ABSTRACT
Here, we present the integration of the decontam method for contaminant identification and a supplemental approach for identifying the source of contaminants in sequencing data within the QIIME 2 Framework for microbiome data science. We demonstrate its use in a tutorial based on the QIIME 2 “Moving Pictures Tutorial” data.
KEYWORDS: microbiome, contamination, decontam, QIIME 2, amplicon, metagenome
ANNOUNCEMENT
Contaminant identification, within a sequenced-based experiment, is crucial to an analysis as contaminants can affect scientific interpretations and downstream processes (1–5). While much work has been done in this area (6–8), the use of these techniques has not been consistently implemented. To this end, here, we expanded the contaminant identification options available within the QIIME 2 Framework by incorporating decontam, an established bioinformatic method for identifying contaminants in taxonomic feature tables derived from microbiome sequencing data (either amplicon or metagenome) (7) previously only available as an R package. QIIME 2 is a very widely used platform for microbiome data analysis (9–13). The QIIME 2 Framework (Q2F, currently being rebranded as rachis) is a Python-based framework that works on the basis of “plugins” or software packages that ease integration between diverse analysis methods (14). The plugin mechanism has been used to compose prominent bioinformatics software packages from other developers such as DADA2 (15), Deblur (16), and Kraken2 (17). While dedicated contaminant identification methods have not been available within QIIME 2 until recently, with the addition of SCRuB (8), the decontam functionality, is now implemented within the QIIME 2 q2-quality-control plugin (https://github.com/qiime2/q2-quality-control), which is provided by default in both the QIIME 2 and MOSHPIT (18) (i.e., the amplicon and metagenome suites of tools, respectively) distributions. This functionality is, therefore, available for Linux, macOS, and Windows (via WSL) and is available in the Docker container builds provided for all QIIME 2 and MOSHPIT releases. Installation instructions can be found at https://library.qiime2.org/quickstart.
Three new QIIME 2 actions were implemented—decontam-identify, decontam-score-viz, and decontam-identify-batches. The decontam-identify action, written in Python and R, implements the core decontam functionality of assigning scores to taxonomic features, indicating their consistency with contaminant or non-contaminant origin (7). The decontam-score-viz action—utilizing Python, HTML, CSS, and JavaScript—produces as output a histogram of the decontam scores and a table of taxonomic features along with their decontam scores, abundances, prevalences, and classifications as contaminant or non-contaminant. If representative sequences were provided, the table includes hyperlinks to a web-based BLAST search of those sequences against NCBI’s “core_nt” reference database (Fig. 1b and c). The decontam-identify-batches action is a QIIME 2 pipeline action intended for identification of batch-associated contamination and can aid users in localizing contaminants to specific sections of their measurement protocol such as sequencing run, kit, or date of extraction.
Fig 1.
QIIME 2 decontam workflow and example output. (a) Standard workflow for using decontam within the QIIME 2 Framework with required inputs. To remove identified contaminants, the actions “qiime feature-table filter-seqs” and “qiime feature-table filter-features” are utilized. (b) Histogram of decontam scores produced as output from decontam-score-viz. (c) Table of taxonomic features and associated information output by decontam-score-viz.
All technical information associated with these actions can be seen in Table 1. To demonstrate the use of the new decontam functionality within QIIME 2, a tutorial was generated using the QIIME 2 Moving Pictures Tutorial data (19) available at https://amplicon-docs.qiime2.org/en/latest/how-to-guides/decontam. This software is made available under the BSD 3-Clause license, and the tutorial is available under the CC-BY license.
TABLE 1.
Technical information for the actions implemented within the QIIME 2 framework
| Flag | Qiime 2 data type | Description | ||
|---|---|---|---|---|
| decontam-identify | Inputs | --i-table | Feature Table [Frequency] |
Feature table from which contaminated sequences will be identified from |
| --m-metadata-file | METADATA | Metadata file indicating which samples in the experiment are control samples | ||
| Outputs | --o-decontam-scores | FeatureData [DecontamScore] |
The resulting table of scores from the decontam algorithm that scores each feature on how likely they are to be a contaminant sequence | |
| decontam-score-viz | Inputs | --i-table | FeatureTable [Frequency] |
Feature table from which contaminated sequences will be identified from |
| --o-decontam-scores | FeatureData [DecontamScore] |
The resulting table of scores from the decontam algorithm that scores each feature on how likely they are to be a contaminant sequence | ||
| Outputs | --o-visualization | VISUALIZATION | Visualization to be rendered via QIIME 2 View | |
| decontam-identify-batches | Inputs | --i-table | Feature Table [Frequency] |
Feature table from which contaminated sequences will be identified from |
| --m-metadata-file | METADATA | Metadata file indicating which samples in the experiment are control samples | ||
| Outputs | --o-batch-subset-tables | Collection [FeatureTable [Frequency]] |
Directory where feature tables split based on metadata and parameter split-column values | |
| --o-decontam-scores | Collection [FeatureData [DecontamScore]] |
The resulting tables of scores from the decontam algorithm that scores each feature on how likely they are to be a contaminant sequence | ||
| --o-score-histograms | VISUALIZATION | Visualization for all data subsets; to be rendered via QIIME 2 View |
ACKNOWLEDGMENTS
This work was supported primarily by the Engineering Research Centers Program of the National Science Foundation under NSF Cooperative Agreement No. EEC-2133504. ChatGPT (OpenAI; 4.0) was utilized to aid with code debugging and search for syntax. All code suggestions generated from ChatGPT were rigorously tested and augmented by the authors. This work was funded in part by NIH National Cancer Institute Informatics Technology for Cancer Research Award 1U24CA248454-01 to J.G.C. and NIGMS Award R35GM133745 to B.J.C.
Conceptualization, J.T.R., B.J.C., H.S., E.B., and J.G.C.; methodology, J.T.R., B.J.C., H.S., E.B., and J.G.C.; software, J.T.R., B.J.C., E.B., and J.G.C.; validation, J.T.R., B.J.C., E.B., and J.G.C.; formal analysis, J.T.R.; investigation, J.T.R.; resources, B.J.C.; data curation, E.B. and J.G.C.; writing—original draft preparation, J.T.R. and B.J.C.; writing—review and editing, J.T.R., B.J.C., H.S., E.B., and J.G.C.; visualization, J.T.R.; supervision, B.J.C.; project administration, B.J.C.; funding acquisition, B.J.C. All authors have read and agreed to the published version of the manuscript.
Contributor Information
Benjamin J. Callahan, Email: benjamin.j.callahan@gmail.com.
Patrick D. Schloss, University of Michigan, Ann Arbor, Michigan, USA
DATA AVAILABILITY
Example data sets used for validation and tutorial generation can be found at "https://data.qiime2.org/2024.10/tutorials/moving-pictures/emp-single-end-sequences/sequences.fastq.gz" as part of the QIIME 2 "Moving Pictures Tutorial." These data are a subset of the sequence data and sample metadata that are publicly available under the "Moving Pictures of the Human Microbiome" project [MG-RAST:4457768.3-4459735.3].
EDITOR'S NOTE
Editor's Note: This article was handled by Pat Schloss, who acted as a Guest Editor in consultation with the Editor in Chief, Irene Newton. Dr. Schloss's role as the Guest Editor of this article is outside of his duties as the chair of the Journals Committee.
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Associated Data
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Data Availability Statement
Example data sets used for validation and tutorial generation can be found at "https://data.qiime2.org/2024.10/tutorials/moving-pictures/emp-single-end-sequences/sequences.fastq.gz" as part of the QIIME 2 "Moving Pictures Tutorial." These data are a subset of the sequence data and sample metadata that are publicly available under the "Moving Pictures of the Human Microbiome" project [MG-RAST:4457768.3-4459735.3].