Integrated Computational Pipeline for Single-Cell Genomic Profiling

Abstract

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.

Ordered and deterministic cancer genome evolution after p53 loss

“…SCClust was used with the default parameters except for the ‘keepboundaries’ parameter, which was set to True. Figure 3a was prepared using a previously described visualization tool (Single-Cell Genome Viewer) 66 available from GitHub/KrasnitzLab. In MEDCICC2, copy-number segments are presented as positive integer vectors with the algorithm solving for MED between pairs of copy-number profiles (for example, profile = aggregate segments of a single cell).…”

“…Phylogenetic and clonal relationships between DP and SP cells from PDAC and pre-tumour mice were investigated using two orthogonal methods: a change-point/breakpoint-based analytical method 66,67 as implemented using the SCC lust software package (available at GitHub/ KrasnitzLab) and a minimum-event distance (MED) method that models whole-genome duplication events in reconstructing phylogenies and ancestral genomes (MEDICC2 68 ). In brief, SCClust identifies change points throughout a set of integer-valued copy-number profiles of single-cell genomes.…”