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Evaluation of commercially available small RNASeq library preparation kits using low input RNA

BACKGROUND: Evolving interest in comprehensively profiling the full range of small RNAs present in small tissue biopsies and in circulating biofluids, and how the profile differs with disease, has launched small RNA sequencing (RNASeq) into more frequent use. However, known biases associated with sm...

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Autores principales: Yeri, Ashish, Courtright, Amanda, Danielson, Kirsty, Hutchins, Elizabeth, Alsop, Eric, Carlson, Elizabeth, Hsieh, Michael, Ziegler, Olivia, Das, Avash, Shah, Ravi V., Rozowsky, Joel, Das, Saumya, Van Keuren-Jensen, Kendall
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5936030/
https://www.ncbi.nlm.nih.gov/pubmed/29728066
http://dx.doi.org/10.1186/s12864-018-4726-6
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author Yeri, Ashish
Courtright, Amanda
Danielson, Kirsty
Hutchins, Elizabeth
Alsop, Eric
Carlson, Elizabeth
Hsieh, Michael
Ziegler, Olivia
Das, Avash
Shah, Ravi V.
Rozowsky, Joel
Das, Saumya
Van Keuren-Jensen, Kendall
author_facet Yeri, Ashish
Courtright, Amanda
Danielson, Kirsty
Hutchins, Elizabeth
Alsop, Eric
Carlson, Elizabeth
Hsieh, Michael
Ziegler, Olivia
Das, Avash
Shah, Ravi V.
Rozowsky, Joel
Das, Saumya
Van Keuren-Jensen, Kendall
author_sort Yeri, Ashish
collection PubMed
description BACKGROUND: Evolving interest in comprehensively profiling the full range of small RNAs present in small tissue biopsies and in circulating biofluids, and how the profile differs with disease, has launched small RNA sequencing (RNASeq) into more frequent use. However, known biases associated with small RNASeq, compounded by low RNA inputs, have been both a significant concern and a hurdle to widespread adoption. As RNASeq is becoming a viable choice for the discovery of small RNAs in low input samples and more labs are employing it, there should be benchmark datasets to test and evaluate the performance of new sequencing protocols and operators. In a recent publication from the National Institute of Standards and Technology, Pine et al., 2018, the investigators used a commercially available set of three tissues and tested performance across labs and platforms. RESULTS: In this paper, we further tested the performance of low RNA input in three commonly used and commercially available RNASeq library preparation kits; NEB Next, NEXTFlex, and TruSeq small RNA library preparation. We evaluated the performance of the kits at two different sites, using three different tissues (brain, liver, and placenta) with high (1 μg) and low RNA (10 ng) input from tissue samples, or 5.0, 3.0, 2.0, 1.0, 0.5, and 0.2 ml starting volumes of plasma. As there has been a lack of robust validation platforms for differentially expressed miRNAs, we also compared low input RNASeq data with their expression profiles on three different platforms (Abcam Fireplex, HTG EdgeSeq, and Qiagen miRNome). CONCLUSIONS: The concordance of RNASeq results on these three platforms was dependent on the RNA expression level; the higher the expression, the better the reproducibility. The results provide an extensive analysis of small RNASeq kit performance using low RNA input, and replication of these data on three downstream technologies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-018-4726-6) contains supplementary material, which is available to authorized users.
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spelling pubmed-59360302018-05-11 Evaluation of commercially available small RNASeq library preparation kits using low input RNA Yeri, Ashish Courtright, Amanda Danielson, Kirsty Hutchins, Elizabeth Alsop, Eric Carlson, Elizabeth Hsieh, Michael Ziegler, Olivia Das, Avash Shah, Ravi V. Rozowsky, Joel Das, Saumya Van Keuren-Jensen, Kendall BMC Genomics Methodology Article BACKGROUND: Evolving interest in comprehensively profiling the full range of small RNAs present in small tissue biopsies and in circulating biofluids, and how the profile differs with disease, has launched small RNA sequencing (RNASeq) into more frequent use. However, known biases associated with small RNASeq, compounded by low RNA inputs, have been both a significant concern and a hurdle to widespread adoption. As RNASeq is becoming a viable choice for the discovery of small RNAs in low input samples and more labs are employing it, there should be benchmark datasets to test and evaluate the performance of new sequencing protocols and operators. In a recent publication from the National Institute of Standards and Technology, Pine et al., 2018, the investigators used a commercially available set of three tissues and tested performance across labs and platforms. RESULTS: In this paper, we further tested the performance of low RNA input in three commonly used and commercially available RNASeq library preparation kits; NEB Next, NEXTFlex, and TruSeq small RNA library preparation. We evaluated the performance of the kits at two different sites, using three different tissues (brain, liver, and placenta) with high (1 μg) and low RNA (10 ng) input from tissue samples, or 5.0, 3.0, 2.0, 1.0, 0.5, and 0.2 ml starting volumes of plasma. As there has been a lack of robust validation platforms for differentially expressed miRNAs, we also compared low input RNASeq data with their expression profiles on three different platforms (Abcam Fireplex, HTG EdgeSeq, and Qiagen miRNome). CONCLUSIONS: The concordance of RNASeq results on these three platforms was dependent on the RNA expression level; the higher the expression, the better the reproducibility. The results provide an extensive analysis of small RNASeq kit performance using low RNA input, and replication of these data on three downstream technologies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-018-4726-6) contains supplementary material, which is available to authorized users. BioMed Central 2018-05-05 /pmc/articles/PMC5936030/ /pubmed/29728066 http://dx.doi.org/10.1186/s12864-018-4726-6 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Yeri, Ashish
Courtright, Amanda
Danielson, Kirsty
Hutchins, Elizabeth
Alsop, Eric
Carlson, Elizabeth
Hsieh, Michael
Ziegler, Olivia
Das, Avash
Shah, Ravi V.
Rozowsky, Joel
Das, Saumya
Van Keuren-Jensen, Kendall
Evaluation of commercially available small RNASeq library preparation kits using low input RNA
title Evaluation of commercially available small RNASeq library preparation kits using low input RNA
title_full Evaluation of commercially available small RNASeq library preparation kits using low input RNA
title_fullStr Evaluation of commercially available small RNASeq library preparation kits using low input RNA
title_full_unstemmed Evaluation of commercially available small RNASeq library preparation kits using low input RNA
title_short Evaluation of commercially available small RNASeq library preparation kits using low input RNA
title_sort evaluation of commercially available small rnaseq library preparation kits using low input rna
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5936030/
https://www.ncbi.nlm.nih.gov/pubmed/29728066
http://dx.doi.org/10.1186/s12864-018-4726-6
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