7
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Correction to: Piphillin predicts metagenomic composition and dynamics from DADA2- corrected 16S rDNA sequences

      correction

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Correction to: BMC Genomics (2020) 21:56 https://doi.org/10.1186/s12864-019-6427-1 Following the publication of this article [1], the authors reported errors in Figs. 1, 2 and 5. Due to a typesetting error the asterisks denoting significance were missing from the published figures. Fig. 1 Piphillin results comparing 16S rRNA sequence analysis approaches using the KEGG database. a 16S rRNA gene amplicon sequences passing the identity threshold to the reference genomes. Percentage of amplicon sequences from two datasets using two different 16S rRNA sequence analysis approaches passing identity cutoffs from 75 to 100% against 16S rRNA gene sequences in the KEGG genome database. b Spearman’s correlation coefficient between Piphillin results and shotgun metagenomics at ten different identity cutoffs tested in Piphillin. Spearman’s correlation coefficient was calculated for each sample and mean, 1st and 3rd quartiles are depicted by the boxes. Whiskers extend to the furthest points within 150% of the interquartile range. c Balanced accuracy in identifying differentially abundant KOs from Piphillin against corresponding metagenomics at each identity cutoff. * indicates p < 0.05, ** indicates p < 0.001, *** indicates p < 0.0001 Fig. 2 Piphillin results comparing 16S rRNA sequence analysis approaches using the BioCyc database. a 16S rRNA gene amplicon sequences passing the identity threshold to the reference genomes. Percentage of amplicon sequences from two datasets using two different 16S rRNA sequence analysis approaches passing identity cutoffs from 75 to 100% against 16S rRNA gene sequences in the BioCyc genome database. b Spearman’s correlation coefficient between Piphillin results and shotgun metagenomics at ten different identity cutoffs tested in Piphillin. Spearman’s correlation coefficient was calculated for each sample and mean, 1st and 3rd quartiles are depicted by the boxes. Whiskers extend to the furthest points within 150% of the interquartile range. c Balanced accuracy in identifying differentially abundant features from Piphillin against corresponding metagenomics at each identity cutoff. * indicates p < 0.05, ** indicates p < 0.001, *** indicates p < 0.0001 Fig. 5 Piphillin executed with BioCyc vs KEGG reference on environmental samples. Spearman’s correlation coefficient against corresponding shotgun metagenomics results were compared the hypersaline microbial mat dataset using either KEGG and BioCyc references. Spearman’s correlation coefficient was calculated for each sample and ranges are depicted as box and whisker plots as described in Fig. 1. * indicates p < 0.05, ** indicates p < 0.001, *** indicates p < 0.0001 The correct figures are reproduced in this Correction article, and the original article has been corrected.

          Related collections

          Most cited references1

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Piphillin predicts metagenomic composition and dynamics from DADA2-corrected 16S rDNA sequences

          Background Shotgun metagenomic sequencing reveals the potential in microbial communities. However, lower-cost 16S ribosomal RNA (rRNA) gene sequencing provides taxonomic, not functional, observations. To remedy this, we previously introduced Piphillin, a software package that predicts functional metagenomic content based on the frequency of detected 16S rRNA gene sequences corresponding to genomes in regularly updated, functionally annotated genome databases. Piphillin (and similar tools) have previously been evaluated on 16S rRNA data processed by the clustering of sequences into operational taxonomic units (OTUs). New techniques such as amplicon sequence variant error correction are in increased use, but it is unknown if these techniques perform better in metagenomic content prediction pipelines, or if they should be treated the same as OTU data in respect to optimal pipeline parameters. Results To evaluate the effect of 16S rRNA sequence analysis method (clustering sequences into OTUs vs amplicon sequence variant error correction into amplicon sequence variants (ASVs)) on the ability of Piphillin to predict functional metagenomic content, we evaluated Piphillin-predicted functional content from 16S rRNA sequence data processed through OTU clustering and error correction into ASVs compared to corresponding shotgun metagenomic data. We show a strong correlation between metagenomic data and Piphillin-predicted functional content resulting from both 16S rRNA sequence analysis methods. Differential abundance testing with Piphillin-predicted functional content exhibited a low false positive rate (< 0.05) while capturing a large fraction of the differentially abundant features resulting from corresponding metagenomic data. However, Piphillin prediction performance was optimal at different cutoff parameters depending on 16S rRNA sequence analysis method. Using data analyzed with amplicon sequence variant error correction, Piphillin outperformed comparable tools, for instance exhibiting 19% greater balanced accuracy and 54% greater precision compared to PICRUSt2. Conclusions Our results demonstrate that raw Illumina sequences should be processed for subsequent Piphillin analysis using amplicon sequence variant error correction (with DADA2 or similar methods) and run using a 99% ID cutoff for Piphillin, while sequences generated on platforms other than Illumina should be processed via OTU clustering (e.g., UPARSE) and run using a 96% ID cutoff for Piphillin. Piphillin is publicly available for academic users (Piphillin server. http://piphillin.secondgenome.com/.)
            Bookmark

            Author and article information

            Contributors
            todd@secondgenome.com
            Journal
            BMC Genomics
            BMC Genomics
            BMC Genomics
            BioMed Central (London )
            1471-2164
            31 January 2020
            31 January 2020
            2020
            : 21
            : 105
            Affiliations
            [1 ]GRID grid.452682.f, Informatics Department, Second Genome Inc, ; South San Francisco, California USA
            [2 ]ISNI 0000000123318773, GRID grid.7872.a, APC Microbiome Ireland, , University College Cork, Co., ; Cork, Ireland
            [3 ]ISNI 0000000123318773, GRID grid.7872.a, School of Microbiology, , University College Cork, Co., ; Cork, Ireland
            [4 ]ISNI 0000000123318773, GRID grid.7872.a, Department of Medicine, , University College Cork, Co., ; Cork, Ireland
            Article
            6537
            10.1186/s12864-020-6537-9
            6993515
            32005153
            a5b30dea-542b-40e1-8431-0620744e62eb
            © The Author(s). 2020

            Open Access 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.

            History
            Categories
            Correction
            Custom metadata
            © The Author(s) 2020

            Genetics
            Genetics

            Comments

            Comment on this article