scholarly journals Integrated Computational Pipeline for Single-Cell Genomic Profiling

2020 ◽  
pp. 464-471 ◽  
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.

scholarly journals Single-Cell, Genome-wide Sequencing Identifies Clonal Somatic Copy-Number Variation in the Human Brain

Cell Reports ◽  
2014 ◽  
Vol 8 (5) ◽  
pp. 1280-1289 ◽  
Author(s):  
Xuyu Cai ◽  
Gilad D. Evrony ◽  
Hillel S. Lehmann ◽  
Princess C. Elhosary ◽  
Bhaven K. Mehta ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Human Brain ◽  
Single Cell ◽  
Copy Number ◽  
Genome Wide ◽  
Number Variation ◽  
Cell Genome ◽  
Single Cell Genome

scholarly journals Single-cell copy number calling and event history reconstruction

2020 ◽  
Author(s):  
Jack Kuipers ◽  
Mustafa Anıl Tuncel ◽  
Pedro Ferreira ◽  
Katharina Jahn ◽  
Niko Beerenwinkel
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Copy Number ◽  
Driving Forces ◽  
Simulated Data ◽  
Read Depth ◽  
Cancer Diagnostics ◽  
Whole Genome ◽  
Copy Number Alterations ◽  
Sequencing Data

Copy number alterations are driving forces of tumour development and the emergence of intra-tumour heterogeneity. A comprehensive picture of these genomic aberrations is therefore essential for the development of personalised and precise cancer diagnostics and therapies. Single-cell sequencing offers the highest resolution for copy number profiling down to the level of individual cells. Recent high-throughput protocols allow for the processing of hundreds of cells through shallow whole-genome DNA sequencing. The resulting low read-depth data poses substantial statistical and computational challenges to the identification of copy number alterations. We developed SCICoNE, a statistical model and MCMC algorithm tailored to single-cell copy number profiling from shallow whole-genome DNA sequencing data. SCICoNE reconstructs the history of copy number events in the tumour and uses these evolutionary relationships to identify the copy number profiles of the individual cells. We show the accuracy of this approach in evaluations on simulated data and demonstrate its practicability in applications to a xenograft breast cancer sample.

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 Single-Cell, Genome-wide Sequencing Identifies Clonal Somatic Copy-Number Variation in the Human Brain

Cell Reports ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. 645 ◽  
Author(s):  
Xuyu Cai ◽  
Gilad D. Evrony ◽  
Hillel S. Lehmann ◽  
Princess C. Elhosary ◽  
Bhaven K. Mehta ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Human Brain ◽  
Single Cell ◽  
Copy Number ◽  
Genome Wide ◽  
Number Variation ◽  
Cell Genome ◽  
Single Cell Genome

scholarly journals scSVAS: CNV clonal visualization online platform for large scale single-cell genomics

2021 ◽  
Author(s):  
Lingxi Chen ◽  
Yuhao Qing ◽  
Ruikang Li ◽  
Chaohui Li ◽  
Hechen Li ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Single Cell ◽  
Real Time ◽  
Copy Number ◽  
Large Scale ◽  
Single Cell Analysis ◽  
Interactive Visualization ◽  
Cell Analysis ◽  
Single Cell Genomics ◽  
Number Variation

The recent advance of single-cell copy number variation analysis plays an essential role in addressing intra-tumor heterogeneity, identifying tumor subgroups, and restoring tumor evolving trajectories at single-cell scale. Pleasant visualization of copy number analysis results boosts productive scientific exploration, validation, and sharing. Several single-cell analysis figures have the effectiveness of visualizations for understanding single-cell genomics in published articles and software packages. However, they almost lack real-time interaction, and it is hard to reproduce them. Moreover, existing tools are time-consuming and memory-intensive when they reach large-scale single-cell throughputs. We present an online visualization platform, scSVAS, for real-time interactive single-cell genomics data visualization. scSVAS is specifically designed for large-scale single-cell analysis. Compared with other tools, scSVAS manifests the most comprehensive functionalities. After uploading the specified input files, scSVAS deploys the online interactive visualization automatically. Users may make scientific discoveries, share interactive visualization, and download high-quality publication-ready figures. scSVAS provides versatile utilities for managing, investigating, sharing, and publishing single-cell copy number variation profiles. We envision this online platform will expedite the biological understanding of cancer clonal evolution in single-cell resolution. All visualizations are publicly hosted at https://sc.deepomics.org.

scholarly journals Construction of thousands of single cell genome sequencing libraries using combinatorial indexing

2016 ◽  
Author(s):  
Sarah A. Vitak ◽  
Kristof A. Torkenczy ◽  
Jimi L. Rosenkrantz ◽  
Andrew J. Fields ◽  
Lena Christiansen ◽  
...  
Keyword(s):  
Single Cell ◽  
Genome Sequencing ◽  
Copy Number ◽  
Low Cost ◽  
Single Cells ◽  
Copy Number Variant ◽  
Tumor Evolution ◽  
Somatic Variation ◽  
Cell Genome ◽  
Single Cell Genome

AbstractSingle cell genome sequencing has proven to be a valuable tool for the detection of somatic variation, particularly in the context of tumor evolution and neuronal heterogeneity. Current technologies suffer from high per-cell library construction costs which restrict the number of cells that can be assessed, thus imposing limitations on the ability to quantitatively measure genomic heterogeneity within a tissue. Here, we present Single cell Combinatorial Indexed Sequencing (SCI-seq) as a means of simultaneously generating thousands of low-pass single cell libraries for the purpose of somatic copy number variant detection. In total, we constructed libraries for 16,698 single cells from a combination of cultured cell lines, frontal cortex tissue from Macaca mulatta, and two human adenocarcinomas. This novel technology provides the opportunity for low-cost, deep characterization of somatic copy number variation in single cells, providing a foundational knowledge across both healthy and diseased tissues.

scholarly journals The Free Range

2006 ◽  
Vol 128 (03) ◽  
pp. 26-29 ◽  
Author(s):  
Robert LaMarca
Keyword(s):  
Open Source ◽  
Machine Tools ◽  
Mechanical Engineering ◽  
Associate Professor ◽  
Massachusetts Institute ◽  
Market Penetration ◽  
Massachusetts Institute Of Technology ◽  
Low Emission ◽  
The World ◽  
Institute Of Technology

This article discusses various aspects of open-source product development. The open-source business definition is the development of a product using components that are not restricted in their use by others. Open source is still novel in the world of mechanical engineering. In software, however, its influence has been quite pervasive, both at the corporate and individual levels. Influence of open source has begun to be felt in publishing, the sciences, and education. According to a professional mechanical engineer, an open-source low-emission car is another possible project. Samir Nayfeh, an associate professor of mechanical engineering at the Massachusetts Institute of Technology, briefly investigated open source in the late 1990s, and expects that it would appeal to buyers in markets like machine tools, where customers do not like being locked into a vendor. The current market penetration of open source owes a great deal to individuals who would participate for their own reasons, sometimes for a moral idea, or for inclusion in a community of their professional peers, or to develop better skills.

scholarly journals Quantitative comparison of single-cell sequencing methods using hippocampal neurons

2014 ◽  
Author(s):  
Luwen Ning ◽  
Guan Wang ◽  
Zhoufang Li ◽  
Wen Hu ◽  
Qingming Hou ◽  
...  
Keyword(s):  
Single Cell ◽  
Copy Number ◽  
Hippocampal Neurons ◽  
Cancer Biology ◽  
Multiple Displacement Amplification ◽  
Genetic Diagnosis ◽  
Genomic Analysis ◽  
Dna Amplification ◽  
Copy Number Variations ◽  
Cell Genome

Single-cell genomic analysis has grown rapidly in recent years and will find widespread applications in various fields of biology, including cancer biology, development, immunology, pre-implantation genetic diagnosis, and neurobiology. In this study, we amplified genomic DNA from individual hippocampal neurons using one of three single-cell DNA amplification methods (multiple annealing and looping-based amplification cycles (MALBAC), multiple displacement amplification (MDA), and GenomePlex whole genome amplification (WGA4)). We then systematically evaluated the genome coverage, GC-bias, reproducibility, and copy number variations among individual neurons. Our results showed that single-cell genome sequencing results obtained from the MALBAC and WGA4 methods are highly reproducible and have a high success rate. Chromosome-level and subchromosomal-level copy number variations among individual neurons can be detected.

scholarly journals Author response: Novel insights into breast cancer copy number genetic heterogeneity revealed by single-cell genome sequencing

2020 ◽  
Author(s):  
Timour Baslan ◽  
Jude Kendall ◽  
Konstantin Volyanskyy ◽  
Katherine McNamara ◽  
Hilary Cox ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Single Cell ◽  
Genome Sequencing ◽  
Genetic Heterogeneity ◽  
Copy Number ◽  
Author Response ◽  
Cell Genome ◽  
Single Cell Genome
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