PyroNoise

PyroNoise models and denoises 454 pyrosequencing flowgrams to infer true sequences and reduce overestimation of operational taxonomic units (OTUs) in amplicon-based microbial diversity analyses.

Key Features:

• Flowgram targeting: Operates on flowgrams generated by 454 pyrosequencing to address technology-specific signal characteristics.
• Noise modeling: Employs a model of sequencing noise that reflects the error properties of pyrosequencing flowgrams.
• Novel distance measure: Uses a novel distance measure tailored to pyrosequencing noise for comparing flowgrams.
• Flowgram clustering: Clusters noisy flowgrams into sequence representatives to infer true sequences from amplicon data.
• Chimera mitigation: Addresses chimeric artifacts present in pyrosequencing datasets.
• OTU correction: Reduces overestimation of operational taxonomic units (OTUs) in downstream diversity analyses.
• Empirical validation: Applied to pyrosequenced microbial 16S rDNA sequences from a lake environment for validation.

Scientific Applications:

• Microbial diversity estimation: Improves accuracy of OTU-based diversity assessments from 454 amplicon 16S rDNA datasets.
• Microbiome studies: Supports high-fidelity sequence inference in microbial community analyses derived from pyrosequencing.
• Environmental genomics: Applied to environmental samples such as lake microbial communities to improve sequence-based ecological conclusions.

Methodology:

Models and mitigates 454 pyrosequencing noise by applying a novel distance measure to flowgrams and clustering the noisy flowgrams into inferred true sequences, while addressing chimeric artifacts.