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

2015 ◽  
Vol 12 (11) ◽  
pp. 1058-1060 ◽  
Author(s):  
Tyler Garvin ◽  
Robert Aboukhalil ◽  
Jude Kendall ◽  
Timour Baslan ◽  
Gurinder S Atwal ◽  
...  
Keyword(s):  
Single Cell ◽  
Copy Number ◽  
Copy Number Variations ◽  
Interactive Analysis

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

2014 ◽  
Author(s):  
Tyler Garvin ◽  
Robert Aboukhalil ◽  
Jude Kendall ◽  
Timour Baslan ◽  
Gurinder S. Atwal ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Single Cell ◽  
Open Source ◽  
Visual Analytics ◽  
Copy Number ◽  
Phylogenetic Trees ◽  
Copy Number Variations ◽  
Copy Number Alterations ◽  
Interactive Analysis ◽  
Web Platform

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.

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

2020 ◽  
Vol 10 ◽  
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

Accurate and sensitive single-cell-level detection of copy number variations by micro-channel multiple displacement amplification (μcMDA)

Nanoscale ◽  
2018 ◽  
Vol 10 (37) ◽  
pp. 17933-17941 ◽  
Author(s):  
Junji Li ◽  
Na Lu ◽  
Yuhan Tao ◽  
Mengqin Duan ◽  
Yi Qiao ◽  
...  
Keyword(s):  
Single Cell ◽  
Copy Number ◽  
High Performance ◽  
Multiple Displacement Amplification ◽  
Copy Number Variations ◽  
Reaction Vessel ◽  
Single Cell Level ◽  
Micro Channel ◽  
Cell Level ◽  
Cnv Detection

An improved multiple displacement amplification (MDA) approach realized by compressing the geometry of the reaction vessel exhibits high performance for single-cell-level CNV detection.

scholarly journals Inferring copy number variation from gene expression data: methods, comparisons, and applications to oncology

2021 ◽  
Author(s):  
Joseph Boen ◽  
Joel P. Wagner ◽  
Noemi Di Nanni
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Copy Number ◽  
Single Cells ◽  
Rapid Development ◽  
Cell Cancer ◽  
Copy Number Variations ◽  
Expression Data ◽  
Cell Gene Expression

Copy number variations (CNVs) are genomic events where the number of copies of a particular gene varies from cell to cell. Cancer cells are associated with somatic CNV changes resulting in gene amplifications and gene deletions. However, short of single-cell whole-genome sequencing, it is difficult to detect and quantify CNV events in single cells. In contrast, the rapid development of single-cell RNA sequencing (scRNA-seq) technologies has enabled easy acquisition of single-cell gene expression data. In this work, we employ three methods to infer CNV events from scRNA-seq data and provide a statistical comparison of the methods' results. In addition, we combine the analysis of scRNA-seq and inferred CNV data to visualize and determine subpopulations and heterogeneity in tumor cell populations.

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

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.

scholarly journals Submegabase copy number variations arise during cerebral cortical neurogenesis as revealed by single-cell whole-genome sequencing

2018 ◽  
Vol 115 (42) ◽  
pp. 10804-10809 ◽  
Author(s):  
Suzanne Rohrback ◽  
Craig April ◽  
Fiona Kaper ◽  
Richard R. Rivera ◽  
Christine S. Liu ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Single Cell ◽  
Genome Sequencing ◽  
Copy Number ◽  
Cortical Development ◽  
Building Blocks ◽  
Copy Number Variations ◽  
Whole Genome ◽  
Mature Brain ◽  
The Brain

Somatic copy number variations (CNVs) exist in the brain, but their genesis, prevalence, forms, and biological impact remain unclear, even within experimentally tractable animal models. We combined a transposase-based amplification (TbA) methodology for single-cell whole-genome sequencing with a bioinformatic approach for filtering unreliable CNVs (FUnC), developed from machine learning trained on lymphocyte V(D)J recombination. TbA–FUnC offered superior genomic coverage and removed >90% of false-positive CNV calls, allowing extensive examination of submegabase CNVs from over 500 cells throughout the neurogenic period of cerebral cortical development in Mus musculus. Thousands of previously undocumented CNVs were identified. Half were less than 1 Mb in size, with deletions 4× more common than amplification events, and were randomly distributed throughout the genome. However, CNV prevalence during embryonic cortical development was nonrandom, peaking at midneurogenesis with levels triple those found at younger ages before falling to intermediate quantities. These data identify pervasive small and large CNVs as early contributors to neural genomic mosaicism, producing genomically diverse cellular building blocks that form the highly organized, mature brain.

scholarly journals sciCNV: High-throughput paired profiling of transcriptomes and DNA copy number variations at single cell resolution

2020 ◽  
Author(s):  
Ali Mahdipour-Shirayeh ◽  
Natalie Erdmann ◽  
Chungyee Leung-Hagesteijn ◽  
Rodger E. Tiedemann
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Copy Number ◽  
Target Genes ◽  
Single Cells ◽  
Copy Number Variations ◽  
Cellular Reprogramming ◽  
Cellular Effects ◽  
Normalization Methods ◽  
Cnv Detection

SUMMARYChromosome copy number variations (CNVs) are a near-universal feature of cancer however their effects on cellular function are incompletely understood. Single cell RNA sequencing (scRNA-seq) can reveal cellular gene expression however cannot directly link this to CNVs. Here we report new normalization methods (RTAM1 and −2) for scRNA-seq that improve gene expression alignment between cells, enhancing gene expression comparisons and the application of scRNA-seq to CNV detection. We also report sciCNV, a pipeline for inferring CNVs from RTAM-normalized data. Together, these tools provide dual profiling of transcriptomes and CNVs at single-cell resolution, enabling exploration of the effects of cancer CNVs on cellular programs. We apply these tools to multiple myeloma (MM) and examine the cellular effects of cancer CNVs +8q. Consistent with prior reports, MM cells with +8q22-24 upregulate MYC, MYC-target genes, mRNA processing and protein synthesis, verifying the approach. Overall, we provide new tools for scRNA-seq that enable matched profiling of the CNV landscape and transcriptome of single cells, facilitate deconstruction of the effects of cancer CNVs on cellular reprogramming within single samples.

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

BMC Genomics ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document