QasimHussain/qiime2_micobiome_analysis

Microbiome Analysis pipeline via QIIME2

Abstract

This repository contains an end-to-end computational pipeline for analyzing 16S rRNA gene amplicon sequencing data using the Quantitative Insights Into Microbial Ecology 2 (QIIME2) framework. The protocol is designed to achieve high-resolution taxonomic assignment, robust alpha and beta diversity metrics, and statistically rigorous differential abundance testing. Moving past standard Operational Taxonomic Unit (OTU) clustering, this workflow leverages advanced denoising algorithms to resolve Amplicon Sequence Variants (ASVs), offering single-nucleotide variance detection.

Experimental Design and Data Provenance

The initial datasets subsumed into this workflow originate from distinct experimental conditions, wherein high-throughput targeted amplicon sequencing of the 16S rRNA gene was performed optimally via Illumina methodologies. The raw reads were processed directly into standard QIIME2 artifact geometries (.qza), safeguarding provenance logs inherently bound to all derivatives and transformations. Sequence structure encompasses multiplexed configurations necessitating demultiplexing driven by precise sample-barcode metadata definitions.

Computational Methodology

Denoising and Amplicon Sequence Variant (ASV) Inference

Prior to diversity analyses, strict quality control procedures are applied. This pipeline operates two independent bioinformatic error-correction models (You can choose one based on your preference):

1. DADA2 Algorithm: DADA2 infers exact amplicon sequences iteratively by assessing nucleotide transition error rates from the corpus itself, stripping chimeric reads and generating a fundamental ASV matrix unencumbered by artificial similarity thresholding.
2. Deblur Framework: Functioning as a secondary control paradigm, Deblur denoises sequences globally at unified lengths by filtering features against static biological assumptions, thereby producing alternate robust feature metrics.

Phylogeny and Diversity Analytics

Reconstructing evolutionary relationships among variant sequences allows community comparisons derived from shared descent. Post-alignment masking is deployed to isolate conserved regions prior to hierarchical tree building. The computed distance matrices encompass both phylogenetic paradigms (UniFrac) and compositional methodologies (Bray-Curtis).

The figure below exhibits an Unweighted UniFrac Principal Coordinates Analysis (PCoA), demonstrating distinct beta-diversity clustering mapped to specific metadata groupings natively.

Taxonomic Classification and Differential Metrics

Taxonomic architecture is identified by passing ASV features through a pre-trained Naive Bayes heuristic classifier mapped against the Greengenes reference database (13_8 release). This ensures each read translates efficiently into structural lineage classifications extending dynamically from Phylum to Genus.

To isolate differentially expressed organisms without inducing false positives rampant in rarefied datasets, we implement ANCOM-BC against compositionally collapsed subsets of the metadata, enforcing absolute statistical confidence across variable testing schemas (e.g., gut-derived niches).

The sequence distribution across representative subsets is rendered via the taxonomic profile below.

Repository Structure and Execution

This repository contains dual execution paradigms: a traditional local analysis and a robust cloud-native deployment strategy. Key structural components include:

• deployment/: Contains the structured pipeline outputs engineered for direct community sharing.
  • dataset/: Houses both raw multi-sample sequence fragments and the fully precomputed QIIME 2 artifacts (.qza/.qzv). Note: Individual files exceeding strictly clamped 100MB repository thresholds are filtered via .gitignore.
  • notebook/: The location of the highly optimized mucosal_moving_pictures.ipynb executable.
• qiime2_16s.ipynb: The baseline execution syntax notebook for localized computational runs.

Requirements

Execution strictly necessitates an operational qiime2 conda environment and associated plugins (q2-diversity, q2-dada2, q2-feature-classifier, etc.). For static plotting derivations, plotly and standard Python data manipulation libraries (pandas) are utilized.

References

1. Bolyen, E., Rideout, J. R., Dillon, M. R., et al. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology, 37(8), 852-857. https://doi.org/10.1038/s41587-019-0209-9
2. Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581-583. https://doi.org/10.1038/nmeth.3869
3. McDonald, D., Price, M. N., Goodrich, J., et al. (2012). An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. The ISME journal, 6(3), 610-618. https://doi.org/10.1038/ismej.2011.139

License

This project is licensed under the MIT License. See the LICENSE file for stringent usage delineations.