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dc.contributor.authorYeo, Grace Hui Ting
dc.contributor.authorJuez, Oscar
dc.contributor.authorChen, Qing
dc.contributor.authorBanerjee, Budhaditya
dc.contributor.authorChu, Lendy
dc.contributor.authorShen, Max Walt
dc.contributor.authorSabry, May
dc.contributor.authorLogister, Ive
dc.contributor.authorSherwood, Richard I.
dc.contributor.authorGifford, David K
dc.date.accessioned2021-04-23T19:12:59Z
dc.date.available2021-04-23T19:12:59Z
dc.date.issued2021-03
dc.date.submitted2020-08
dc.identifier.issn1553-7358
dc.identifier.urihttps://hdl.handle.net/1721.1/130516
dc.description.abstractWe introduce poly-adenine CRISPR gRNA-based single-cell RNA-sequencing (pAC-Seq), a method that enables the direct observation of guide RNAs (gRNAs) in scRNA-seq. We use pAC-Seq to assess the phenotypic consequences of CRISPR/Cas9 based alterations of gene cis-regulatory regions. We show that pAC-Seq is able to detect cis-regulatory-induced alteration of target gene expression even when biallelic loss of target gene expression occurs in only ~5% of cells. This low rate of biallelic loss significantly increases the number of cells required to detect the consequences of changes to the regulatory genome, but can be ameliorated by transcript-targeted sequencing. Based on our experimental results we model the power to detect regulatory genome induced transcriptomic effects based on the rate of mono/biallelic loss, baseline gene expression, and the number of cells per target gRNA.en_US
dc.language.isoen
dc.publisherPublic Library of Science (PLoS)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pcbi.1008789en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourcePLoSen_US
dc.titleDetection of gene cis-regulatory element perturbations in single-cell transcriptomesen_US
dc.typeArticleen_US
dc.identifier.citationYeo, Grace Hui Ting et al. "Detection of gene cis-regulatory element perturbations in single-cell transcriptomes." PLoS Computational Biology 17, 3 (March 2021): e1008789. © 2021 Yeo et al.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Computational and Systems Biology Programen_US
dc.relation.journalPLoS Computational Biologyen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2021-04-22T12:08:54Z
dspace.orderedauthorsYeo, GHT; Juez, O; Chen, Q; Banerjee, B; Chu, L; Shen, MW; Sabry, M; Logister, I; Sherwood, RI; Gifford, DKen_US
dspace.date.submission2021-04-22T12:08:57Z
mit.journal.volume17en_US
mit.journal.issue3en_US
mit.licensePUBLISHER_CC
mit.metadata.statusComplete


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