reads to reduce the effect of ancient DNA deamination.”
The genetic history of Ice Age Europe (2016, neutral): “59 31,250-30,690 Layer (10) Gravettian Cranium 1240K M U5 Vestonice No 0.”
EAGER: efficient ancient genome reconstruction (2016, methodology): “For low coverage samples, the pipeline encompasses the ANGSD method to generate genotype likelihoods [16].”
pixy: Unbiased estimation of nucleotide diversity and divergence in the presence of missing data (2020, methodology): “In addition to the three previously explored programs (VCFtools, PopGenome, and scikit-allel), we included estimates of π and d XY from the software ANGSD (Korneliussen et al. 2014).”
From the field to the laboratory: Controlling DNA contamination in human ancient DNA research in the high-throughput sequencing era (2017, neutral): “…patterns are used to assess the authenticity of aDNA, and have been implemented in several recent bioinformatics programs (Helgason et al. 2007; Jónsson et al. 2013; Korneliussen, Albrechtsen, and Nielsen 2014; Skoglund et al. 2014; Renaud et al. 2015; Peltzer et al. 2016; Ginolhac et al. 2011).”
MitoZ: a toolkit for animal mitochondrial genome assembly, annotation and visualization (2019, methodology): “We further calculated the heterozygosity value for each sample based on Site Frequency Spectrum (SFS) obtained using ANGSD (57).”
Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions (2016, neutral): “For example, sequencing samples to a reasonable read depth can minimize genotyping error for individuals ( 1 10-fold read depth [Han et al. 2014; although see also Korneliussen et al. 2014]) and for pooled samples ( 1 100-fold read depth [Schlötterer et al. 2014]).”