A multi-platform reference for somatic structural variation detection

Jose Espejo Valle-Inclan; Nicolle J M Besselink; Ewart de Bruijn; Daniel L Cameron; Jana Ebler; Joachim Kutzera; Stef van Lieshout; Tobias Marschall; Marcel Nelen; Peter Priestley; Ivo Renkens; Margaretha G M Roemer; Markus J van Roosmalen; Aaron M Wenger; Bauke Ylstra; Remond J A Fijneman; Wigard P Kloosterman; Edwin Cuppen

doi:10.1016/j.xgen.2022.100139

A multi-platform reference for somatic structural variation detection

Jose Espejo Valle-Inclan, Nicolle J M Besselink, Ewart de Bruijn, Daniel L Cameron, Jana Ebler, Joachim Kutzera, Stef van Lieshout, Tobias Marschall, Marcel Nelen, Peter Priestley, Ivo Renkens, Margaretha G M Roemer, Markus J van Roosmalen, Aaron M Wenger, Bauke Ylstra, Remond J A Fijneman, Wigard P Kloosterman^*, Edwin Cuppen^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Accurate detection of somatic structural variation (SV) in cancer genomes remains a challenging problem. This is in part due to the lack of high-quality, gold-standard datasets that enable the benchmarking of experimental approaches and bioinformatic analysis pipelines. Here, we performed somatic SV analysis of the paired melanoma and normal lymphoblastoid COLO829 cell lines using four different sequencing technologies. Based on the evidence from multiple technologies combined with extensive experimental validation, we compiled a comprehensive set of carefully curated and validated somatic SVs, comprising all SV types. We demonstrate the utility of this resource by determining the SV detection performance as a function of tumor purity and sequence depth, highlighting the importance of assessing these parameters in cancer genomics projects. The truth somatic SV dataset as well as the underlying raw multi-platform sequencing data are freely available and are an important resource for community somatic benchmarking efforts.

Original language	English
Article number	100139
Journal	Cell genomics
Volume	2
Issue number	6
DOIs	https://doi.org/10.1016/j.xgen.2022.100139
Publication status	Published - 8 Jun 2022

Keywords

benchmarking
cancer
long sequencing read
short sequencing read
structural variant
truth set
whole-genome sequencing

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Espejo Valle-Inclan, J., Besselink, N. J. M., de Bruijn, E., Cameron, D. L., Ebler, J., Kutzera, J., van Lieshout, S., Marschall, T., Nelen, M., Priestley, P., Renkens, I., Roemer, M. G. M., van Roosmalen, M. J., Wenger, A. M., Ylstra, B., Fijneman, R. J. A., Kloosterman, W. P., & Cuppen, E. (2022). A multi-platform reference for somatic structural variation detection. Cell genomics, 2(6), Article 100139. https://doi.org/10.1016/j.xgen.2022.100139

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title = "A multi-platform reference for somatic structural variation detection",

abstract = "Accurate detection of somatic structural variation (SV) in cancer genomes remains a challenging problem. This is in part due to the lack of high-quality, gold-standard datasets that enable the benchmarking of experimental approaches and bioinformatic analysis pipelines. Here, we performed somatic SV analysis of the paired melanoma and normal lymphoblastoid COLO829 cell lines using four different sequencing technologies. Based on the evidence from multiple technologies combined with extensive experimental validation, we compiled a comprehensive set of carefully curated and validated somatic SVs, comprising all SV types. We demonstrate the utility of this resource by determining the SV detection performance as a function of tumor purity and sequence depth, highlighting the importance of assessing these parameters in cancer genomics projects. The truth somatic SV dataset as well as the underlying raw multi-platform sequencing data are freely available and are an important resource for community somatic benchmarking efforts.",

keywords = "benchmarking, cancer, long sequencing read, short sequencing read, structural variant, truth set, whole-genome sequencing",

author = "{Espejo Valle-Inclan}, Jose and Besselink, {Nicolle J M} and {de Bruijn}, Ewart and Cameron, {Daniel L} and Jana Ebler and Joachim Kutzera and {van Lieshout}, Stef and Tobias Marschall and Marcel Nelen and Peter Priestley and Ivo Renkens and Roemer, {Margaretha G M} and {van Roosmalen}, {Markus J} and Wenger, {Aaron M} and Bauke Ylstra and Fijneman, {Remond J A} and Kloosterman, {Wigard P} and Edwin Cuppen",

note = "Publisher Copyright: {\textcopyright} 2022 The Author(s)",

year = "2022",

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doi = "10.1016/j.xgen.2022.100139",

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Espejo Valle-Inclan, J, Besselink, NJM, de Bruijn, E, Cameron, DL, Ebler, J, Kutzera, J, van Lieshout, S, Marschall, T, Nelen, M, Priestley, P, Renkens, I, Roemer, MGM, van Roosmalen, MJ, Wenger, AM, Ylstra, B, Fijneman, RJA, Kloosterman, WP & Cuppen, E 2022, 'A multi-platform reference for somatic structural variation detection', Cell genomics, vol. 2, no. 6, 100139. https://doi.org/10.1016/j.xgen.2022.100139

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AU - Espejo Valle-Inclan, Jose

AU - Besselink, Nicolle J M

AU - de Bruijn, Ewart

AU - Cameron, Daniel L

AU - Ebler, Jana

AU - Kutzera, Joachim

AU - van Lieshout, Stef

AU - Marschall, Tobias

AU - Nelen, Marcel

AU - Priestley, Peter

AU - Renkens, Ivo

AU - Roemer, Margaretha G M

AU - van Roosmalen, Markus J

AU - Wenger, Aaron M

AU - Ylstra, Bauke

AU - Fijneman, Remond J A

AU - Kloosterman, Wigard P

AU - Cuppen, Edwin

PY - 2022/6/8

Y1 - 2022/6/8

N2 - Accurate detection of somatic structural variation (SV) in cancer genomes remains a challenging problem. This is in part due to the lack of high-quality, gold-standard datasets that enable the benchmarking of experimental approaches and bioinformatic analysis pipelines. Here, we performed somatic SV analysis of the paired melanoma and normal lymphoblastoid COLO829 cell lines using four different sequencing technologies. Based on the evidence from multiple technologies combined with extensive experimental validation, we compiled a comprehensive set of carefully curated and validated somatic SVs, comprising all SV types. We demonstrate the utility of this resource by determining the SV detection performance as a function of tumor purity and sequence depth, highlighting the importance of assessing these parameters in cancer genomics projects. The truth somatic SV dataset as well as the underlying raw multi-platform sequencing data are freely available and are an important resource for community somatic benchmarking efforts.

AB - Accurate detection of somatic structural variation (SV) in cancer genomes remains a challenging problem. This is in part due to the lack of high-quality, gold-standard datasets that enable the benchmarking of experimental approaches and bioinformatic analysis pipelines. Here, we performed somatic SV analysis of the paired melanoma and normal lymphoblastoid COLO829 cell lines using four different sequencing technologies. Based on the evidence from multiple technologies combined with extensive experimental validation, we compiled a comprehensive set of carefully curated and validated somatic SVs, comprising all SV types. We demonstrate the utility of this resource by determining the SV detection performance as a function of tumor purity and sequence depth, highlighting the importance of assessing these parameters in cancer genomics projects. The truth somatic SV dataset as well as the underlying raw multi-platform sequencing data are freely available and are an important resource for community somatic benchmarking efforts.

KW - benchmarking

KW - cancer

KW - long sequencing read

KW - short sequencing read

KW - structural variant

KW - truth set

KW - whole-genome sequencing

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VL - 2

JO - Cell genomics

JF - Cell genomics

IS - 6

M1 - 100139

ER -

A multi-platform reference for somatic structural variation detection

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Keywords

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