In order to analyze targeted microbiome sequencing, one should use the right approach to characterize the origin of the sequence. Straying away from typical alignment-based methods is recommended when opting for this approach. The reason is that the gene of origin in an amplified target gene is known, and the actual objective is the determination of its taxonomic origin. This approach is dependent upon a relatively small number of variations that are related to the same taxa.
Whereas, in terms of whole-genome sequencing, a limited number of single nucleotide variations (SNVs) induced by sequencing mistakes are unlikely to confound an aligner and have little effect on the sequence’s final attribution in the process of whole-genome sequencing.
A targeted sequencing, on the other hand, involves the comparison of more than one similar sequence instead of aligning multiple genomes in the main operation. This type of sequence can be mixed up with erroneous SNVs. As a result, wrongful attribution of the sequence that either detects a similar but incorrect organism, as well as the fake invention of a novel organism, might occur. If you run a lab and need to stock up on Rainin LTS filter tips, Molecular Biology Products is the best place to approach.
To counter the above-mentioned drawbacks of targeted sequencing, two strategies are used. These work to reduce the effects of errors that are likely to occur during this type of sequencing. Each has its own pros and cons. We have created this blog to get you familiarized with the applications, steps, and features of each.
All About OTU Clustering:
Clustering is a method that was developed initially in order to counter the risks of sequencer errors associated with targeted sequencing. The method is designed based on the idea that organisms that are related or similar to each other have the same target gene sequences. This also means that rarely occurring sequencing mistakes will have a meaningless impact on the consensus sequence generated for the operating taxonomic units(OTUs).
Three methods can be applied to extract OTUs from sequencing data. With a 97 percent sequence identity in the similarity threshold, the clusters in these methods are identified. However, this approach can lead to multiple species getting grouped together under the identity of a single OTU. This way, the individual identity of each is lost to the cluster abstract. One can apply high levels of sequence identity to eliminate the risk of losing assortment in clustering. You can also opt for a hundred percent threshold to counter this effect, but this can lead to difficult identification of sequencing errors as a new species or a false diversity.
All About ASV Analysis:
ASV analysis is unlike the OTU clustering approach that minimizes the influence of sequencing errors by blurring similar sequences into a consensus sequence. The Amplicon Sequence Variant or ASV is a contrasting approach. This method performs the exact identification of the read sequences in addition to the number of times each was read. The data is collected and mixed with an error model to run the sequence.
This helps perform a comparative analysis of similar reads to bring out the probability of a given sequence getting impacted by a sequencing error. The reading frequency of each sequence helps in error detection. This way, you will get a p-value for each sequence in which the null hypothesis is equal to the sequence in its exact state, but generated as a consequence of sequencing error.
Following this technique, the sequences get distinguished based on a threshold value for assurance. This leaves us with a group of exact sequences that are characterized by a statistical representation. ASV results are comparable for multiple studies that have used the same target region due to the involvement of exact sequences. (Microbiome Informatics: OTU Vs. ASV, 2020)
While the OTU was the first developed approach to bring convenience for the microbiome community and has served the purpose for many years, the ASV is expected to become the future of target sequencing. With a number of mature bioinformatics applications for the analysis of errors, the approach will provide ease of comparison between various studies. Whereas the OTU clustering method will also remain functional in specific processes.
To sum it up, each approach has its own applications and requires appropriate equipment to carry out the procedure. If your lab is in need of laboratory equipment such as filer tips for Rainin LTS, instruments for OTU or ATV analysis, or cell culture plates and pipettes, get in touch with Molecular Biology Products.
Microbiome Informatics: OTU vs. ASV. (2020, May 28). Zymo Research. Retrieved March 9, 2022, from https://www.zymoresearch.com/blogs/blog/microbiome-informatics-otu-vs-asv