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Quantitative mapping of the cellular small RNA landscape with AQRNA-seq
Current next-generation RNA sequencing 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 (AQ) RNA-seq, that minimizes biases an...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8355021/ https://www.ncbi.nlm.nih.gov/pubmed/33859402 http://dx.doi.org/10.1038/s41587-021-00874-y |
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author | Hu, Jennifer F. Yim, Daniel Ma, Duanduan Huber, Sabrina M. Davis, Nick Bacusmo, Jo Marie Vermeulen, Sidney Zhou, Jieliang Begley, Thomas J. DeMott, Michael S. Levine, Stuart S. de Crécy-Lagard, Valerie Dedon, Peter C. Cao, Bo |
author_facet | Hu, Jennifer F. Yim, Daniel Ma, Duanduan Huber, Sabrina M. Davis, Nick Bacusmo, Jo Marie Vermeulen, Sidney Zhou, Jieliang Begley, Thomas J. DeMott, Michael S. Levine, Stuart S. de Crécy-Lagard, Valerie Dedon, Peter C. Cao, Bo |
author_sort | Hu, Jennifer F. |
collection | PubMed |
description | Current next-generation RNA sequencing 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 (AQ) RNA-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 miRNA reference library, oligonucleotide standards of varying lengths, and northern 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 tRNA 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. |
format | Online Article Text |
id | pubmed-8355021 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
record_format | MEDLINE/PubMed |
spelling | pubmed-83550212021-10-15 Quantitative mapping of the cellular small RNA landscape with AQRNA-seq Hu, Jennifer F. Yim, Daniel Ma, Duanduan Huber, Sabrina M. Davis, Nick Bacusmo, Jo Marie Vermeulen, Sidney Zhou, Jieliang Begley, Thomas J. DeMott, Michael S. Levine, Stuart S. de Crécy-Lagard, Valerie Dedon, Peter C. Cao, Bo Nat Biotechnol Article Current next-generation RNA sequencing 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 (AQ) RNA-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 miRNA reference library, oligonucleotide standards of varying lengths, and northern 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 tRNA 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. 2021-04-15 2021-08 /pmc/articles/PMC8355021/ /pubmed/33859402 http://dx.doi.org/10.1038/s41587-021-00874-y Text en http://www.nature.com/authors/editorial_policies/license.html#termsUsers may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms |
spellingShingle | Article Hu, Jennifer F. Yim, Daniel Ma, Duanduan Huber, Sabrina M. Davis, Nick Bacusmo, Jo Marie Vermeulen, Sidney Zhou, Jieliang Begley, Thomas J. DeMott, Michael S. Levine, Stuart S. de Crécy-Lagard, Valerie Dedon, Peter C. Cao, Bo Quantitative mapping of the cellular small RNA landscape with AQRNA-seq |
title | Quantitative mapping of the cellular small RNA landscape with AQRNA-seq |
title_full | Quantitative mapping of the cellular small RNA landscape with AQRNA-seq |
title_fullStr | Quantitative mapping of the cellular small RNA landscape with AQRNA-seq |
title_full_unstemmed | Quantitative mapping of the cellular small RNA landscape with AQRNA-seq |
title_short | Quantitative mapping of the cellular small RNA landscape with AQRNA-seq |
title_sort | quantitative mapping of the cellular small rna landscape with aqrna-seq |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8355021/ https://www.ncbi.nlm.nih.gov/pubmed/33859402 http://dx.doi.org/10.1038/s41587-021-00874-y |
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