Amplicon 16S Classification

16S Metagenomics

For taxonomic profiling based amplicon 16S data, the CosmosID 16S data analysis pipeline starts with preprocessing of the raw reads from either paired-end or single-end Fastq files through read-trimming to remove adapters as well as reads and bases of low quality. If the reads are in a paired-end format, the forward and reverse overlapping pairs are joined together; the unjoined R1 and R2 reads are then added to the end of the file. The file is then converted to Fasta format and used as input for OTU picking. OTUs are identified against the CosmosID curated 16S database using a closed-reference OTU picker and 97% sequence similarity through the QIIME framework. The final results are then presented in tabular format with the taxonomic names, OTU IDs, frequency, and relative abundance. Results can be downloaded or compared to other 16S samples for visualizations through the CosmosID Comparative Analysis tool.

The CosmosID-HUB Microbiome’s 16S workflow implements the DADA2 algorithm(3) as its core engine and utilizes the Nextflow ampliseq pipeline(1) definitions to run it on our cloud infrastructure. Briefly, primer removal is done with Cutadapt (4), and quality trimming parameters are passed to DADA2 to ensure that the median quality score over the length of the read exceeds a certain Phred score threshold. Within DADA2, forward and reverse reads are each trimmed to a uniform length based on the quality of reads in the sample—higher quality data will generally result in longer reads. DADA2 uses machine learning with a parametric error model to learn the error rates for the forward and reverse reads, based on the premise that correct sequences should be more common than any particular error-variant. DADA2 then applies its core sample inference algorithm to the filtered and trimmed data, applying these learned error models. Paired-end reads are then merged if they have at least 12 bases of overlap and are identical across the entire overlap.

The resulting table of sequences and observed frequencies is filtered to remove chimeric sequences (those that exactly match a combination of more-prevalent “parent” sequences). Taxonomy and species-level identification (where possible) are conducted with DADA2’s naive Bayesian classifier, using the Silva version 138 database.

Lastly, the predicted functional potential of the community was profiled using PICRUST2 (5)(6)(7)(8)(9). Briefly, PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) is a tool that predicts functional capabilities and abundances of a microbial community based on the observed amplicon (marker gene) content. Functional capabilities are given by EC classifiers, or MetaCyc ontologies, and these can be aggregated to predict pathways that are likely present in a given sample.

What is a primer sequence and why is it needed to for 16S workflow?

Primers are short, artificial DNA strands of about 18 to 25 nucleotides that match the beginning and end of the DNA fragment to be amplified. In amplicon sequencing methods, PCR with specific primers produces the amplicon of interest. These primer sequences need to be trimmed from the reads before further processing and any downstream analytics. Please do not use any technical sequence such as adapter sequences but only the primer sequence that matches the biological amplicon. For example:

--Forward Primer "GTGYCAGCMGCCGCGGTAA" --Reverse Primer "GGACTACNVGGGTWTCTAAT”

When do you use Stringent vs Relaxed Trimming Results ?

For stringent results, a Phred quality score of 25 is used to ensure that the reads retained maintain a median quality score of 25 over the length of the read along with a maximum expected error of 2 bases per read. In our high-confidence/stringent/filtered results, reads will only be retained if they meet this standard, so with low-QC sequencing data, no reads may be returned for some samples. The more relaxed standard guarantees that at least 50% reads will be retained for each sample, but if there are not enough reads meeting the Phred score quality threshold of q25, progressively lower quality reads will included in the results to meet that minimum.

What is an ASV?

An amplicon sequence variant (ASV) is any one of the inferred single DNA sequences recovered from a high-throughput sequencing analysis of marker genes. Because these sequences are created following the removal of erroneous sequences generated during PCR and sequencing, using ASVs makes it possible to distinguish sequence variation by a single nucleotide change. The uses of ASVs include classifying groups of species based on DNA sequences, finding biological and environmental variation, and determining ecological patterns.

What does the “ASV count” represent on the single sample taxonomic results explorer?

The ASV count represents the number of observations of an ASV in that sample.

What does the “raw count” represent on the single sample functional results explorer?

The raw count represents the read depth per ASV multiplied by the predicted function abundances per ASV.

Why would you choose ASVs methods over OTUs methods for inferring taxonomic composition from your 16S data?

OTU clustering reduces resolution by clustering similar sequences over a certain threshold into one specific sequence or one specific consensus sequence. ASVs capture the fine scale variation that exists and allows more sensitivity and thus the ability to get closer to “True” taxonomic composition

The advantages of ASVs over OTUs

  1. There are many arguments in the microbiome community to move 16S analysis approach to ASV based methods (10)

  2. ASV methodology have showcased sensitivity and specificity as good or better than OTU methods and allow better discrimination of ecological patterns (10)

  3. ASV methods are reproducible since these are exact sequences, generated without clustering or reference databases (10)

  4. OTUs often overestimate bacterial richness when compared to ASVs (10)

What does the “percentage of reads retained” imply?

You can track how many reads were retained and rejected through each step in the 16S workflow. It’s normal to lose some reads during each step, but large drops at particular steps can indicate specific issues with the data. For example, if a large number of reads are lost during filtering, it could indicate poor sequence data overall, while a major decrease at the chimera-removal step frequently indicates an issue with primer removal, so we suggest confirming the primer sequences are correct and adapters are trimmed.

How do you upload your 16S amplicon data to the Hub?

You have the option to upload your data from your desktop or your Illumina BaseSpace account or from NCBI SRA as well.

How do you view your results for your uploaded Amplicon 16S data?

All the uploaded data are available in the dashboard to be viewed in the single sample explorer.

How do you generate comparative analysis to aggregate and compare your 16S results across cohorts?

Results can be downloaded or compared to other 16S samples for visualizations through the CosmosID Comparative Analysis tool.

References:

  1. Straub, D. et al. Interpretations of Environmental Microbial Community Studies Are Biased by the Selected 16S rRNA (Gene) Amplicon Sequencing Pipeline. Front. Microbiol. 11, 1–18 (2020).
  2. Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).
  3. Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
  4. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10 (2011).
  5. Douglas, G. M. et al. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 38, 685–688 (2020).
  6. Barbera, P. et al. EPA-ng: Massively Parallel Evolutionary Placement of Genetic Sequences. Syst. Biol. 68, 365–369 (2019).
  7. Czech, L., Barbera, P. & Stamatakis, A. Genesis and Gappa: processing, analyzing and visualizing phylogenetic (placement) data. Bioinformatics 36, 3263–3265 (2020).
  8. MIRARAB, S., NGUYEN, N. & WARNOW, T. SEPP: SATé-Enabled Phylogenetic Placement. in Biocomputing 2012 247–258 (WORLD SCIENTIFIC, 2011). doi:10.1142/9789814366496_0024.
  9. Louca, S. & Doebeli, M. Efficient comparative phylogenetics on large trees. Bioinformatics 34, 1053–1055 (2018).
  10. Ye, Y. & Doak, T. G. A Parsimony Approach to Biological Pathway Reconstruction/Inference for Genomes and Metagenomes. PLoS Comput. Biol. 5, e1000465 (2009).
  11. Chiarello, M., McCauley, M., Villéger, S. & Jackson, C. R. Ranking the biases: The choice of OTUs vs. ASVs in 16S rRNA amplicon data analysis has stronger effects on diversity measures than rarefaction and OTU identity threshold. PLoS One 17, 1–19 (2022).