- Technical Note
- Open Access
Pipeline for amplifying and analyzing amplicons of the V1–V3 region of the 16S rRNA gene
- Received: 9 January 2016
- Accepted: 19 July 2016
- Published: 2 August 2016
Abstract
Background
Profiling of 16S rRNA gene sequences is an important tool for testing hypotheses in complex microbial communities, and analysis methods must be updated and validated as sequencing technologies advance. In host-associated bacterial communities, the V1–V3 region of the 16S rRNA gene is a valuable region to profile because it provides a useful level of taxonomic resolution; however, use of Illumina MiSeq data for experiments targeting this region needs validation.
Results
Using a MiSeq machine and the version 3 (300 × 2) chemistry, we sequenced the V1–V3 region of the 16S rRNA gene within a mock community. Nineteen bacteria and one archaeon comprised the mock community, and 12 replicate amplifications of the community were performed and sequenced. Sequencing the large fragment (490 bp) that encompasses V1–V3 yielded a higher error rate (3.6 %) than has been reported when using smaller fragment sizes. This higher error rate was due to a large number of sequences that occurred only one or two times among all mock community samples. Removing sequences that occurred one time among all samples (singletons) reduced the error rate to 1.4 %. Diversity estimates of the mock community containing all sequences were inflated, whereas estimates following singleton removal more closely reflected the actual mock community membership. A higher percentage of the sequences could be taxonomically assigned after singleton and doubleton sequences were removed, and the assignments reflected the membership of the input DNA.
Conclusions
Sequencing the V1–V3 region of the 16S rRNA gene on the MiSeq platform may require additional sequence curation in silico, and improved error rates and diversity estimates show that removing low-frequency sequences is reasonable. When datasets have a high number of singletons, these singletons can be removed from the analysis without losing statistical power while reducing error and improving microbiota assessment.
Keywords
- V1–V3
- 16S rRNA gene
- MiSeq
- Mock community
- Microbial ecology
Background
The affordability and scalability of nucleic acid sequencing have enabled researchers to conduct microbial community analyses on an unprecedented scale. One highly used method to query bacterial communities involves sequencing amplicons of the 16S rRNA gene. However, sequence and analysis of these amplicons has numerous technical limitations including chimera formation during the PCR step and errors introduced by sequencing technologies. Previous advances in validating 16S rRNA gene sequence analyses have employed various sequencing platforms, multiple sequencing centers, and both real and mock bacterial communities [1, 2]. In silico methods, such as chimera removal [3, 4], quality filtering [5], and clustering methods [6], have been developed and validated to improve analysis of amplicons and are essential to separate true data from noise [7].
The quality of 16S rRNA gene sequence data is dependent on numerous technical steps that are difficult to control, often resulting in thousands of unique sequences even after implementing quality-control steps. One approach to managing these low-frequency sequences is to implement closed-reference operational taxonomic unit (OTU)-picking, which clusters sequences into OTUs based on their assignment to a reference database [8]. However, many researchers choose not to require phylogenetic assignment prior to clustering because it could eliminate important, undescribed members of the community. An additional approach is to remove low-frequency OTUs after sequence clustering, although it is computationally expensive to retain sequencing artifacts through distance matrix creation and subsequent clustering. Unreferenced removal of sequences prior to OTU-calling improves the speed and feasibility of analyzing large datasets, as we demonstrate here by sequencing and analyzing the 16S rRNA gene, V1–V3 region, of an artificial (mock) microbial community.
The challenge of sequencing 16S rRNA gene amplicons via the MiSeq platform is choosing a variable region that both informs the microbiota of interest and results in an amplicon sufficiently short to overlap both forward and reverse reads of a paired-end reaction. The V4 region has therefore been used for this sequencing method because its short amplicon (~400 bp including primer sequences) is technically amenable to assembly with a low error rate (0.01 %) [2]. However, longer 16S gene regions are sometimes preferred for biological reasons based on the research question despite the potential higher error rates associated with longer amplicon sequences. For example, the V1–V3 region, but not the V3–V5, region can discriminate Staphylococci populations [9]. We have previously used the V1–V3 region to analyze the swine gut microbiota (e.g., [10]), thus we adapted the MiSeq amplicon method for this region by adding a singleton removal step prior to OTU clustering and validated the method by sequencing 12 technical replicates of a mock community.
Methods
Generating the mock community
16S gene composition of the mock community
Strain | Genome sizea | # 16S genesa | Genome copies per microgramb | Reference or DSMZ catalogue numberc | |
---|---|---|---|---|---|
1 | Campylobacter jejuni 11168 | 1,641,481 | 3 | 5.64E+08 | [23] |
2 | Salmonella enterica serovar Typhimurium SL1344 | 4,878,012 | 6 | 1.89E+08 | [24] |
3 | Escherichia coli mg1655 | 4,656,144 | 7 | 1.99E+08 | [25] |
4 | Megasphaera elsdenii LC-1 | 2,474,718 | 7 | 3.74E+08 | DSM 20460 |
5 | Cloacibacillus porcorum CL-84 | 3,585,187 | 3 | 2.57E+08 | [26] |
6 | Brachyspira hyodysenteriae | 3,050,489 | 1 | 3.04E+08 | [27] |
7 | Haemophilus parasuis 29755 | 2,224,137 | 2 | 4.17E+08 | [28] |
8 | Bordetella bronchiseptica 1289 | 5,207,899 | 3 | 1.78E+08 | [29] |
9 | Staphylococcus aureus USA300 | 2,872,915 | 5 | 3.22E+08 | [30] |
10 | Bacteroides thetaiotaomicron | 6,293,399 | 5 | 1.47E+08 | DSM 2079 |
11 | Faecalibacterium prausnitzii A2-165 | 3,080,849 | 3 | 3.01E+08 | DSM 17677 |
12 | Streptococcus parasanguinis | 2,153,652 | 4 | 4.30E+08 | DSM 6778 |
13 | Parabacteroides merdae | 4,431,877 | 3 | 2.09E+08 | DSM 19495 |
14 | Oscillibacter valericigenes | 4,470,622 | 3 | 2.07E+08 | DSM 18026 |
15 | Desulfovibrio gigas | 3,788,225 | 4 | 2.45E+08 | DSM 1382 |
16 | Lactobacillus delbrueckii subspecies bulgaricus | 1,864,998 | 9 | 4.97E+08 | DSM 20081 |
17 | Coriobacterium glomerans | 2,115,681 | 2 | 4.38E+08 | DSM 20642 |
18 | Oxalobacter formigenes BA-2 | 2,509,362 | 1 | 3.69E+08 | DSM 4420 |
19 | Roseburia hominis A2-183 | 3,592,125 | 4 | 2.58E+08 | DSM 16839 |
20 | Methanobrevibacter smithii d | 1,704,865 | 1 | 5.43E+08 | DSM 2375 |
Generating and sequencing 16S rRNA gene sequence amplicons
Primers used in this study
Primer name | Sequence (5′–3′)a |
---|---|
i5+V3 | AATGATACGGCGACCACCGAGATCTACAC ATCGTACGTATGGTAATTCAATTACCGCGGCTGCTGG |
i7+V1 | CAAGCAGAAGACGGCATACGAGAT AACTCTCGAGTCAGTCAGCCGAGTTTGATCMTGGCTCAG |
For.seq.V3 | TATGGTAATTCAATTACCGCGGCTGCTGG |
Rev.seq.V1 | AGTCAGTCAGCCGAGTTTGATCMTGGCTCAG |
Index.V1 | CTGAGCCAKGATCAAACTCGGCTGACTGACT |
Data analysis
The mothur analysis package was used to assemble contigs, align sequences, trim sequences, remove chimeras (UCHIME, [3]), and remove non-bacterial sequences (mothur versions 1.33.3 and 1.34.0, http://www.mothur.org/wiki/MiSeq_SOP, [2, 12]). Following these quality-control steps, the data were rarified to 6654 sequences/sample and analyzed in three ways: (1) all sequences together, (2) with cross-sample singletons removed, or (3) with cross-sample singletons and doubletons removed. Cross-sample singletons and doubletons were defined as sequences that occurred only once (singletons) or twice (doubletons) among all samples. Taxonomy was assigned by aligning to mothur’s implementation of the SILVA database [13]. OTUs were clustered at 97 % similarity and analyzed in mothur for community metrics. Richness, evenness, and diversity were also conducted in mothur and included the use of the program Catchall [14]. Error analysis was performed in mothur based on the alignment of the experimental mock community to a FASTA file containing the actual 16S rRNA gene sequences from the genome sequences (Additional file 1). A complete list of the commands executed in mothur is available (Additional file 2).
Results and discussion
Sequencing the V1–V3 region yields spurious sequences
Cluster size versus frequency plot. The log10 of the cluster size (logCt) (i.e. number of sequences in each cluster) plotted against the log10 of the frequency of cluster membership sizes (i.e. the frequency of clusters that contained n sequences, where n is the cluster membership size) found among all mock community samples. Plots of all mock community data (a), data with cross-sample singletons removed (b), and data with both cross-sample singletons and doubletons removed (c) are shown. Red line regression line, blue line lowess fit line, SE standard error, r2 coefficient of determination
Removal of low-frequency sequences improves microbiota assessments
Average diversity estimates of the mock community (n = 12, rarified to 6654 sequences per sample) with and without removing low-frequency sequences
Mock community | Actual number of OTUsa | Observed number of OTUs | Estimated total number of OTUsb | Chao diversity index | Shannon diversity index | Inverse Simpson index | Error rate (%) | File size (Gb)c |
---|---|---|---|---|---|---|---|---|
All sequences | 20 | 734 ± 56 | 374,770 ± 214,807 | 21,676 ± 3273 | 3.6 ± 0.1 | 18 ± 0.8 | 3.6 | 41 |
Singletons removed | 20 | 28 ± 0.8 | 68 ± 13 | 41 ± 3 | 2.7 ± 0.02 | 12 ± 0.3 | 1.4 | 21 |
Single and doubletons removed | 20 | 22 ± 0.3 | 22 ± 0.3 | 23 ± 0.7 | 2.6 ± 0.02 | 12 ± 0.3 | 1.3 | 3 |
Removing the large number of clusters with a membership size of one sequence (or one and two sequences) has a distinct advantage for streamlining the clustering of sequences into OTUs by reducing the size of the dataset without detracting from the ability to make valid comparisons. Reducing the number of sequences resulted in a 13-fold decrease in file size and exponentially reduced the time and memory space needed to create the distance matrix since the distance matrix is comprised of pairwise comparisons (Table 3).
Average relative abundance of bacterial genera in 12 replicates of the mock community
Conclusions
Discarding low-frequency sequences can be concerning for those interested in the rare members of a bacterial community. Indeed, defining the rare microbiota in the context of sequencing errors has been explored [18–20]. Other work has supported the value of singleton removal [21, 22]. Our method is beneficial to researchers that use the Illumina MiSeq to sequence longer amplicons, such as the V1-V3 region of the 16S rRNA gene, from hundreds of samples. Removing the singletons and doubletons will suit the vast majority of projects seeking to analyze the abundant (>1 %) community organisms to draw biological conclusions.
Declarations
Authors’ contributions
HKA analyzed data and drafted the manuscript. DOB, TL, JT, and BB performed experiments or analyses. SMDB, TN, and TAC provided strains. All authors contributed to and finalized the manuscript. All authors read and approved the final manuscript.
Acknowledgements
The authors thank Stephanie Jones, Lisa Lai, and Sarah Shore for outstanding technical support. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the US Department of Agriculture. USDA is an equal opportunity provider and employer.
Competing interests
The authors declare that Diamond V, manufacturer of all-natural, microbial, fermentation-based feed additives, partially funded this work.
Availability of data and materials
The dataset supporting the conclusions of this article is available in GenBank’s Short Read Archive (SRP076629) under Bioproject PRJNA325813. The Cloacibacillus genome is available in GenBank under BioProject PRJNA335387.
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.
Authors’ Affiliations
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