Microbiome Analysis

7.

Taxonomic Analysis and Profiling

7.1.

Amplicon-Based Taxonomic Analysis

7.1.1.

Sequence Clustering Approaches

7.1.1.1.

Operational Taxonomic Units

7.1.1.1.1.

Similarity Thresholds

7.1.1.1.2.

Clustering Algorithms

7.1.1.1.3.

Representative Sequences

7.1.1.2.

Amplicon Sequence Variants

7.1.1.2.1.

Denoising Methods

7.1.1.2.2.

Error Correction

7.1.1.2.3.

Exact Sequence Variants

7.1.2.

Taxonomic Classification

7.1.2.1.

Reference Database Selection

7.1.2.1.1.

Greengenes Database

7.1.2.1.2.

7.1.2.1.3.

7.1.2.1.4.

7.1.2.2.

Classification Algorithms

7.1.2.2.1.

Naive Bayes Classifiers

7.1.2.2.2.

k-mer Based Methods

7.1.2.2.3.

Alignment-Based Methods

7.1.2.2.4.

Machine Learning Approaches

7.1.3.

Confidence Assessment

7.1.3.1.

Bootstrap Values

7.1.3.2.

Classification Confidence

7.1.3.3.

Taxonomic Resolution Limits

7.2.

Shotgun Metagenomics Taxonomic Profiling

7.2.1.

Marker Gene-Based Profiling

7.2.1.1.

Single-Copy Gene Markers

7.2.1.2.

Phylogenetic Markers

7.2.1.3.

Abundance Estimation

7.2.2.

k-mer Based Classification

7.2.2.1.

k-mer Database Construction

7.2.2.2.

Classification Speed

7.2.2.3.

Memory Requirements

7.2.3.

Read Mapping Approaches

7.2.3.1.

Reference Genome Mapping

7.2.3.2.

Alignment Quality

7.2.3.3.

Multi-Mapping Reads

7.2.4.

Profiling Tool Comparison

7.2.4.1.

7.2.4.2.

7.2.4.3.

7.2.4.4.

7.3.

Taxonomic Data Interpretation

7.3.1.

Abundance Metrics

7.3.1.1.

Relative Abundance

7.3.1.2.

Absolute Abundance

7.3.1.3.

Presence-Absence

7.3.2.

Taxonomic Resolution

7.3.2.1.

Species-Level Identification

7.3.2.2.

Strain-Level Analysis

7.3.2.3.

Higher Taxonomic Levels

7.3.3.

Contamination Assessment

7.3.3.1.

Negative Controls

7.3.3.2.

Positive Controls

7.3.3.3.

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8. Diversity Analysis Methods