Needlestack: an ultra-sensitive variant caller for multi-sample next generation sequencing data
Abstract
The emergence of next-generation sequencing (NGS) has revolutionized the way of reaching a genome sequence, with the promise of potentially providing a comprehensive characterization of DNA variations. Nevertheless, detecting somatic mutations is still a difficult problem, in particular when trying to identify low abundance mutations, such as subclonal mutations, tumour-derived alterations in body fluids or somatic mutations from histological normal tissue. The main challenge is to precisely distinguish between sequencing artefacts and true mutations, particularly when the latter are so rare they reach similar abundance levels as artefacts. Here, we present needlestack, a highly sensitive variant caller, which directly learns from the data the level of systematic sequencing errors to accurately call mutations. Needlestack is based on the idea that the sequencing error rate can be dynamically estimated from analysing multiple samples together. We show that the sequencing error rate varies across alterations, illustrating the need to precisely estimate it. We evaluate the performance of needlestack for various types of variations, and we show that needlestack is robust among positions and outperforms existing state-of-the-art method for low abundance mutations. Needlestack, along with its source code is freely available on the GitHub platform: https://github.com/IARCbioinfo/needlestack.
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BibTeX
@article{delhomme2026needlestack,
title = {{Needlestack: an ultra-sensitive variant caller for multi-sample next generation sequencing data}},
author = {Tiffany M Delhomme and Patrice H Avogbe and Aurélie a G Gabriel and Nicolas Alcala and Noemie Leblay and Catherine Voegele and Maxime Vallée and Priscilia Chopard and Amélie Chabrier and Behnoush Abedi-Ardekani and Valérie Gaborieau and Ivana Holcatova and Vladimir Janout and Lenka Foretová and Sasa Milosavljevic and David Zaridze and Anush Mukeriya and Elisabeth Brambilla and Paul Brennan and Ghislaine Scelo and Lynnette Fernandez-Cuesta and Graham Byrnes and Florence L Calvez-Kelm and James D Mckay and Matthieu Foll},
journal = {NAR Genomics and Bioinformatics},
year = {2026},
month = oct,
doi = {10.1093/nargab/lqaa021},
url = {https://hal.science/hal-05842044v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Needlestack: an ultra-sensitive variant caller for multi-sample next generation sequencing data
AU - Tiffany M Delhomme
AU - Patrice H Avogbe
AU - Aurélie a G Gabriel
AU - Nicolas Alcala
AU - Noemie Leblay
AU - Catherine Voegele
AU - Maxime Vallée
AU - Priscilia Chopard
AU - Amélie Chabrier
AU - Behnoush Abedi-Ardekani
AU - Valérie Gaborieau
AU - Ivana Holcatova
AU - Vladimir Janout
AU - Lenka Foretová
AU - Sasa Milosavljevic
AU - David Zaridze
AU - Anush Mukeriya
AU - Elisabeth Brambilla
AU - Paul Brennan
AU - Ghislaine Scelo
AU - Lynnette Fernandez-Cuesta
AU - Graham Byrnes
AU - Florence L Calvez-Kelm
AU - James D Mckay
AU - Matthieu Foll
PY - 2026
DA - 2026/10/28
JO - NAR Genomics and Bioinformatics
DO - 10.1093/nargab/lqaa021
UR - https://hal.science/hal-05842044v1
AB - The emergence of next-generation sequencing (NGS) has revolutionized the way of reaching a genome sequence, with the promise of potentially providing a comprehensive characterization of DNA variations. Nevertheless, detecting somatic mutations is still a difficult problem, in particular when trying to identify low abundance mutations, such as subclonal mutations, tumour-derived alterations in body fluids or somatic mutations from histological normal tissue. The main challenge is to precisely distinguish between sequencing artefacts and true mutations, particularly when the latter are so rare they reach similar abundance levels as artefacts. Here, we present needlestack, a highly sensitive variant caller, which directly learns from the data the level of systematic sequencing errors to accurately call mutations. Needlestack is based on the idea that the sequencing error rate can be dynamically estimated from analysing multiple samples together. We show that the sequencing error rate varies across alterations, illustrating the need to precisely estimate it. We evaluate the performance of needlestack for various types of variations, and we show that needlestack is robust among positions and outperforms existing state-of-the-art method for low abundance mutations. Needlestack, along with its source code is freely available on the GitHub platform: https://github.com/IARCbioinfo/needlestack.
SN - 2631-9268
ER -