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Summarizing performance for genome scale measurement of miRNA: reference samples and metrics

Pine, P. Scott and Lund, Steven P. and Parsons, Jerod R. and Vang, Lindsay K. and Mahabal, Ashish A. and Cinquini, Luca and Kelly, Sean C. and Kincaid, Heather and Crichton, Daniel J. and Spira, Avrum and Liu, Gang and Gower, Adam C. and Pass, Harvey I. and Goparaju, Chandra and Dubinett, Steven M. and Krysan, Kostyantyn and Stass, Sanford A. and Kukuruga, Debra and Van Keuren-Jensen, Kendall and Courtright-Lim, Amanda and Thompson, Karol L. and Rosenzweig, Barry A. and Sorbara, Lynn and Srivastava, Sudhir and Salit, Marc L. (2018) Summarizing performance for genome scale measurement of miRNA: reference samples and metrics. BMC Genomics, 19 . Art. No. 180. ISSN 1471-2164. PMCID PMC5838960. https://resolver.caltech.edu/CaltechAUTHORS:20180312-125835096

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Abstract

Background: The potential utility of microRNA as biomarkers for early detection of cancer and other diseases is being investigated with genome-scale profiling of differentially expressed microRNA. Processes for measurement assurance are critical components of genome-scale measurements. Here, we evaluated the utility of a set of total RNA samples, designed with between-sample differences in the relative abundance of miRNAs, as process controls. Results: Three pure total human RNA samples (brain, liver, and placenta) and two different mixtures of these components were evaluated as measurement assurance control samples on multiple measurement systems at multiple sites and over multiple rounds. In silico modeling of mixtures provided benchmark values for comparison with physical mixtures. Biomarker development laboratories using next-generation sequencing (NGS) or genome-scale hybridization assays participated in the study and returned data from the samples using their routine workflows. Multiplexed and single assay reverse-transcription PCR (RT-PCR) was used to confirm in silico predicted sample differences. Data visualizations and summary metrics for genome-scale miRNA profiling assessment were developed using this dataset, and a range of performance was observed. These metrics have been incorporated into an online data analysis pipeline and provide a convenient dashboard view of results from experiments following the described design. The website also serves as a repository for the accumulation of performance values providing new participants in the project an opportunity to learn what may be achievable with similar measurement processes. Conclusions: The set of reference samples used in this study provides benchmark values suitable for assessing genome-scale miRNA profiling processes. Incorporation of these metrics into an online resource allows laboratories to periodically evaluate their performance and assess any changes introduced into their measurement process.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1186/s12864-018-4496-1DOIArticle
https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4496-1PublisherArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5838960/PubMed CentralArticle
https://doi.org/10.1101/210310DOIDiscussion Paper
ORCID:
AuthorORCID
Mahabal, Ashish A.0000-0003-2242-0244
Additional Information:© 2018 The Author(s). This 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. Received: 29 September 2017; Accepted: 25 January; 2018; Published: 6 March 2018. Acknowledgements: Not applicable The work presented in this manuscript is jointly designed, executed and written under the auspices of an Interagency Agency Agreement between the National Cancer Institute and the National Institute of Standards and Technology, both of which are Federal Agencies supported by the funds from US Government. Additional funding comes from NCI-EDRN Grant numbers: U01CA214182, U01CA214195, U24CA115091. Part of the work was performed at JPL/Caltech under the contract to NASA, and at the Center for Data-Driven Discovery, Caltech. Availability of data and materials: The datasets supporting the conclusions of this article are included within the article, its Additional files, and online at [https://edrn.nci.nih.gov/microrna]. Disclaimer: Certain commercial entities, equipment or materials may be identified in this document in order to describe an experimental procedure or concept adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the entities, materials or equipment are necessarily the best available for the purpose. Authors’ contributions: PSP, LS, SS, and MLS designed the study. PSP, LKV, and MLS developed the reference samples. LKV, AS, GL, ACG, HIP, CG, SMD, KK, SAS, DK, KVKJ, ACL, KLT, and BAR acquired and processed the data. PSP, SPL, and JRP developed metrics and visualizations. PSP, AAM, LC, SCK, HK, and DJC developed the website. PSP drafted the manuscript. SPL, JRP, AAM, and LC contributed manuscript sections. All authors participated in the revision process and provided final approval. Ethics approval and consent to participate: Not applicable. Human Brain Reference RNA (Cat. No. AM6050), Human Liver Total RNA (Cat. No. AM7960), and Human Placenta Total RNA (Cat. No. AM7950) was obtained from Ambion (Thermo Fisher Scientific). Consent for publication: Not applicable. The authors declare that they have no competing interest.
Funders:
Funding AgencyGrant Number
National Cancer InstituteUNSPECIFIED
National Institute of Standards and Technology (NIST)UNSPECIFIED
NIHU01CA214182
NIHU01CA214195
NIHU24CA115091
PubMed Central ID:PMC5838960
Record Number:CaltechAUTHORS:20180312-125835096
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20180312-125835096
Official Citation:P. Scott PineEmail author, Steven P. Lund, Jerod R. Parsons, Lindsay K. Vang, Ashish A. Mahabal, Luca Cinquini, Sean C. Kelly, Heather Kincaid, Daniel J. Crichton, Avrum Spira, Gang Liu, Adam C. Gower, Harvey I. Pass, Chandra Goparaju, Steven M. Dubinett, Kostyantyn Krysan, Sanford A. Stass, Debra Kukuruga, Kendall Van Keuren-Jensen, Amanda Courtright-Lim, Karol L. Thompson, Barry A. Rosenzweig, Lynn Sorbara, Sudhir Srivastava and Marc L. Salit BMC Genomics201819:180. https://doi.org/10.1186/s12864-018-4496-1
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:85248
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:12 Mar 2018 20:10
Last Modified:26 Nov 2019 11:15

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