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We have used CS to determine whether the rates of chimera formation differed when these separate regions were PCR amplified. These comparisons had not been previously performed, in part because of the lack of effective methods for detecting chimeras in large numbers of short reads. The amplification of these shorter windows showed differential bias resulting in non-uniform species abundance estimates Supplemental Fig. The distribution of chimera breakpoints observed in full-length clones did not readily explain the higher frequency for certain organism pairs and windows.
Further, among the organisms in our mock community, we do not find evidence for differential CS sensitivity in detecting chimera abundance by organism pair or region. However, two notable chimera pairs eluded detection due to insufficient sequence variation: More similar 16S genes clearly form chimeras more readily. When we mitigated sequence abundance effects by considering only cases where a less-abundant species formed chimeras with a more abundant species, we observed a strong positive correlation between the percent identities shared by the 16S sequence of chimera pair species and the percent of chimeras observed.
Thus, the abundance of chimeras corresponding to a given genus appeared to be a reflection of both the degree of 16S sequence identity and the abundance of sequences from organisms within the genus. Correlation of chimera content with sequence homology and organism abundance. A Percent of other organism abundance corresponding to chimeras with the indicated more abundant species y -axis , plotted according to percent identity x -axis between homologous 16S genes.
B Number of chimeric sequences corresponding to a given genus were plotted as a function of total genus-level classified reads for the even eMC and staggered sMC mock community. C Percent of sequences that correspond to chimeras for each genus plotted according to genus-level sequence abundance. Error bars correspond to standard error from the mean based on four technical replicates. To further test the hypothesis that more abundant organisms form chimeras more readily, we used another mock community sMC [ s taggered m ock c ommunity] containing the same species, but with 16S template concentrations staggered across four orders of magnitude Supplemental Fig.
Interestingly, the same chimera often appeared in multiple, independent amplifications.
This chimera pair was generated in each of four experimental replicates, and exemplifies the reproducibility of chimera formation and breakpoint occurrence across multiple PCR reactions. Only columns from the NAST multiple alignment containing nonidentical nucleotides between the reference sequences top and bottom are shown. Nucleotides matching Streptococcus sequences are colored red.
Sequence prefixes correspond to the four experimental replicates A—D. Chimeras are clearly a hindrance to the accurate discovery of novel organisms in PCR-based 16S surveys. These surveys randomly sample every DNA sequence present, and sequences corresponding to 16S can be retrieved and analyzed separately. Although methods involving WGS metagenomic sequencing can involve PCR amplification steps, they are not directed to specific gene targets, and so chimera formation would be expected to be minimal.
These 16S reads were examined using CS and no reads were flagged as potential chimeras.
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The essential function of the 16S gene, and its highly conserved sequence and structure, has made it the molecule of choice for studies of microbial evolution and ecological surveys Pace ; Tringe and Hugenholtz The many highly conserved regions spanning the length of the gene enable the amplification of sequences from a broad range of species. These same highly conserved regions, however, contribute to cross-hybridization and mispriming events during amplification that create chimeric sequences. Although the majority of chimeras form between closely related sequences, organisms across different phyla can form chimeras, and these are most likely to be classified as novel organisms if not properly identified as aberrant.
Properly identifying chimeric 16S sequences is a challenging computational problem. In evaluating chimera detection accuracy of the widely utilized Pintail and BellerophonGG algorithms, we found them to vary considerably, with BellerophonGG capable of recognizing chimeras mostly restricted to the most divergent sequence pairs. Although the Pintail algorithm has excellent chimera detection capabilities in full-length sequences, it has little sensitivity for detecting chimeras in shorter sequences.
Our new CS tool is the only method currently capable of sensitive chimera detection in short 16S sequence reads.
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However, perfect chimera detection is still an unsolved problem. Although CS is largely robust to varying sequence characteristics including divergence and length, detection accuracy does begin to degrade with increasing divergence to reference sequences. This underscores the importance of obtaining and validating sequences that represent novel bacterial diversity and continuing to expand upon the reference database leveraged by CS and additional analysis tools.
Also, CS is designed to detect only the simplest form of chimeras, involving two homologous parental sequences. More complex chimeras and sequence anomalies may evade detection. Given that chimeric sequences can be rare and diverse, the problem of identifying rare species correlated with disease or other important microbial ecosystem function remains challenging.
- Chimeric 16S rRNA sequence formation and detection in Sanger and pyrosequenced PCR amplicons.
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Shorter PCR products targeted to windows of the 16S gene were surprisingly rife with chimeras. The high chimera rates within short PCR products targeted to next-generation DNA-sequencing technologies indicates the continued importance of chimera screening in such sequence surveys and the need for tools such as CS that are capable of detecting chimeras in short reads.
Cumulative chimera rates, as often cited in previous studies, grossly understate the magnitude of the chimera problem. Cumulative rates can be heavily biased toward the most abundant species in the sample. Sequence reads from previously known organisms tend to be well classified by existing methods Wang et al. By restricting evaluation of sample diversity to those sequences classified at high confidence, chimeras appeared to minimally affect estimates of diversity via taxonomic binning Supplemental Text S6, S7 , with the caveat that low-abundance taxa should be treated with skepticism.
This implies that the often-suggested criterion for trusting a novel sequence—that it appear in multiple samples or experiments Kunin et al. Even when applying PCR to harvest 16S sequences from clonal species, one must be very careful in analyzing such sequences, since even low levels of contaminating microbes can result in chimeric PCR products data not shown. The goal of chimeric 16S detection tools should be to identify likely unnatural artifacts, such as chimeras resulting from PCR amplification, and to avoid flagging sequences that correctly represent biology and evolution.
Including such naturally occurring chimeric sequences in the reference set ensures that query sequences with best alignments to naturally chimeric reference sequences are not flagged inappropriately. Since the reference collection of 16S sequences does not represent all of the bacterial diversity, putative intra-genus chimeras identified in sequence surveys should be treated with skepticism since many may represent genuine sequence diversity and naturally occurring chimeras.
The predicted intra-taxon chimera type is reported in the output of CS so that researchers can make informed decisions regarding the types of chimeras that may deserve special attention. For example, retaining intra-genus chimeras for subsequent analyses such as taxonomic binning may be warranted, but defining new organisms based on sequence clustering should proceed with caution, especially given that chimeras reproducibly form across multiple experiments. In addition to pursuing advancements in detection of chimeras once they are formed, there is a need to identify experimental conditions that are least conducive to chimera formation Wang and Wang ; Thompson et al.
Our investigation into the effects of multiple PCR conditions on the observed prevalence of chimeras among pyrosequences and Sanger-sequenced clones supports a dominant effect of amplification cycle number Supplemental Text S8. By limiting the number of amplification cycles to the fewest number needed to produce yields required for sequencing, one can mitigate the relative yield of chimeric sequences.
Although we detect minimal chimeras formed at 20 cycles, earlier studies observed near peak chimeras formed at 20 cycles Wang and Wang Capturing the amplification product at a time where yield is maximized and chimeras are minimized will likely depend on the PCR protocol utilized. Further exploration of PCR conditions, such as by leveraging single molecule amplification in oil emulsions, could prove highly advantageous Williams et al. We were unable to detect chimeric 16S sequences in our pyrosequencing WGS experiment, suggesting that WGS is relatively chimera free.
However, the concentration of 16S reads in this data set was very low 0. Ultimately, the small number of 16S sequences generated by the WGS approach suggests that pursuing WGS methods as an alternative to directed 16S sequence surveys to specifically mine 16S data is neither efficient nor cost effective. Perhaps, as costs of sequencing continue to plummet, WGS methods will become a viable alternative to directed 16S sequence surveys.
Until then, optimizing PCR conditions to mitigate chimera amplification and leveraging tools such as CS to flag suspect sequences should help minimize the impact of such artifacts on related microbiota research. It is also important to note that chimeras are only one source of diversity artifacts. Even with filtering of chimeras, the appearance of unique sequence clusters occurs at a high rate when compared with known sample diversity.
This is particularly true for reads generated using pyrosequencing as compared with the Sanger-generated reads; thus, the effects of sequencing error and other anomalies cannot be ignored Quince et al. Additional studies leveraging controlled mock communities should help clarify insights into the true diversity represented within the rare biosphere. To generate the even and staggered mock communities, DNA from each organism was mixed according to the calculated 16S concentration. In the even community, the 16S concentration from all organisms was normalized so that each organism contributed a calculated number of , 16S molecules to each amplification reaction.
In the staggered mock community, species were present in one of four concentrations calculated to contribute either 10 3 , 10 4 , 10 5 , or 10 6 16S molecules per reaction.
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The strains and the molecules per staggered reaction are as follows: Forward primers contained the B adapter and the reverse primers contained the A. The 16S-specific sequence with adapters were as follows: All three PCR products were normalized to the same molecule concentration 1.
Emulsion PCR and sequencing were performed according to the manufacturer's specifications. Because of sequencing bias likely due to hairpin formations with the adapter and forward 16S primer, we restricted our analyses to sequences derived from the reverse 16S primer. Counts of pyrosequenced reads analyzed are included in Supplemental Table S1.
A query sequence was aligned to a NAST-formatted reference sequence or set of NAST-formatted reference sequences , and gap insertion was restricted to the query sequence in generating the global optimal alignment. End-gaps in the aligned query sequence were not penalized because the subject sequences were usually partial , and regions of the query sequence that extended beyond the boundaries of the NAST-formatted reference sequence s were excluded in order to maintain the fixed width; this was particularly useful in the case where the query included unaligned vector or low-quality sequence at its ends, which in many cases became excluded from the resulting alignment.
When a query was aligned to a set of multiple reference sequences, a profile was constructed based on the multiple reference sequences, and alignment scores were computed by summing all match and mismatch scores within a position of the alignment. Pre-existing gap characters in the NAST-formatted reference sequences were not penalized when aligned to a gap inserted in the query. The global dynamic programming algorithm with a fixed width profile P and unaligned query sequence Q was defined by the following recursion: The optimal scoring alignment was chosen as max[ F i , j ], where i was the position of the last position in the NAST alignment profile.
The query sequence was aligned to this profile as described above.
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Generating a NAST alignment for a single query sequence, including performing the reference sequence database search, takes on the order of one second per sequence on an average desktop computer. A database of what was expected to be mostly chimera-free sequences was compiled from two sources: A large overlap exists between the sequences derived from these two sources, and so CD-HIT Li and Godzik was used to retrieve the longest nonredundant reference sequence requiring The resulting reference database consisted of sequences, corresponding to type strains, and the remaining derived from complete or draft genome sequences.
The complete taxonomy of each sequence, including domain, phylum, class, order, family, and genus was predicted using the RDP Bayesian classifier Wang et al. Simulated chimeric 16S sequences were constructed by joining two immediately adjacent segments of a pair of NAST-formatted reference sequences. A random breakpoint was selected from the range of the NAST alignment columns between the positions corresponding to and in the E. At least 50 nucleotide characters G, A, T, or C were required on each side of the breakpoint. The disparate sequence regions from each side of the breakpoint were joined to create a simulated chimera.
The pair of reference sequences from which the chimera was derived is referred to as the parents. The divergence between the parents is referred to as the chimera-pair divergence. Pairs of parental reference sequences to be joined into a chimera were randomly selected based on differences at each level of their taxonomy intra-phylum chimeras down to intra-genus chimeras. Smaller length simulated chimeras were constructed similarly according to the targeted unaligned sequence lengths. Simulated sequence divergence was performed by randomly selecting a position within the NAST-formatted chimera sequence and introducing a mismatch, insertion, or deletion, as specified.
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