| Literature DB >> 33859402 |
Jennifer F Hu1,2, Daniel Yim3,4, Duanduan Ma5, Sabrina M Huber3,6, Nick Davis3,7, Jo Marie Bacusmo8, Sidney Vermeulen3, Jieliang Zhou9, Thomas J Begley10, Michael S DeMott3,11, Stuart S Levine3,5,11, Valérie de Crécy-Lagard8, Peter C Dedon12,13,14, Bo Cao15,16,17.
Abstract
Current next-generation RNA-sequencing (RNA-seq) methods do not provide accurate quantification of small RNAs within a sample, due to sequence-dependent biases in capture, ligation and amplification during library preparation. We present a method, absolute quantification RNA-sequencing (AQRNA-seq), that minimizes biases and provides a direct, linear correlation between sequencing read count and copy number for all small RNAs in a sample. Library preparation and data processing were optimized and validated using a 963-member microRNA reference library, oligonucleotide standards of varying length, and RNA blots. Application of AQRNA-seq to a panel of human cancer cells revealed >800 detectable miRNAs that varied during cancer progression, while application to bacterial transfer RNA pools, with the challenges of secondary structure and abundant modifications, revealed 80-fold variation in tRNA isoacceptor levels, stress-induced site-specific tRNA fragmentation, quantitative modification maps, and evidence for stress-induced, tRNA-driven, codon-biased translation. AQRNA-seq thus provides a versatile means to quantitatively map the small RNA landscape in cells.Entities:
Year: 2021 PMID: 33859402 DOI: 10.1038/s41587-021-00874-y
Source DB: PubMed Journal: Nat Biotechnol ISSN: 1087-0156 Impact factor: 54.908