Yuqi Wang

Supplementary Figures and Supplementary Methods

Epigenetic data used in this study

In vivo G-quadruplex data used in this study

G-quadruplexes (G4) are noncanonical secondary genome structures. Aberrant formation of G4s can impair genome integrity. Investigation of the relationship between G4s and somatic structural variants (SV) in cancers could provide a better understanding of the role of G4 formation in cancer development and progression. In this study, we combined bioinformatic approaches and multiomics data to investigate the connection between G4s and the somatic SVs. Somatic SV breakpoints were significantly enriched in G4 regions, regardless of SV subtypes. This enrichment was only observed in regions demonstrated to form G4s in cells (“active quadruplexes”), rather than in regions with a sequence compatible with G4 formation but without confirmed G4 formation (“potential quadruplexes”). Several genomic features affected the connection between G4s and SVs, with the enrichment being notably strengthened at the boundary of topologically associated domains. Somatic breakpoints were also preferentially associated with G4 regions with earlier replication timing and open chromatin status. In patients with cancer with homologous recombination repair defects, G4s and somatic breakpoints were substantially more strongly associated. Machine learning models were constructed that showed that G4 propensity is a potent feature for predicting the density of SV breakpoints. Altogether, these findings suggest that the G4 structures play a critical role in modulating the production of somatic SVs in cancers.Significance:G-quadruplex structure formation constitutes a critical step in the production of somatic structural variants in cancers, suggesting G-quadruplex structures as potential targets for future cancer prevention and treatment strategies.

DNA G-quadruplexes (G4) are non-canonical DNA structures that play key roles in various biological processes. Antibody-dependent sequencing is an important tool for identifying intracellularly formed DNA G4s, and peak calling is a crucial step in processing the sequencing data. As the applicability of existing peak calling algorithms to intracellular G4 data has not been previously assessed, we systematically compared and evaluated these algorithms to determine those best suited for G4 detection. We selected seven representative candidates from 43 published peak calling algorithms for detailed evaluation. The performance of each candidate on six published intracellular G4 sequencing datasets (GSE107690, GSE145090, GSE133379, GSE178668ChIP-seq, GSE178668CUT&Tag, GSE221437) were assessed by precision and recall against customized benchmarks integrating results from multiple algorithms, as well as consistency with known G4 information (pG4 predicted by pqsfinder, oG4 from GSE63874, and multi-cell-line conserved G4s) and epigenetic signals. We identified MACS2, PeakRanger, and GoPeaks as the most effective algorithms for analyzing intracellular G4 sequencing data, and attributed their superior performance partially to the distribution model of sequencing reads/fragments used in the hypothesis testing step of the peak calling procedures. These findings provide guidance and rationale for selecting peak callers appropriate for intracellular G4 data.

DNA G-quadruplexes (G4) are non-canonical DNA structures that play key roles in various biological processes. Antibody-dependent sequencing is an important tool for identifying intracellularly formed DNA G4s, and peak calling is a crucial step in processing the sequencing data. As the applicability of existing peak calling algorithms to intracellular G4 data has not been previously assessed, we systematically compared and evaluated these algorithms to identify those best suited for G4 detection. We selected seven representative candidates from 43 published peak calling algorithms for detailed evaluation. With precision and recall based on benchmark datasets, as well as consistency with known G4 information and epigenetic signals, we identified MACS2, PeakRanger, and GoPeaks as the most effective algorithms for analyzing intracellular G4 sequencing data, and attributed their superior performance partially to the distribution model of sequencing reads/fragments used in the hypothesis testing step of the peak calling procedures. These findings provide guidance and rationale for selecting peak callers appropriate for intracellular G4 data.

G-quadruplexes (G4s) are non-canonical secondary genome structures. Aberrant formation of G4s can impair genome integrity. Investigation of the relationship between G4s and somatic structural variants (SVs) in cancers could provide a better understanding of the role of G4 formation in cancer development and progression. In this study, we combined bioinformatic approaches and multi-omics data to investigate the connection between G4s and the somatic SVs. Somatic SV breakpoints were significantly enriched in G4 regions, regardless of SV subtypes. This enrichment was only observed in regions demonstrated to form G4s in cells (“active quadruplexes”), rather than in regions with a sequence compatible with G4 formation but without confirmed G4 formation (“potential quadruplexes”). Several genomic features impacted the connection between G4s and SVs, with the enrichment being notably strengthened at the boundary of topologically associated domains. Somatic breakpoints were also preferentially associated with G4 regions with earlier replication timing and open chromatin status. In cancer patients with homologous recombination repair defects, G4s and somatic breakpoints were substantially more strongly associated. Machine learning models were constructed that showed that G4 propensity is a potent feature for predicting the density of SV breakpoints. Altogether, these findings suggest that the G4 structures play a critical role in modulating the production of somatic SVs in cancers.

Supplementary Figures and Supplementary Methods

Supplementary Figures and Supplementary Methods

In vivo G-quadruplex data used in this study