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Will the Fed lose its patience this week? For the paired reads, the reads are split into their R1 and R2 components by looking up the corresponding reads in the R1 and R2 libraries, and then aligned separately using Infernal. Alignment scores are not inspected since the reads are free of contaminants by construction. The unmapped reads are presumed to be either chimeric reads or singletons distant to any reported OTU.

Empty columns are removed from the alignments of the OTU representatives, and the trees are calculated using FastTree [29]. These trees are not suitable for direct comparison of the phylogenetic structure between the different libraries, since the OTU picking procedure may choose different reads as representatives, as well as a different number of OTUs. To solve this, we took as a reference the trees created for the full-length reads, created the equivalent libraries with R1, R2 and paired reads and re-run the alignment and calculate the new trees.

This way, the trees will have the same number of leaves, same leaf IDs and hence directly comparable. For each of the synthetic communities, we took the resulting BIOM files and phylogenetic trees for all four libraries, R1, R2, paired and full-length , and calculated the unweighted UniFrac distance matrix [31] between the 20 samples of each community, using QIIME 1.

We compared the distance matrices of the libraries R1, R2 and paired to the corresponding full-length distance matrix using a Mantel correlation test, as implemented by the R package vegan [39] see Fig.

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For each of the synthetic communities, we took the full-length tree and the equivalent and comparable trees created using libraries R1, R2 and paired, and calculated the cophenetic distance matrix which measures the distances between leaves of the phylogenetic tree through branch lengths , as implemented in the R package ape [40].

The distance matrices were compared using a Mantel correlation test, as implemented in the R package vegan [39] see Fig. As another validation of our approach, we used an existing set of read libraries from stool samples to create a synthetic mock community based on real gastrointestinal microbiome data, for a more realistic case testing.

Written consent was provided by all subjects. A total of 20 multiple sclerosis patients were consented under Mayo Clinic IRB and stool samples were collected either in clinic or using a specimen mailer tube. After verifying there were no other large bands besides the amplicon, purification was carried out using magnetic beads. Concentrations for each sample were then diluted to 10 nM and pooled for sequencing. Using PrimerProspector [37] we extracted bp long V3 and V5 paired reads from the matches, and also created an almost-full-length 16S library by trimming their read lengths down to bp long.

We multiple sequence aligned the OTU representatives to a model secondary structure of the 16S gene using Infernal 1. We compared the matrices of the three short libraries R1, R2 and paired against the full-length UniFrac matrix using a Mantel correlation test, as implemented in the R package vegan [39]. All the data created for the sample synthetic mock community, including original reads and synthetic reads, resulting OTU tables and scripts, are available for download at the Dryad repository.

We have shown that the use of paired reads improves performance over R1 or R2 alone. Moreover, significance tests revealed that in all cases R1 and R2 together almost always provided a better reflection of the results from full length 16S rDNA. By itself, this fact is unsurprising since R1 or R2 both use only a subset of the information available to paired reads, which are made up of both R1 and R2 together.

On the other hand, what may seem more surprising is that paired read approaches are not utilized as the norm. However, the overall gains from using paired reads is moderate when considering the enormous amount of book-keeping and tracking involved shown in Fig. It is clear that the travails of making maximal use of paired reads outweighs the benefit for many microbiome researchers. It is our view that the most valuable tools are also the most widely usable. Now more than ever, community standards within the field of microbiome research are becoming increasingly important as the field and the data continues to grow at a rapid pace.

Finally, as an important consideration in future studies, the ability to use non-overlapping paired end 16S rDNA reads means added flexibility to experimental design. This comes in direct contrast to overlapping paired read designs which require primers to be adjacent to each other.


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While overlapping primer design has potential advantages see Supplemental Materials and could in principle use nearly the same amount of information as non-overlapping reads, with a small number of bases given to assemble the reads, primer choice ideally would vary between studies due to emphasis on different taxa in particular environments.

Creating a means for the more holistic analysis of non-overlapping paired reads allows for greater flexibility in primer choice, allowing researchers to target a larger variety of regions to maximize sensitivity and specificity for a particular environment without using only adjacent regions or resorting to single end read analysis. This applies not only to 16S applications. For example, studies using the 18S rDNA gene [45] will benefit from a paired-end sequencing approach.

And these benefits extend to any other structurally alignable RNA molecule. Being able to utilize non-overlapping reads also ameliorates issues arising from poor sequencing quality and shorter than expected read lengths after quality trimming by enabling analysis of data from lower quality sequencing runs.

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As a quality metric for our particular implementation, we demonstrate the differences between the use of single versus non-overlapping paired-end reads. This is meant to address another possible methodology for data analysis in studies that utilize primer designs where there are non-overlapping paired-end reads [12] , [15]. Given the implied increase in accuracy when analyzing environmental data, it is evident that paired-end analysis should be used when possible in these cases. Taxonomy comparison. Accuracy of taxonomy assignments for different read lengths, with errors and read trimming.

The table shows the effect of base errors in the accuracy of the taxonomy assignment process Paired, with errors, average It also shows the improvement in accuracy after applying the read trimming step pf the pipeline Paired, trimmed, with errors, average 6.

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Comparison of OTU counts for synthetic mock communities. From the table we can observe the trend of Full length reads showing comparable or higher OTU counts than the short read types, and among these short reads, R1 shows the higher OTU counts. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. National Center for Biotechnology Information , U. PLoS One. Published online Dec Paul Jaak Janssen, Editor. Author information Article notes Copyright and License information Disclaimer.

Competing Interests: The authors would also note here that Prof. Received May 1; Accepted Oct This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

This article has been cited by other articles in PMC. Abstract Motivation 16S rDNA hypervariable tag sequencing has become the de facto method for accessing microbial diversity. Introduction The advent of high-throughput 16S rDNA microbial population sequencing has revolutionized our ability to carry out culture independent surveys [1] and paved the way to understanding the microbiome from environments ranging from the practical [2] , [3] to the exotic [4] , [5].

Taxonomy Determining taxonomy relies on profiling characteristics or alignments of sequences in a reference database. Table 1 Taxonomy comparison. Open in a separate window. Analyses were carried out using the Ribosomal Database Project taxonomy classifier with the complete Greengenes database.

Using Paired read analysis provides more accurate taxonomic classification than either R1 or R2 alone across all taxonomic levels. Full length 16S rDNA was used for comparison purposes. Multiple Sequence Alignment Before making a comparison of phylogenies, we must make sure we make a sensible choice for the tool used to create the sequence alignment used for the phylogenies. Figure 1. Comparison of alignment tools. Phylogeny Phylogenetic trees are often used as part of the metric of choice [31] when comparing organismal differences between communities. Figure 2. Comparison of phylogenetic trees between libraries.

Figure 3. Use Case The purpose of this use case test was to create a synthetic dataset that contains similar diversity and relatedness of organisms that might be found in a real dataset. Table 2 Library comparison in a realistic sample. Library type Mantel r statistic Paired 0. Mantel r statistic comparing the unweighted UniFrac matrices for different short-read 16S libraries against a full-length 16S library, based on a real paired-end libary sequenced from stool samples.

The paired reads have a higher correlation to the full-length library than any of the other single read libraries. Figure 4. Quality Filtering Demultiplexed sequence files are subject to quality filtering using Trimmomatic [32] version 0. Taxonomy Assignment Out of many possible ways of assigning taxonomy for pared-end 16S reads, we have decided to use k-mer-based methods, since, besides their speed and accuracy, they allow us to query reads with gaps in them marked as unknown nucleotides without loss of accuracy.

Structural Alignment After taxonomy assignment, reads R1 and R2 are aligned using Infernal version 1. Methods for Validation Using Synthetic Communities To asses the performance and accuracy of our choice of primers and tools when compared to full-length 16S reads, we used synthetic communities of 20 samples each, for two pairs of primers targeting regions V3—V5 and V6—V9. Input Data To create our synthetic communities, we started with the complete 16S Greengenes 13 — 5 database. Synthetic Communities For each of the primer pairs used for validation, we created synthetic communities, each with 20 samples.

Comparison of Phylogenies Between the Different Libraries for Validation For each of the synthetic communities, we took the full-length tree and the equivalent and comparable trees created using libraries R1, R2 and paired, and calculated the cophenetic distance matrix which measures the distances between leaves of the phylogenetic tree through branch lengths , as implemented in the R package ape [40]. Data Availability All the data created for the synthetic communities analysis, including reads, resulting OTU tables and scripts, are available for download from the Dryad repository.

Methods for Validation Using a Sample-Modeled Synthetic Mock Community As another validation of our approach, we used an existing set of read libraries from stool samples to create a synthetic mock community based on real gastrointestinal microbiome data, for a more realistic case testing. Data Availiability All the data created for the sample synthetic mock community, including original reads and synthetic reads, resulting OTU tables and scripts, are available for download at the Dryad repository.

Discussion We have shown that the use of paired reads improves performance over R1 or R2 alone. Supporting Information S1 Table Taxonomy comparison. PDF Click here for additional data file. GZ Click here for additional data file. Data Availability The authors confirm that all data underlying the findings are fully available without restriction. References 1. PLOS Biol 10 :e Nature — Hamady M, Knight R Microbial community profiling for human microbiome projects: Tools, techniques, and challenges.

Genome Res 19 — Nat Methods 7 — Appl Environ Microbiol 75 — ISME J 6 — Nat Methods 10 — Nucleic Acids Res 36 :e Mol Neurodegener 9 Appl Environ Microbiol 77 — BMC Bioinformatics 13 Appl Environ Microbiol 79 — Bioinformatics 29 — Appl Environ Microbiol 72 — Nucleic Acids Res 34 :W—W Bioinformatics 25 — Bioinformatics 26 — Appl Environ Microbiol 73 — Bioinformatics 30 — Nucleic Acids Res 37 :D—D GigaScience 1


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