Improved detection of differentially represented DNA barcodes for high-throughput clonal phenomics

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Akimov , Y , Bulanova , D , Timonen , S , Wennerberg , K & Aittokallio , T 2020 , ' Improved detection of differentially represented DNA barcodes for high-throughput clonal phenomics ' , Molecular Systems Biology , vol. 16 , no. 3 , 9195 . https://doi.org/10.15252/msb.20199195

Title: Improved detection of differentially represented DNA barcodes for high-throughput clonal phenomics
Author: Akimov, Yevhen; Bulanova, Daria; Timonen, Sanna; Wennerberg, Krister; Aittokallio, Tero
Other contributor: University of Helsinki, Computational Systems Medicine
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Immunobiology Research Program
University of Helsinki, Krister Wennerberg / Principal Investigator
University of Helsinki, Helsinki Institute for Information Technology







Date: 2020-03-18
Language: eng
Number of pages: 18
Belongs to series: Molecular Systems Biology
ISSN: 1744-4292
DOI: https://doi.org/10.15252/msb.20199195
URI: http://hdl.handle.net/10138/314210
Abstract: Abstract Cellular DNA barcoding has become a popular approach to study heterogeneity of cell populations and to identify clones with differential response to cellular stimuli. However, there is a lack of reliable methods for statistical inference of differentially responding clones. Here, we used mixtures of DNA-barcoded cell pools to generate a realistic benchmark read count dataset for modelling a range of outcomes of clone-tracing experiments. By accounting for the statistical properties intrinsic to the DNA barcode read count data, we implemented an improved algorithm that results in a significantly lower false-positive rate, compared to current RNA-seq data analysis algorithms, especially when detecting differentially responding clones in experiments with strong selection pressure. Building on the reliable statistical methodology, we illustrate how multidimensional phenotypic profiling enables one to deconvolute phenotypically distinct clonal subpopulations within a cancer cell line. The mixture control dataset and our analysis results provide a foundation for benchmarking and improving algorithms for clone-tracing experiments.
Subject: 1182 Biochemistry, cell and molecular biology
clone tracing
DNA barcoding
fate mapping
lineage tracing
phenomics
STEM-CELLS
EXPRESSION ANALYSIS
AUTOPHAGY
MODEL
FATE
HETEROGENEITY
DYNAMICS
REVEALS
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