epiAneufinder identifies copy number alterations from single-cell ATAC-seq data.
Akshaya Ramakrishnan, Aikaterini Symeonidi, Patrick Hanel, Katharina T Schmid, Maria L Richter, Michael Schubert, Maria Colomé-Tatché
Author Information
Akshaya Ramakrishnan: Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. ORCID
Aikaterini Symeonidi: Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. aikaterini.symeonidi@helmholtz-munich.de. ORCID
Patrick Hanel: Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Katharina T Schmid: Biomedical Center (BMC), Physiological Chemistry, Faculty of Medicine, LMU Munich, Planegg-Martinsried, Germany. ORCID
Maria L Richter: Biomedical Center (BMC), Physiological Chemistry, Faculty of Medicine, LMU Munich, Planegg-Martinsried, Germany. ORCID
Michael Schubert: Oncode Institute, Division of Cell Biology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, the Netherlands. ORCID
Maria Colomé-Tatché: Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. maria.colome@bmc.med.lmu.de.
Single-cell open chromatin profiling via scATAC-seq has become a mainstream measurement of open chromatin in single-cells. Here we present epiAneufinder, an algorithm that exploits the read count information from scATAC-seq data to extract genome-wide copy number alterations (CNAs) for individual cells, allowing the study of CNA heterogeneity present in a sample at the single-cell level. Using different cancer scATAC-seq datasets, we show that epiAneufinder can identify intratumor clonal heterogeneity in populations of single cells based on their CNA profiles. We demonstrate that these profiles are concordant with the ones inferred from single-cell whole genome sequencing data for the same samples. EpiAneufinder allows the inference of single-cell CNA information from scATAC-seq data, without the need of additional experiments, unlocking a layer of genomic variation which is otherwise unexplored.
References
Genome Biol. 2016 May 31;17(1):115
[PMID: 27246460]
