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CaSpER identifies and visualizes CNV events by integrative analysis of single-cell or bulk RNA-sequencing data

Author

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  • Akdes Serin Harmanci

    (University of Texas Health Science Center at Houston)

  • Arif O. Harmanci

    (University of Texas Health Science Center at Houston)

  • Xiaobo Zhou

    (University of Texas Health Science Center at Houston
    McGovern Medical School at The University of Texas Health Science Center at Houston
    University of Texas Health Science Center at Houston)

Abstract

RNA sequencing experiments generate large amounts of information about expression levels of genes. Although they are mainly used for quantifying expression levels, they contain much more biologically important information such as copy number variants (CNVs). Here, we present CaSpER, a signal processing approach for identification, visualization, and integrative analysis of focal and large-scale CNV events in multiscale resolution using either bulk or single-cell RNA sequencing data. CaSpER integrates the multiscale smoothing of expression signal and allelic shift signals for CNV calling. The allelic shift signal measures the loss-of-heterozygosity (LOH) which is valuable for CNV identification. CaSpER employs an efficient methodology for the generation of a genome-wide B-allele frequency (BAF) signal profile from the reads and utilizes it for correction of CNVs calls. CaSpER increases the utility of RNA-sequencing datasets and complements other tools for complete characterization and visualization of the genomic and transcriptomic landscape of single cell and bulk RNA sequencing data.

Suggested Citation

  • Akdes Serin Harmanci & Arif O. Harmanci & Xiaobo Zhou, 2020. "CaSpER identifies and visualizes CNV events by integrative analysis of single-cell or bulk RNA-sequencing data," Nature Communications, Nature, vol. 11(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-019-13779-x
    DOI: 10.1038/s41467-019-13779-x
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    Cited by:

    1. Peter Bailey & Rachel A. Ridgway & Patrizia Cammareri & Mairi Treanor-Taylor & Ulla-Maja Bailey & Christina Schoenherr & Max Bone & Daniel Schreyer & Karin Purdie & Jason Thomson & William Rickaby & R, 2023. "Driver gene combinations dictate cutaneous squamous cell carcinoma disease continuum progression," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Sujun Chen & Jessica Petricca & Wenbin Ye & Jiansheng Guan & Yong Zeng & Nicholas Cheng & Linsey Gong & Shu Yi Shen & Junjie T. Hua & Megan Crumbaker & Michael Fraser & Stanley Liu & Scott V. Bratman , 2022. "The cell-free DNA methylome captures distinctions between localized and metastatic prostate tumors," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    3. Xian F Mallory & Mohammadamin Edrisi & Nicholas Navin & Luay Nakhleh, 2020. "Assessing the performance of methods for copy number aberration detection from single-cell DNA sequencing data," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-24, July.
    4. Aaron Wing Cheung Kwok & Chen Qiao & Rongting Huang & Mai-Har Sham & Joshua W. K. Ho & Yuanhua Huang, 2022. "MQuad enables clonal substructure discovery using single cell mitochondrial variants," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    5. Akshaya Ramakrishnan & Aikaterini Symeonidi & Patrick Hanel & Katharina T. Schmid & Maria L. Richter & Michael Schubert & Maria Colomé-Tatché, 2023. "epiAneufinder identifies copy number alterations from single-cell ATAC-seq data," Nature Communications, Nature, vol. 14(1), pages 1-10, December.

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