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

Show simple item record

dc.contributor.author Akimov, Yevhen
dc.contributor.author Bulanova, Daria
dc.contributor.author Timonen, Sanna
dc.contributor.author Wennerberg, Krister
dc.contributor.author Aittokallio, Tero
dc.date.accessioned 2020-04-21T05:46:07Z
dc.date.available 2020-04-21T05:46:07Z
dc.date.issued 2020-03-18
dc.identifier.citation 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
dc.identifier.other PURE: 133913491
dc.identifier.other PURE UUID: c69bea5b-82a0-49e4-b343-8870ea37aa16
dc.identifier.other RIS: urn:8124E5D6784E7DB4C3AD425B262C4203
dc.identifier.other WOS: 000522453400008
dc.identifier.other ORCID: /0000-0002-8139-5950/work/72779913
dc.identifier.other ORCID: /0000-0002-0886-9769/work/72780492
dc.identifier.uri http://hdl.handle.net/10138/314210
dc.description.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. en
dc.format.extent 18
dc.language.iso eng
dc.relation.ispartof Molecular Systems Biology
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 1182 Biochemistry, cell and molecular biology
dc.subject clone tracing
dc.subject DNA barcoding
dc.subject fate mapping
dc.subject lineage tracing
dc.subject phenomics
dc.subject STEM-CELLS
dc.subject EXPRESSION ANALYSIS
dc.subject AUTOPHAGY
dc.subject MODEL
dc.subject FATE
dc.subject HETEROGENEITY
dc.subject DYNAMICS
dc.subject REVEALS
dc.title Improved detection of differentially represented DNA barcodes for high-throughput clonal phenomics en
dc.type Article
dc.contributor.organization Computational Systems Medicine
dc.contributor.organization Institute for Molecular Medicine Finland
dc.contributor.organization University of Helsinki
dc.contributor.organization Helsinki Institute of Life Science HiLIFE
dc.contributor.organization Immunobiology Research Program
dc.contributor.organization Krister Wennerberg / Principal Investigator
dc.contributor.organization Helsinki Institute for Information Technology
dc.contributor.organization Bioinformatics
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.15252/msb.20199195
dc.relation.issn 1744-4292
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

Files in this item

Total number of downloads: Loading...

Files Size Format View
msb.20199195.pdf 32.26Mb PDF View/Open

This item appears in the following Collection(s)

Show simple item record