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

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NGSNGS : next-generation simulator for next-generation sequencing data. / Henriksen, Rasmus Amund; Zhao, Lei; Korneliussen, Thorfinn Sand.

In: Bioinformatics, Vol. 39, No. 1, btad041, 2023.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Henriksen, RA, Zhao, L & Korneliussen, TS 2023, 'NGSNGS: next-generation simulator for next-generation sequencing data', Bioinformatics, vol. 39, no. 1, btad041. https://doi.org/10.1093/bioinformatics/btad041

APA

Henriksen, R. A., Zhao, L., & Korneliussen, T. S. (2023). NGSNGS: next-generation simulator for next-generation sequencing data. Bioinformatics, 39(1), [btad041]. https://doi.org/10.1093/bioinformatics/btad041

Vancouver

Henriksen RA, Zhao L, Korneliussen TS. NGSNGS: next-generation simulator for next-generation sequencing data. Bioinformatics. 2023;39(1). btad041. https://doi.org/10.1093/bioinformatics/btad041

Author

Henriksen, Rasmus Amund ; Zhao, Lei ; Korneliussen, Thorfinn Sand. / NGSNGS : next-generation simulator for next-generation sequencing data. In: Bioinformatics. 2023 ; Vol. 39, No. 1.

Bibtex

@article{a1212a4370e3447295bd86a3e54fa181,
title = "NGSNGS: next-generation simulator for next-generation sequencing data",
abstract = "SUMMARY: With the rapid expansion of the capabilities of the DNA sequencers throughout the different sequencing generations, the quantity of generated data has likewise increased. This evolution has also led to new bioinformatical methods, for which in silico data have become crucial when verifying the accuracy of a model or the robustness of a genomic analysis pipeline. Here, we present a multithreaded next-generation simulator for next-generation sequencing data (NGSNGS), which simulates reads faster than currently available methods and programs. NGSNGS can simulate reads with platform-specific characteristics based on nucleotide quality score profiles as well as including a post-mortem damage model which is relevant for simulating ancient DNA. The simulated sequences are sampled (with replacement) from a reference DNA genome, which can represent a haploid genome, polyploid assemblies or even population haplotypes and allows the user to simulate known variable sites directly. The program is implemented in a multithreading framework and is factors faster than currently available tools while extending their feature set and possible output formats. AVAILABILITY AND IMPLEMENTATION: The method and associated programs are released as open-source software, code and user manual are available at https://github.com/RAHenriksen/NGSNGS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
author = "Henriksen, {Rasmus Amund} and Lei Zhao and Korneliussen, {Thorfinn Sand}",
note = "Publisher Copyright: {\textcopyright} The Author(s) 2023. Published by Oxford University Press.",
year = "2023",
doi = "10.1093/bioinformatics/btad041",
language = "English",
volume = "39",
journal = "Bioinformatics (Online)",
issn = "1367-4811",
publisher = "Oxford University Press",
number = "1",

}

RIS

TY - JOUR

T1 - NGSNGS

T2 - next-generation simulator for next-generation sequencing data

AU - Henriksen, Rasmus Amund

AU - Zhao, Lei

AU - Korneliussen, Thorfinn Sand

N1 - Publisher Copyright: © The Author(s) 2023. Published by Oxford University Press.

PY - 2023

Y1 - 2023

N2 - SUMMARY: With the rapid expansion of the capabilities of the DNA sequencers throughout the different sequencing generations, the quantity of generated data has likewise increased. This evolution has also led to new bioinformatical methods, for which in silico data have become crucial when verifying the accuracy of a model or the robustness of a genomic analysis pipeline. Here, we present a multithreaded next-generation simulator for next-generation sequencing data (NGSNGS), which simulates reads faster than currently available methods and programs. NGSNGS can simulate reads with platform-specific characteristics based on nucleotide quality score profiles as well as including a post-mortem damage model which is relevant for simulating ancient DNA. The simulated sequences are sampled (with replacement) from a reference DNA genome, which can represent a haploid genome, polyploid assemblies or even population haplotypes and allows the user to simulate known variable sites directly. The program is implemented in a multithreading framework and is factors faster than currently available tools while extending their feature set and possible output formats. AVAILABILITY AND IMPLEMENTATION: The method and associated programs are released as open-source software, code and user manual are available at https://github.com/RAHenriksen/NGSNGS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

AB - SUMMARY: With the rapid expansion of the capabilities of the DNA sequencers throughout the different sequencing generations, the quantity of generated data has likewise increased. This evolution has also led to new bioinformatical methods, for which in silico data have become crucial when verifying the accuracy of a model or the robustness of a genomic analysis pipeline. Here, we present a multithreaded next-generation simulator for next-generation sequencing data (NGSNGS), which simulates reads faster than currently available methods and programs. NGSNGS can simulate reads with platform-specific characteristics based on nucleotide quality score profiles as well as including a post-mortem damage model which is relevant for simulating ancient DNA. The simulated sequences are sampled (with replacement) from a reference DNA genome, which can represent a haploid genome, polyploid assemblies or even population haplotypes and allows the user to simulate known variable sites directly. The program is implemented in a multithreading framework and is factors faster than currently available tools while extending their feature set and possible output formats. AVAILABILITY AND IMPLEMENTATION: The method and associated programs are released as open-source software, code and user manual are available at https://github.com/RAHenriksen/NGSNGS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

U2 - 10.1093/bioinformatics/btad041

DO - 10.1093/bioinformatics/btad041

M3 - Journal article

C2 - 36661298

AN - SCOPUS:85147318129

VL - 39

JO - Bioinformatics (Online)

JF - Bioinformatics (Online)

SN - 1367-4811

IS - 1

M1 - btad041

ER -

ID: 336529710