What is OTU Clustering?
Operational Taxonomic Units (OTUs) play a pivotal role in microbial taxonomy. The determination of the distance or similarity between distinct sequences involves the application of specific distance metrics. Subsequently, a designated classification threshold is established to generate a uniform distance matrix. The clustering process is then executed under this predefined threshold, giving rise to diverse taxonomic units.
Operational Taxonomic Unit (OTU) clustering is a fundamental concept frequently employed in microbiology research. To unravel the intricate details of species composition diversity within samples, it becomes imperative to cluster valid sequences. The pristine tags undergo clustering into OTUs, typically at a default similarity threshold (commonly set at 97%). Each resulting cluster is then assigned a distinct random number, serving as its unique OTU ID. This process allows for a comprehensive analysis of the sequences within each cluster, shedding light on the diverse microbial communities present in the samples.
CD Genomics analyzes microbial data to classify species, determine abundance, and explore relationships with the environment. We provide insights into species composition, conduct gene prediction, and compare gene abundance and metabolic differences across samples.
Why Use OTUs in 16S Sequencing?
With high-throughput sequencing generating thousands of 16S sequences, individually annotating each sequence for species identification becomes a laborious and time-intensive task. Additionally, errors during the 16S amplification and sequencing stages can compromise result accuracy. The integration of OTUs in 16S analysis addresses these challenges. Initially, similar sequences are clustered into a reduced number of taxonomic units, streamlining the workload and enhancing analytical efficiency. Subsequent species annotation is then conducted based on these taxonomic units. This approach not only simplifies the analysis process but also mitigates sequencing errors by eliminating erroneous sequences during clustering, ultimately improving the overall accuracy of the analysis.
Species annotation and taxonomic analysis demo – CD Genomics
How OTUs Are Obtained from Sequence Information?
OTU clustering encompasses diverse methodologies, including Uclust, cd-hit, BLAST, mothur, usearch, and prefix/suffix, all of which are seamlessly integrated into the QIIME software. While each clustering method employs distinct algorithms, resulting in varied outcomes, the fundamental clustering process remains consistent across methodologies.
Singleton OTU and Chimera Removal
Singleton Operational Taxonomic Units (OTUs), characterized by having only a single representative sequence, are commonly attributed to sequencing errors or PCR-induced chimeras in high-throughput sequencing. During the clustering process, the usearch algorithm systematically eliminates singleton OTUs and chimeras utilizing the UCHIME algorithm. This meticulous approach ensures the generation of a refined set of Effective Tags, free from the influence of potential artifacts, and thereby enhances the accuracy of downstream analyses.
Selection of OTU Representative Sequences
Optimizing downstream analysis efficiency involves the extraction of a single representative sequence from the multitude within each Operational Taxonomic Unit (OTU). Qiime, a tool employed for this purpose, facilitates the enumeration of sequence counts and species annotations for each OTU. Three distinct methods guide the selection of representative sequences: choosing the sequence with the highest frequency of occurrence, opting for the longest sequence, or employing a random selection. By default, the first method, based on the highest frequency of occurrence, is applied for its practical utility in this critical step.
