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. 2015 Nov 27;16:265. doi: 10.1186/s13059-015-0821-z

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© Zhang et al. 2015

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.

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Fig. 4 — Correlation between qPCR-based bacterial quantification and WGS read counts. a Correlation plot between H. pylori-specific ureA gene-based qPCR results and WGS read counts (normalized by counts per million (CPM) reads) mapped to H. pylori. b Correlation plot between 16S rRNA gene-based qPCR results and total WGS read counts mapped to all bacterial genomes. c Correlation plot between 16S rRNA gene-based qPCR results and WGS read counts mapped to non-H. pylori genomes. In three cases qPCR measurements were derived from a sample that was adjacent to the WGS sample whereas in all other cases qPCR and WGS were performed on the same biopsy. Blue dashed lines are the linear regression models