Oligotyping (sequencing)

Oligotyping is a computational method used in microbial ecology to resolve complex mixtures of closely related DNA sequences, particularly 16S rRNA gene amplicons, beyond the resolution offered by traditional operational taxonomic unit (OTU) clustering approaches. It aims to identify and differentiate subtle but biologically meaningful variations within highly conserved regions of DNA, often single-nucleotide differences, that are masked by sequencing errors and inherent limitations of standard clustering algorithms.

Unlike OTU clustering, which groups sequences based on a predefined similarity threshold (e.g., 97% identity), oligotyping leverages the information content of each nucleotide position in the sequence. It decomposes the sequence variation into a set of informative nucleotide positions, termed "oligotypes," that are then used to distinguish between different biological entities. This process typically involves identifying positions with high Shannon entropy, indicating greater variability, and then using these positions to partition the sequences into distinct oligotypes.

The fundamental principle of oligotyping is that true biological variation often manifests as consistent patterns of nucleotide differences across multiple sequence reads. In contrast, sequencing errors are generally random and do not exhibit consistent patterns. By focusing on informative nucleotide positions and using statistical methods to filter out noise, oligotyping can reveal finer-scale taxonomic resolution than OTU clustering.

Oligotyping involves several key steps:

1. Sequence Alignment: Input sequences, typically amplified 16S rRNA gene sequences, are aligned to a reference sequence or de novo aligned.

2. Entropy Calculation: Shannon entropy is calculated for each nucleotide position in the alignment. High entropy positions indicate higher variability and potential for distinguishing different oligotypes.

3. Oligotype Partitioning: Sequences are partitioned into distinct oligotypes based on the nucleotides present at the high-entropy positions. This partitioning is often performed using a decision tree-based approach.

4. Error Filtering: Statistical methods are applied to filter out oligotypes that are likely to be the result of sequencing errors. This step is crucial for minimizing false-positive detections.

5. Oligotype Abundance Analysis: The relative abundance of each oligotype is determined, and these abundances are used to analyze the composition and dynamics of the microbial community.

Oligotyping is particularly useful for analyzing microbial communities where subtle taxonomic differences are important, such as in studies of closely related bacterial strains or in environments with high microbial diversity. It provides a more sensitive and specific method for identifying and quantifying different microbial populations compared to traditional OTU clustering approaches.