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cPAS-based sequencing on the BGISEQ-500 to explore small non-coding RNAs
BACKGROUND: We present the first sequencing data using the combinatorial probe-anchor synthesis (cPAS)-based BGISEQ-500 sequencer. Applying cPAS, we investigated the repertoire of human small non-coding RNAs and compared it to other techniques. RESULTS: Starting with repeated measurements of differe...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5117531/ https://www.ncbi.nlm.nih.gov/pubmed/27895807 http://dx.doi.org/10.1186/s13148-016-0287-1 |
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author | Fehlmann, Tobias Reinheimer, Stefanie Geng, Chunyu Su, Xiaoshan Drmanac, Snezana Alexeev, Andrei Zhang, Chunyan Backes, Christina Ludwig, Nicole Hart, Martin An, Dan Zhu, Zhenzhen Xu, Chongjun Chen, Ao Ni, Ming Liu, Jian Li, Yuxiang Poulter, Matthew Li, Yongping Stähler, Cord Drmanac, Radoje Xu, Xun Meese, Eckart Keller, Andreas |
author_facet | Fehlmann, Tobias Reinheimer, Stefanie Geng, Chunyu Su, Xiaoshan Drmanac, Snezana Alexeev, Andrei Zhang, Chunyan Backes, Christina Ludwig, Nicole Hart, Martin An, Dan Zhu, Zhenzhen Xu, Chongjun Chen, Ao Ni, Ming Liu, Jian Li, Yuxiang Poulter, Matthew Li, Yongping Stähler, Cord Drmanac, Radoje Xu, Xun Meese, Eckart Keller, Andreas |
author_sort | Fehlmann, Tobias |
collection | PubMed |
description | BACKGROUND: We present the first sequencing data using the combinatorial probe-anchor synthesis (cPAS)-based BGISEQ-500 sequencer. Applying cPAS, we investigated the repertoire of human small non-coding RNAs and compared it to other techniques. RESULTS: Starting with repeated measurements of different specimens including solid tissues (brain and heart) and blood, we generated a median of 30.1 million reads per sample. 24.1 million mapped to the human genome and 23.3 million to the miRBase. Among six technical replicates of brain samples, we observed a median correlation of 0.98. Comparing BGISEQ-500 to HiSeq, we calculated a correlation of 0.75. The comparability to microarrays was similar for both BGISEQ-500 and HiSeq with the first one showing a correlation of 0.58 and the latter one correlation of 0.6. As for a potential bias in the detected expression distribution in blood cells, 98.6% of HiSeq reads versus 93.1% of BGISEQ-500 reads match to the 10 miRNAs with highest read count. After using miRDeep2 and employing stringent selection criteria for predicting new miRNAs, we detected 74 high-likely candidates in the cPAS sequencing reads prevalent in solid tissues and 36 candidates prevalent in blood. CONCLUSIONS: While there is apparently no ideal platform for all challenges of miRNome analyses, cPAS shows high technical reproducibility and supplements the hitherto available platforms. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13148-016-0287-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5117531 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-51175312016-11-28 cPAS-based sequencing on the BGISEQ-500 to explore small non-coding RNAs Fehlmann, Tobias Reinheimer, Stefanie Geng, Chunyu Su, Xiaoshan Drmanac, Snezana Alexeev, Andrei Zhang, Chunyan Backes, Christina Ludwig, Nicole Hart, Martin An, Dan Zhu, Zhenzhen Xu, Chongjun Chen, Ao Ni, Ming Liu, Jian Li, Yuxiang Poulter, Matthew Li, Yongping Stähler, Cord Drmanac, Radoje Xu, Xun Meese, Eckart Keller, Andreas Clin Epigenetics Research BACKGROUND: We present the first sequencing data using the combinatorial probe-anchor synthesis (cPAS)-based BGISEQ-500 sequencer. Applying cPAS, we investigated the repertoire of human small non-coding RNAs and compared it to other techniques. RESULTS: Starting with repeated measurements of different specimens including solid tissues (brain and heart) and blood, we generated a median of 30.1 million reads per sample. 24.1 million mapped to the human genome and 23.3 million to the miRBase. Among six technical replicates of brain samples, we observed a median correlation of 0.98. Comparing BGISEQ-500 to HiSeq, we calculated a correlation of 0.75. The comparability to microarrays was similar for both BGISEQ-500 and HiSeq with the first one showing a correlation of 0.58 and the latter one correlation of 0.6. As for a potential bias in the detected expression distribution in blood cells, 98.6% of HiSeq reads versus 93.1% of BGISEQ-500 reads match to the 10 miRNAs with highest read count. After using miRDeep2 and employing stringent selection criteria for predicting new miRNAs, we detected 74 high-likely candidates in the cPAS sequencing reads prevalent in solid tissues and 36 candidates prevalent in blood. CONCLUSIONS: While there is apparently no ideal platform for all challenges of miRNome analyses, cPAS shows high technical reproducibility and supplements the hitherto available platforms. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13148-016-0287-1) contains supplementary material, which is available to authorized users. BioMed Central 2016-11-21 /pmc/articles/PMC5117531/ /pubmed/27895807 http://dx.doi.org/10.1186/s13148-016-0287-1 Text en © The Author(s). 2016 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 | Research Fehlmann, Tobias Reinheimer, Stefanie Geng, Chunyu Su, Xiaoshan Drmanac, Snezana Alexeev, Andrei Zhang, Chunyan Backes, Christina Ludwig, Nicole Hart, Martin An, Dan Zhu, Zhenzhen Xu, Chongjun Chen, Ao Ni, Ming Liu, Jian Li, Yuxiang Poulter, Matthew Li, Yongping Stähler, Cord Drmanac, Radoje Xu, Xun Meese, Eckart Keller, Andreas cPAS-based sequencing on the BGISEQ-500 to explore small non-coding RNAs |
title | cPAS-based sequencing on the BGISEQ-500 to explore small non-coding RNAs |
title_full | cPAS-based sequencing on the BGISEQ-500 to explore small non-coding RNAs |
title_fullStr | cPAS-based sequencing on the BGISEQ-500 to explore small non-coding RNAs |
title_full_unstemmed | cPAS-based sequencing on the BGISEQ-500 to explore small non-coding RNAs |
title_short | cPAS-based sequencing on the BGISEQ-500 to explore small non-coding RNAs |
title_sort | cpas-based sequencing on the bgiseq-500 to explore small non-coding rnas |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5117531/ https://www.ncbi.nlm.nih.gov/pubmed/27895807 http://dx.doi.org/10.1186/s13148-016-0287-1 |
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