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 |
Contributor organization: | Computational Systems Medicine Institute for Molecular Medicine Finland University of Helsinki Helsinki Institute of Life Science HiLIFE Immunobiology Research Program Krister Wennerberg / Principal Investigator Helsinki Institute for Information Technology Bioinformatics |
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 |
Peer reviewed: | Yes |
Rights: | cc_by |
Usage restriction: | openAccess |
Self-archived version: | publishedVersion |
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