Interactive analysis and quality assessment of single-cell copy-number variations

We present an open-source visual-analytics web platform, Ginkgo (http://qb.cshl.edu/ginkgo), for the interactive analysis and quality assessment of single-cell copy-number alterations. Ginkgo automatically constructs copy-number profiles of individual cells from mapped reads, as well as constructing phylogenetic trees of related cells. We validate Ginkgo by reproducing the results of five major studies and examine the data characteristics of three commonly used single-cell amplification techniques to conclude DOP-PCR to be the most consistent for CNV analysis.

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Uphyloplot2: visualizing phylogenetic trees from single-cell RNA-seq data

BMC Genomics ◽

10.1186/s12864-021-07739-3 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Stefan Kurtenbach ◽

Anthony M. Cruz ◽

Daniel A. Rodriguez ◽

Michael A. Durante ◽

J. William Harbour

Keyword(s):

Dna Sequencing ◽

Single Cell ◽

Copy Number ◽

Phylogenetic Trees ◽

Copy Number Variations ◽

Rna Seq ◽

Single Cell Sequencing ◽

Sequencing Technologies ◽

Manual Analysis ◽

Tumor Clonality

Abstract Background Recent advances in single cell sequencing technologies allow for greater resolution in assessing tumor clonality using chromosome copy number variations (CNVs). While single cell DNA sequencing technologies are ideal to identify tumor sub-clones, they remain expensive and in contrast to single cell RNA-seq (scRNA-seq) methods are more limited in the data they generate. However, CNV data can be inferred from scRNA-seq and bulk RNA-seq, for which several tools have been developed, including inferCNV, CaSpER, and HoneyBADGER. Inferences regarding tumor clonality from CNV data (and other sources) are frequently visualized using phylogenetic plots, which previously required time-consuming and error-prone, manual analysis. Results Here, we present Uphyloplot2, a python script that generates phylogenetic plots directly from inferred RNA-seq data, or any Newick formatted dendrogram file. The tool is publicly available at https://github.com/harbourlab/UPhyloplot2/. Conclusions Uphyloplot2 is an easy-to-use tool to generate phylogenetic plots to depict tumor clonality from scRNA-seq data and other sources.

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Joint copy number and mutation phylogeny reconstruction from single-cell amplicon sequencing data

10.1101/2022.01.06.475205 ◽

2022 ◽

Author(s):

Etienne Sollier ◽

Jack Kuipers ◽

Niko Beerenwinkel ◽

Koichi Takahashi ◽

Katharina Jahn

Keyword(s):

Single Cell ◽

Copy Number ◽

Current Knowledge ◽

Snp Array ◽

Point Mutations ◽

Copy Number Variations ◽

Computational Method ◽

Phylogeny Reconstruction ◽

Copy Number Alterations ◽

Sequencing Data

Reconstructing the history of somatic DNA alterations that occurred in a tumour can help understand its evolution and predict its resistance to treatment. Single-cell DNA sequencing (scDNAseq) can be used to investigate clonal heterogeneity and to inform phylogeny reconstruction. However, existing phylogenetic methods for scDNAseq data are designed either for point mutations or for large copy number variations, but not for both types of events simultaneously. Here, we develop COMPASS, a computational method for inferring the joint phylogeny of mutations and copy number alterations from targeted scDNAseq data. We evaluate COMPASS on simulated data and show that it outperforms existing methods. We apply COMPASS to a large cohort of 123 patients with acute myeloid leukemia (AML) and detect copy number alterations, including subclonal ones, which are in agreement with current knowledge of AML development. We further used bulk SNP array data to orthogonally validate or findings.

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UPhyloplot2: Visualizing Phylogenetic Trees from Single-Cell RNA-seq Data

10.1101/2020.05.25.115550 ◽

2020 ◽

Author(s):

Stefan Kurtenbach ◽

Daniel A. Rodriguez ◽

Michael A. Durante ◽

J. William Harbour

Keyword(s):

Single Cell ◽

Copy Number ◽

Phylogenetic Trees ◽

Copy Number Variations ◽

Rna Seq ◽

Chromosome Copy Number ◽

Single Cell Sequencing ◽

Sequencing Technologies ◽

Manual Analysis ◽

Tumor Clonality

AbstractRecent advances in single cell sequencing technologies allow for greater resolution in assessing tumor clonality using chromosome copy number variations (CNVs), which can be inferred from single cell RNA-seq (scRNA-seq) data using applications such as inferCNV. Inferences regarding tumor clonality are frequently visualized using phylogenetic plots, which previously required time-consuming and tedious manual analysis. Here, we present UPhyloplot2, a python script that generates phylogenetic plots directly from inferCNV output files. The tool is publicly available at https://github.com/harbourlab/UPhyloplot2/.

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Interactive analysis and assessment of single-cell copy-number variations

Nature Methods ◽

10.1038/nmeth.3578 ◽

2015 ◽

Vol 12 (11) ◽

pp. 1058-1060 ◽

Cited By ~ 100

Author(s):

Tyler Garvin ◽

Robert Aboukhalil ◽

Jude Kendall ◽

Timour Baslan ◽

Gurinder S Atwal ◽

...

Keyword(s):

Single Cell ◽

Copy Number ◽

Copy Number Variations ◽

Interactive Analysis

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Detection of Copy Number Alterations Using Single Cell Sequencing

Journal of Visualized Experiments ◽

10.3791/55143 ◽

2017 ◽

Cited By ~ 4

Author(s):

Kristin A. Knouse ◽

Jie Wu ◽

Austin Hendricks

Keyword(s):

Single Cell ◽

Copy Number ◽

Copy Number Alterations ◽

Single Cell Sequencing

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Comprehensive Analysis of Copy Number Variations in Kidney Cancer by Single-Cell Exome Sequencing

Frontiers in Genetics ◽

10.3389/fgene.2019.01379 ◽

2020 ◽

Vol 10 ◽

Cited By ~ 1

Author(s):

Wenyang Zhou ◽

Fan Yang ◽

Zhaochun Xu ◽

Meng Luo ◽

Pingping Wang ◽

...

Keyword(s):

Single Cell ◽

Exome Sequencing ◽

Kidney Cancer ◽

Copy Number ◽

Copy Number Variations ◽

Comprehensive Analysis

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Integrated Computational Pipeline for Single-Cell Genomic Profiling

JCO Clinical Cancer Informatics ◽

10.1200/cci.19.00171 ◽

2020 ◽

pp. 464-471 ◽

Cited By ~ 1

Author(s):

Lubomir Chorbadjiev ◽

Jude Kendall ◽

Joan Alexander ◽

Viacheslav Zhygulin ◽

Junyan Song ◽

...

Keyword(s):

Single Cell ◽

Open Source ◽

Copy Number ◽

Cancer Diagnostics ◽

Massachusetts Institute Of Technology ◽

Computational Pipeline ◽

Single Cell Genomics ◽

Single Nucleus ◽

Institute Of Technology ◽

Cell Genome

PURPOSE Copy-number profiling of multiple individual cells from sparse sequencing may be used to reveal a detailed picture of genomic heterogeneity and clonal organization in a tissue biopsy specimen. We sought to provide a comprehensive computational pipeline for single-cell genomics, to facilitate adoption of this molecular technology for basic and translational research. MATERIALS AND METHODS The pipeline comprises software tools programmed in Python and in R and depends on Bowtie, HISAT2, Matplotlib, and Qt. It is installed and used with Anaconda. RESULTS Here we describe a complete pipeline for sparse single-cell genomic data, encompassing all steps of single-nucleus DNA copy-number profiling, from raw sequence processing to clonal structure analysis and visualization. For the latter, a specialized graphical user interface termed the single-cell genome viewer (SCGV) is provided. With applications to cancer diagnostics in mind, the SCGV allows for zooming and linkage to the University of California at Santa Cruz Genome Browser from each of the multiple integrated views of single-cell copy-number profiles. The latter can be organized by clonal substructure or by any of the associated metadata such as anatomic location and histologic characterization. CONCLUSION The pipeline is available as open-source software for Linux and OS X. Its modular structure, extensive documentation, and ease of deployment using Anaconda facilitate its adoption by researchers and practitioners of single-cell genomics. With open-source availability and Massachusetts Institute of Technology licensing, it provides a basis for additional development by the cancer bioinformatics community.

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