NGSNGS: next-generation simulator for next-generation sequencing data

Henriksen, Rasmus Amund; Zhao, Lei; Korneliussen, Thorfinn

doi:10.1093/bioinformatics/btad041

Cited by 8 publications

(8 citation statements)

References 9 publications

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“…We used NGSNGS 23 to simulate single end sequencing data from forty individuals with varying data qualities– modern, degraded A, and degraded B (see Methods , Figure 2B-C ). The two sets of degraded DNA differ in data quality due to variability in deamination rates and fragmentation patterns.…”

Section: Resultsmentioning

confidence: 99%

Benchmarking for genotyping and imputation using degraded DNA for forensic applications across diverse populations

Zavala,

Rohlfs,

Moorjani

2024

Preprint

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Advancements in sequencing and laboratory technologies have enabled forensic genetic analysis on increasingly low quality and degraded DNA samples. However, existing computational methods applied to genotyping and imputation for generating DNA profiles from degraded DNA have not been tested for forensic applications. Here we simulated sequencing data of varying qualities– coverage, fragment lengths, and deamination patterns–from forty individuals of diverse genetic ancestries. We used this dataset to test the performance of commonly used genotype and imputation methods (SAMtools, GATK, ATLAS, Beagle, and GLIMPSE) on five different SNP panels (MPS- plex, FORCE, two extended kinship panels, and the Human Origins array) that are used for forensic and population genetics applications. For genome mapping and variant calling with degraded DNA, we find use of parameters and methods (such as ATLAS) developed for ancient DNA analysis provides a marked improvement over conventional standards used for next generation sequencing analysis. We find that ATLAS outperforms GATK and SAMtools, achieving over 90% genotyping accuracy for the four largest SNP panels with coverages greater than 10X. For lower coverages, decreased concordance rates are correlated with increased rates of heterozygosity. Genotype refinement and imputation improve the accuracy at lower coverages by leveraging population reference data. For all five SNP panels, we find that using a population reference panel representative of worldwide populations (e.g., the 1000 Genomes Project) results in increased genotype accuracies across genetic ancestries, compared to ancestry-matched population reference panels. Importantly, we find that the low SNP density of commonly used forensics SNP panels can impact the reliability and performance of genotype refinement and imputation. This highlights a critical trade-off between enhancing privacy by using panels with fewer SNPs and maintaining the effectiveness of genomic tools. We provide benchmarks and recommendations for analyzing degraded DNA from diverse populations with widely used genomic methods in forensic casework.HighlightsBiallelic SNP panels: >92% genotyping accuracy for 10X data with ATLASDegraded DNA impacts accuracy under sequencing depth of 10X coverageHigher accuracies across genetic ancestries achieved with a diverse reference panelLeveraging population reference data is not applicable for small SNP panelsTrade-off between genotype accuracy and privacy when considering SNP panel size

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Section: Resultsmentioning

confidence: 99%

Benchmarking for genotyping and imputation using degraded DNA for forensic applications across diverse populations

Zavala,

Rohlfs,

Moorjani

2024

Preprint

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“…We simulated different input data in order to examine the impact of data sources and program parameters on the results, including sequencing depth, reference and parameter‐bootstrapping. We generated simulation datasets with coverage levels from 1 to 100×, leveraging the genome of A. thaliana from the Arabidopsis Information Resource (TAIR), specifically release TAIR10 (Lamesch et al., 2012), utilizing NGSNGS version 0.9 (Henriksen et al., 2023). Our approach incorporated two distinct sets of reference sequences: one set comprised actual reference sequences, identical to those used in Test I with Brassicaceae, and the other set consisted of simulated reference sequences.…”

Section: Methodsmentioning

confidence: 99%

“…We generated simulation datasets with coverage levels from 1 to 100×, leveraging the genome of A. thaliana from the Arabidopsis Information Resource (TAIR), specifically release TAIR10 (Lamesch et al, 2012), utilizing NGSNGS version 0.9 (Henriksen et al, 2023).…”

Section: Parameter and Performance Testingmentioning

confidence: 99%

GeneMiner: A tool for extracting phylogenetic markers from next‐generation sequencing data

Xie,

Guo,

Teng

et al. 2024

Molecular Ecology Resources

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The advancement of next‐generation sequencing (NGS) technologies has been revolutionary for the field of evolutionary biology. This technology has led to an abundance of available genomes and transcriptomes for researchers to mine. Specifically, researchers can mine for various types of molecular markers that are vital for phylogenetic, evolutionary and ecological studies. Numerous tools have been developed to extract these molecular markers from NGS data. However, due to an insufficient number of well‐annotated reference genomes for non‐model organisms, it remains challenging to obtain these markers accurately and efficiently. Here, we present GeneMiner, an improved and expanded version of our previous tool, Easy353. GeneMiner combines the reference‐guided de Bruijn graph assembly with seed self‐discovery and greedy extension. Additionally, it includes a verification step using a parameter‐bootstrap method to reduce the pitfalls associated with using a relatively distant reference. Our results, using both experimental and simulation data, showed GeneMiner can accurately acquire phylogenetic molecular markers for plants using transcriptomic, genomic and other NGS data. GeneMiner is designed to be user‐friendly, fast and memory‐efficient. Further, it is compatible with Linux, Windows and macOS. All source codes are publicly available on GitHub (https://github.com/sculab/GeneMiner) and Gitee (https://gitee.com/sculab/GeneMiner) for easy accessibility and transparency.

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“…To determine the accuracy of our regression framework, we used ngsBriggs inference on simulated files. Using the simulation software NGSNGS [10] we generated 100 files for the biotin- and 100 for the non-biotin model, equally separated into five groups with a varying number of reads, i.e. 10 3 , 10 4 , 10 5 , 10 6 and 10 7 to test different scenarios.…”

Section: Methodsmentioning

confidence: 99%

Revisiting the Briggs ancient DNA damage model: a fast regression method to estimate postmortem damage

Zhao,

Heriksen,

Ramsøe

et al. 2023

Preprint

Self Cite

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MotivationOne essential initial step in the analysis of ancient DNA is to authenticate its ancientness to ensure reliable conclusions. That is, meticulously assessing whether next-generation sequencing reads exhibit ancient characteristics, with a particular focus on the postmortem damage (PMD) signal induced by cytosine deamination in the fragments termini. We present a novel statistical method implementation in a fast multithreaded program ngsBriggs that enables the rapid quantification of PMD by calculation of the Briggs ancient damage model parameters (Briggs parameters).ResultsUsing a fast multinomial regression approach, ngsBriggs accurately models the Briggs parameters, quantifying the PMD signal from single and double-stranded DNA regions. We revisit and extend the original Briggs model, with ngsBriggs modeling PMD signals for contemporary sequencing platforms. Furthermore, ngsBriggs asserts itself as a reliable and consistent tool, by accurately estimating the Briggs parameters across a variety of contamination levels. The classification accuracy of ngsBriggs significantly exceeds the current tool available when discerning ancient-from modern sequencing reads to decontaminate samples. Our novel method and implementation ngsBriggs outperforms existing tools regarding computational speed and accuracy, establishing its practicality and usability. Our tool, ngsBriggs offers a practical and accurate toolset for researchers seeking to authenticate ancient DNA and improve the quality of their data.Availabilityhttps://github.com/lz398/metadamage_briggs

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NGSNGS: next-generation simulator for next-generation sequencing data

Cited by 8 publications

References 9 publications

Benchmarking for genotyping and imputation using degraded DNA for forensic applications across diverse populations

Benchmarking for genotyping and imputation using degraded DNA for forensic applications across diverse populations

GeneMiner: A tool for extracting phylogenetic markers from next‐generation sequencing data

Revisiting the Briggs ancient DNA damage model: a fast regression method to estimate postmortem damage

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