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Single cell lineage reconstruction using distance-based algorithms and the R package, DCLEAR

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dc.contributor.authorGong, Wuming-
dc.contributor.authorKim, Hyunwoo J.-
dc.contributor.authorGarry, Daniel J.-
dc.contributor.authorKwak, Il-Youp-
dc.date.accessioned2022-06-10T20:40:27Z-
dc.date.available2022-06-10T20:40:27Z-
dc.date.created2022-06-09-
dc.date.issued2022-03-24-
dc.identifier.issn1471-2105-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/141896-
dc.description.abstractBackground DCLEAR is an R package used for single cell lineage reconstruction. The advances of CRISPR-based gene editing technologies have enabled the prediction of cell lineage trees based on observed edited barcodes from each cell. However, the performance of existing reconstruction methods of cell lineage trees was not accessed until recently. In response to this problem, the Allen Institute hosted the Cell Lineage Reconstruction Dream Challenge in 2020 to crowdsource relevant knowledge from across the world. Our team won sub-challenges 2 and 3 in the challenge competition. Results The DCLEAR package contained the R codes, which was submitted in response to sub-challenges 2 and 3. Our method consists of two steps: (1) distance matrix estimation and (2) the tree reconstruction from the distance matrix. We proposed two novel methods for distance matrix estimation as outlined in the DCLEAR package. Using our method, we find that two of the more sophisticated distance methods display a substantially improved level of performance compared to the traditional Hamming distance method. DCLEAR is open source and freely available from R CRAN and from under the GNU General Public License, version 3. Conclusions DCLEAR is a powerful resource for single cell lineage reconstruction.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherBMC-
dc.subjectEVOLUTION-
dc.titleSingle cell lineage reconstruction using distance-based algorithms and the R package, DCLEAR-
dc.typeArticle-
dc.contributor.affiliatedAuthorKim, Hyunwoo J.-
dc.identifier.doi10.1186/s12859-022-04633-x-
dc.identifier.scopusid2-s2.0-85127023009-
dc.identifier.wosid000772822200001-
dc.identifier.bibliographicCitationBMC BIOINFORMATICS, v.23, no.1-
dc.relation.isPartOfBMC BIOINFORMATICS-
dc.citation.titleBMC BIOINFORMATICS-
dc.citation.volume23-
dc.citation.number1-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaBiochemistry & Molecular Biology-
dc.relation.journalResearchAreaBiotechnology & Applied Microbiology-
dc.relation.journalResearchAreaMathematical & Computational Biology-
dc.relation.journalWebOfScienceCategoryBiochemical Research Methods-
dc.relation.journalWebOfScienceCategoryBiotechnology & Applied Microbiology-
dc.relation.journalWebOfScienceCategoryMathematical & Computational Biology-
dc.subject.keywordPlusEVOLUTION-
dc.subject.keywordAuthorCell lineage tracing-
dc.subject.keywordAuthorLineage reconstruction-
dc.subject.keywordAuthorMachine learning-
dc.subject.keywordAuthorSimulation-
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