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Uzbas, F. ; Opperer, F. ; Sönmezer, C. ; Shaposhnikov, D. ; Sass, S. ; Krendl, C. ; Angerer, P. ; Theis, F.J. ; Müller, N.S. ; Drukker, M.

BART-Seq: cost-effective massively parallelized targeted sequencing for genomics, transcriptomics, and single-cell analysis.

Genome Biol. 20:155 (2019)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
We describe a highly sensitive, quantitative, and inexpensive technique for targeted sequencing of transcript cohorts or genomic regions from thousands of bulk samples or single cells in parallel. Multiplexing is based on a simple method that produces extensive matrices of diverse DNA barcodes attached to invariant primer sets, which are all pre-selected and optimized in silico. By applying the matrices in a novel workflow named Barcode Assembly foR Targeted Sequencing (BART-Seq), we analyze developmental states of thousands of single human pluripotent stem cells, either in different maintenance media or upon Wnt/beta-catenin pathway activation, which identifies the mechanisms of differentiation induction. Moreover, we apply BART-Seq to the genetic screening of breast cancer patients and identify BRCA mutations with very high precision. The processing of thousands of samples and dynamic range measurements that outperform global transcriptomics techniques makes BART-Seq first targeted sequencing technique suitable for numerous research applications.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Barcoding ; Single-cell Rna Sequencing ; Targeted Transcriptomics ; High-throughput Screening ; Human Pluripotent Stem Cells ; Multiplex Pcr; Stem-cell; Gene-expression; Differentiation; Brca2; State; Rna
ISSN (print) / ISBN 1474-760X
e-ISSN 1465-6906
Zeitschrift Genome Biology
Quellenangaben Band: 20, Heft: 1, Seiten: , Artikelnummer: 155 Supplement: ,
Verlag BioMed Central
Verlagsort Campus, 4 Crinan St, London N1 9xw, England
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Stem Cell Research (ISF)
Institute of Computational Biology (ICB)