TY - JOUR
T1 - A comparison of tools for copy-number variation detection in germline whole exome and whole genome sequencing data
AU - Gabrielaite, Migle
AU - Torp, Mathias Husted
AU - Rasmussen, Malthe Sebro
AU - Andreu-Sánchez, Sergio
AU - Vieira, Filipe Garrett
AU - Pedersen, Christina Bligaard
AU - Kinalis, Savvas
AU - Madsen, Majbritt Busk
AU - Kodama, Miyako
AU - Demircan, Gül Sude
AU - Simonyan, Arman
AU - Yde, Christina Westmose
AU - Olsen, Lars Rønn
AU - Marvig, Rasmus L.
AU - Østrup, Olga
AU - Rossing, Maria
AU - Nielsen, Finn Cilius
AU - Winther, Ole
AU - Bagger, Frederik Otzen
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021
Y1 - 2021
N2 - Copy-number variations (CNVs) have important clinical implications for several diseases and cancers. Relevant CNVs are hard to detect because common structural variations define large parts of the human genome. CNV calling from short-read sequencing would allow single protocol full genomic profiling. We reviewed 50 popular CNV calling tools and included 11 tools for benchmarking in a reference cohort encompassing 39 whole genome sequencing (WGS) samples paired current clinical standard—SNP-array based CNV calling. Additionally, for nine samples we also performed whole exome sequencing (WES), to address the effect of sequencing protocol on CNV calling. Furthermore, we included Gold Standard reference sample NA12878, and tested 12 samples with CNVs confirmed by multiplex ligation-dependent probe amplification (MLPA). Tool performance varied greatly in the number of called CNVs and bias for CNV lengths. Some tools had near-perfect recall of CNVs from arrays for some samples, but poor precision. Several tools had better performance for NA12878, which could be a result of overfitting. We suggest combining the best tools also based on different methodologies: GATK gCNV, Lumpy, DELLY, and cn.MOPS. Reducing the total number of called variants could potentially be assisted by the use of background panels for filtering of frequently called variants.
AB - Copy-number variations (CNVs) have important clinical implications for several diseases and cancers. Relevant CNVs are hard to detect because common structural variations define large parts of the human genome. CNV calling from short-read sequencing would allow single protocol full genomic profiling. We reviewed 50 popular CNV calling tools and included 11 tools for benchmarking in a reference cohort encompassing 39 whole genome sequencing (WGS) samples paired current clinical standard—SNP-array based CNV calling. Additionally, for nine samples we also performed whole exome sequencing (WES), to address the effect of sequencing protocol on CNV calling. Furthermore, we included Gold Standard reference sample NA12878, and tested 12 samples with CNVs confirmed by multiplex ligation-dependent probe amplification (MLPA). Tool performance varied greatly in the number of called CNVs and bias for CNV lengths. Some tools had near-perfect recall of CNVs from arrays for some samples, but poor precision. Several tools had better performance for NA12878, which could be a result of overfitting. We suggest combining the best tools also based on different methodologies: GATK gCNV, Lumpy, DELLY, and cn.MOPS. Reducing the total number of called variants could potentially be assisted by the use of background panels for filtering of frequently called variants.
KW - Benchmark
KW - Bioinformatics
KW - Copy-number variation (CNV)
KW - Structural variant
KW - Whole exome sequencing (WES)
KW - Whole genome sequencing (WGS)
U2 - 10.3390/cancers13246283
DO - 10.3390/cancers13246283
M3 - Journal article
C2 - 34944901
AN - SCOPUS:85121460687
VL - 13
JO - Cancers
JF - Cancers
SN - 2072-6694
IS - 24
M1 - 6283
ER -