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Order Now Free Inquiry. Calculate your paper price. Type of paper. Academic level. Michelle W. USA, New York. Your writers are very professional. Michael Samuel. The relationship between relative errors of the Simpson indices and the number of reads per sample can be explained by similar reasons as for the Shannon indices. For the V2-V3 region, the Chao1 index values varied from 51 to mean value , the ACE index values varied from 48 to mean value For the V3-V4 region, the Chao1 index values varied from 36 to mean value 91 , the ACE index values varied from 36 to mean value Statistically confirmed that V2-V3 region for metabarcoding studies of microbial communities gives greater resolution at low clustering thresholds the species level, 0.
Moving to analysis of phylotypes, we were interested in correlation of OTUs obtained using V2-V3 and V3-V4 fragments mapped to bacterial taxons of higher level, such as phylum, class, order and family. Analysis of Bray-Curtis distances which is semi metric distances index at the phylum level Fig.
There is also no clear clustering of samples on the heat map dendrogram. Consequently, number of reads per phyla in the paired comparison of samples by V2-V3 or V3-V4 regions did not differ much. With lower taxonomic ranks from class to family , differences between samples analyzed by V2-V3 or V3-V4 regions become larger Table 4.
Jaccard metric of distance which is metric distances index showed a result very similar to the analysis based on the Bray Curtis index Fig.
The close results of the Bray Curtis and Jaccard indices are related to the fact that data were normalized by the average number of reads per sample At lower taxonomic ranks, increase differences both in terms of the qualitative metric and the quantitative metric of distance. V2-V3 fragments — red pointer, V3-V4 fragments — blue pointer. Analysis of the V2-V3 region has produced lower average genetic distances than the V3-V4 fragment did 0.
A histogram of the pairwise distances for the V3-V4 fragment is skewed to the right compared to that for the V2-V3 fragment. The latter also exhibited a quicker increase in the genetic distance frequencies in the area of lower values. Thus, the fragment of the 16S rRNA gene that includes V2 and V3 regions accumulates mutations quicker than V3 and V4 regions do during early stages of bacterial speciation. In this work, species-level OTUs were detected at the 0. Therefore, V2-V3 16S rRNA fragments are better suited for distinguishing closely related species for example, species within a genus.
This is, in fact, the reason why there are and OTUs, respectively, although analyses of both fragments produce convergent Shannon diversity index estimates. Obviously, using V3-V4 fragments does not allow for distinguishing some species-level OTUs, erroneously merging them into a single species. We can therefore conclude that V2-V3 16S rRNA fragments are more appropriate for the metabarcoding works aiming at detecting species in the bacterial community. In a number of cases, the points corresponding to similar OTUs detected with different 16S rRNA fragments overlap precisely, in which case species are exactly matched.
In other, quite numerous, cases they do not overlap; often, there are no V3-V4-based OTUs close to those detected with V2-V3 fragments. This means that taxa detected using the V2-V3 region are genetically different from those detected with the V3-V4 fragment owing to differences in their genetic variance described previously. Our results show that targeting V2-V3 or V3-V4 16S rRNA fragments result in similar estimates of community diversity in metabarcoding studies as measured by the Shannon and Simpson indices.
From the first view, this means there is little advantage of one index over another in practice. Yet, Shannon and Simpson indices themselves or their comparisons do not tell much about how similar or different the communities are in their species composition Any diversity index used in this work, including Shannon and Simpson ones, depends on the number of species and the uniformity of their abundance or biomass.
The more species there are in the community and the more evenly their abundance is distributed, the higher Shannon and Simpson indices will be. Differences in the species spectrum the number of shared and non-shared species do not affect it. On the other hand, the analysis of the indices of the expected species richness such as Chao1 and ACE showed that the number of species identified by V2-V3 fragments is larger than V3-V4 fragments.
A different question is much more important for comparative ecology: are the species similar between samples or are they different? One of the most commonly used measures of community similarity, Bray-Curtis dissimilarity, does depend on the counts of shared and non-shared specimens in two communities. Therefore, in metabarcoding studies, imprecise estimates of the biodiversity with any index for example, Shannon are less important than possible artifacts of processing raw data into the lists of species.
Detection of species strictly speaking, species-level OTUs in metabarcoding is based on genetic distances and clustering methods. If a marker, for example, a 16S rRNA fragment turns out to be too conserved, the genetic distances will be too low, different species will be lumped into a single OTU, and the information regarding the differences in structure or functioning of the communities will be lost.
Two or more potentially different OTUs occupying different niches reacting variably to environmental differences between samples do not inform the researcher about differences between communities when they are merged into a single OTU. On the other hand, splitting a single species into several different OTUs will only lead to detection of several pseudotaxa similarly reacting to the environment, which does not impact ecological conclusions.
Our work shows that the V2-V3 fragment of the 16S rRNA gene is preferable for metabarcoding analyses as the V3-V4 fragment underestimates species diversity by merging several species into a single OTU. There are two arguments against this idea: first, lesser primer specificity would lead to decreasing the abundance of particular taxa, which in turn would disrupt the distribution uniformity and decrease the Shannon index.
Our results show that there is no significant difference between Shannon indices produced with either fragment, i. Second, the two pools of major OTUs produced with two primer pairs included different species.
The reasons behind these peculiarities of the bacterial taxonomic identification performed using different 16S rRNA fragments may be related to the functions of these fragments. A second class includes regions V3 and V7, of which the role in translation is currently understudied. According to their functions, regions of the first group should be the most conservative, followed by more variable V3 and V7, and finally by the quickest-evolving V2 and V8.
Regions of the first group will accumulate mutations slowly and, at the phylogenetic level, should be sufficiently distinct only in higher taxa, such as phyla and classes. Less conservative regions of the second group will be different between orders and families. The third class regions, V2 and V8, could distinguish genera within a family and species within a genus.
In our work, one of the fragments V2-V3 included one region from the third class and one from the second, and another V3-V4 included regions from the second and first classes, so it was reasonable to expect that regions of V2-V3 fragment will provide a better picture of species- or genus-level resolution than will V3-V4.
The problem of lower resolution of V3-V4 fragments at the species level can be solved by reducing the threshold of genetic distances which is used for OTU clustering. Some studies 51 , 52 suggest to reduce the clustering threshold to 1. However, here one may meet a number of problems. The accuracy of base call in Illumina technology is significantly less than that of Sanger method. Thus, by lowering the selection threshold by the species delimitation, one may come across the fact that new taxa will be distinguished due to sequencing errors.
The conclusions of this work are based on an analysis of bacterial communities of the same biotope from different individuals 38 females. In our study, there were also several samples Figs 1 and 2 where Simpson and Chao1 indices computed for V3-V4 data were greater than those for V2-V3 data.
The communities studied in our work were sampled from contrasting biotopes of the Lake Baikal ecosystem different depths in the water column and bottom sediment. The studied biotopes are characterized by different temperatures, concentrations of oxygen, organic matter, pH, mineralization and concentrations of biogenic elements. Therefore, the conclusions drawn from our research are likely more generally applicable than those from the work of 53 , although it is possible for some microbial communities V3-V4 fragment will better delineate the fine-grained community structure.
The bacterial communities consisted of taxa characteristic of freshwater lakes 54 and were similar to the community composition of other Baikal areas 17 , In the communities, we observed a high percentage of sequences of the phyla Actinobacteria and Bacteroidetes. The presence of these bacteria in the communities may be due to their active role in the destruction of the dying diatoms, which massively develop under the ice of Lake Baikal in the spring 55 , In their genomes, the key enzymes and pathways for effective degradation of at least two polysaccharides, disaccharides, and amino sugars were detected Choosing this fragment for the analysis allows for more precise separation of the read pool into species-level OTUs based on genetic distances.
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