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An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing
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Integrated Computational Pipeline for Single-Cell Genomic Profiling.

Lubomir Chorbadjiev1, Jude Kendall2, Joan Alexander2

  • 1Technological School of Electronic Systems, Technical University of Sofia, Sofia, Bulgaria.

JCO Clinical Cancer Informatics
|May 21, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a computational pipeline for single-cell genomics, enabling detailed analysis of genomic heterogeneity and clonal organization in tissue biopsies. The open-source software facilitates adoption for cancer research and diagnostics.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Sparse sequencing of individual cells can reveal genomic heterogeneity and clonal organization in tissue.
  • Single-cell genomics is a powerful molecular technology for research and translational applications.

Purpose of the Study:

  • To develop a comprehensive computational pipeline for single-cell genomics.
  • To facilitate the adoption of single-cell genomic analysis in basic and translational research.

Main Methods:

  • The pipeline utilizes Python and R software tools, with dependencies on Bowtie, HISAT2, Matplotlib, and Qt.
  • Installation and usage are streamlined through Anaconda.
  • The pipeline handles single-nucleus DNA copy-number profiling from raw sequence processing to clonal structure analysis and visualization.

Main Results:

  • A complete pipeline for sparse single-cell genomic data is described.
  • A specialized graphical user interface, the single-cell genome viewer (SCGV), is provided for visualization.
  • The SCGV supports zooming and linkage to the UCSC Genome Browser, with data organized by clonal substructure or metadata.

Conclusions:

  • The pipeline is available as open-source software for Linux and OS X.
  • Its modular structure, documentation, and ease of deployment promote researcher adoption.
  • The open-source and MIT-licensed nature fosters further development by the cancer bioinformatics community.