[QIIME2] 주로 사용하는 QIIME2의 plugins 정리

Amplicon data를 분석할 때 주로 사용하는 QIIME2의 plugins를 정리하였다.

 

전체 plugin 목록 (2021.4 기준)

주로 사용하는 plugin과 pipeline, methods, visualizers는 bold로 표시하였다.

 

1. alignment: Plugin for generating and manipulating alignments.
• Methods
• mafft: De novo multiple sequence alignment with MAFFT
• mafft-add: Add sequences to multiple sequence alignment with MAFFT.
• mask: Positional conservation and gap filtering.
2. composition: Plugin for compositional data analysis.
• Methods
• add-pseudocount: Add pseudocount to table
• Visualizers
• ancom: Apply ANCOM to identify features that differ in abundance.
3. cutadapt: Plugin for removing adapter sequences, primers, and other unwanted sequence from sequence data.
• Methods
• demux-paired: Demultiplex paired-end sequence data with barcodes in-sequence.
• demux-single: Demultiplex single-end sequence data with barcodes in-sequence.
• trim-paired: Find and remove adapters in demultiplexed paired-end sequences.
• trim-single: Find and remove adapters in demultiplexed single-end sequences. 특정 길이 이하의 sequence는 버리고 싶을 때 사용 가능.
4. dada2: Plugin for sequence quality control with DADA2.
• Methods
• denoise-paired: Denoise and dereplicate paired-end sequences
• denoise-pyro: Denoise and dereplicate single-end pyrosequences
• denoise-single: Denoise and dereplicate single-end sequences
5. deblur: Plugin for sequence quality control with Deblur.
• Methods
• denoise-16S: Deblur sequences using a 16S positive filter. ASV profile을 얻기 위해 사용할 수 있다. 이때 input으로는 QC를 마친 데이터를 넣어주어야 한다.
• denoise-other: Deblur sequences using a user-specified positive filter.
• Visualizers
• visualize-stats: Visualize Deblur stats per sample. Deblur후 남은 reads는 몇 개인지 등의 정보를 알 수 있다. deblur 사용 시 --p-sample-stats 옵션을 넣지 않았다면 표시가 안 된다.
6. demux: Plugin for demultiplexing & viewing sequence quality.
• Methods
• emp-paired: Demultiplex paired-end sequence data generated with the EMP protocol.
• emp-single: Demultiplex sequence data generated with the EMP protocol.
• filter-samples: Filter samples out of demultiplexed data.
• subsample-paired: Subsample paired-end sequences without replacement.
• subsample-single: Subsample single-end sequences without replacement.
• Visualizers
• summarize: Summarize counts per sample. Sequence의 read quality와 길이 정보 등을 알 수 있다.
7. diversity: Plugin for exploring community diversity.
• Pipelines
• alpha: Alpha diversity
• alpha-phylogenetic: Alpha diversity (phylogenetic)
• beta: Beta diversity
• beta-correlation: Beta diversity correlation
• beta-phylogenetic: Beta diversity (phylogenetic)
• core-metrics: Core diversity metrics (non-phylogenetic)
• core-metrics-phylogenetic: Core diversity metrics (phylogenetic and non-phylogenetic)
• Methods
• filter-distance-matrix: Filter samples from a distance matrix.
• pcoa: Principal Coordinate Analysis
• pcoa-biplot: Principal Coordinate Analysis Biplot
• procrustes-analysis: Procrustes Analysis
• Visualizers
• adonis: adonis PERMANOVA test for beta group significance
• alpha-correlation: Alpha diversity correlation
• alpha-group-significance: Alpha diversity comparisons
• alpha-rarefaction: Alpha rarefaction curves
• beta-group-significance: Beta diversity group significance
• beta-rarefaction: Beta diversity rarefaction
• bioenv: bioenv
• mantel: Apply the Mantel test to two distance matrices
8. diversity-lib: Plugin for computing community diversity.
• Methods
• alpha-passthrough: Alpha Passthrough (non-phylogenetic)
• beta-passthrough: Beta Passthrough (non-phylogenetic)
• beta-phylogenetic-meta-passthrough: Beta Phylogenetic Meta Passthrough
• beta-phylogenetic-passthrough: Beta Phylogenetic Passthrough
• bray-curtis: Bray-Curtis Dissimilarity
• faith-pd: Faith’s Phylogenetic Diversity
• jaccard: Jaccard Distance
• observed-features: Observed Features
• pielou-evenness: Pielou’s Evenness
• shannon-entropy: Shannon’s Entropy
• unweighted-unifrac: Unweighted Unifrac
• weighted-unifrac: Weighted Unifrac
9. emperor: Plugin for ordination plotting with Emperor.
• Visualizers
• biplot: Visualize and Interact with Principal Coordinates Analysis Biplot
• plot: Visualize and Interact with Principal Coordinates Analysis Plots
• procrustes-plot: Visualize and Interact with a procrustes plot
10. feature-classifier: Plugin for taxonomic classification.
• Pipelines
• classify-hybrid-vsearch-sklearn: ALPHA Hybrid classifier: VSEARCH exact match + sklearn classifier
• Methods
• classify-consensus-blast: BLAST+ consensus taxonomy classifier
• classify-consensus-vsearch: VSEARCH-based consensus taxonomy classifier. Global alignment를 바탕으로 taxonomic assignment를 진행할 때 사용 가능.
• classify-sklearn: Pre-fitted sklearn-based taxonomy classifier
• extract-reads: Extract reads from reference sequences.
• fit-classifier-naive-bayes: Train the naive_bayes classifier
• fit-classifier-sklearn: Train an almost arbitrary scikit-learn classifier
11. feature-table: Plugin for working with sample by feature tables.
• Methods
• filter-features: Filter features from table
• filter-features-conditionally: Filter features from a table based on abundance and prevalence
• filter-samples: Filter samples from table. Table에서 특정 samples을 제외하고 싶을 때 사용 가능.
• filter-seqs: Filter features from sequences. Table에 존재하는 sequence만 남기고 싶을 때 사용 가능.
• group: Group samples or features by a metadata column
• merge: Combine multiple tables. 여러 개의 table을 합치고 싶을 때 사용 가능.
• merge-seqs: Combine collections of feature sequences. 여러 개의 sequence 파일을 합치고 싶을 때 사용 가능.
• merge-taxa: Combine collections of feature taxonomies
• presence-absence: Convert to presence/absence
• rarefy: Rarefy table
• relative-frequency: Convert to relative frequencies
• rename-ids: Renames sample or feature ids in a table
• subsample: Subsample table
• transpose: Transpose a feature table.
• Visualizers
• core-features: Identify core features in table
• heatmap: Generate a heatmap representation of a feature table
• summarize: Summarize table. FeatureTable[Frequency]를 확인할 때 사용 가능.
• tabulate-seqs: View sequence associated with each feature. FeatureData[Sequence]를 확인할 때 사용 가능.
12. fragment-insertion: Plugin for extending phylogenies.
• Methods
• classify-otus-experimental: Experimental: Obtain taxonomic lineages, by finding closest OTU in reference phylogeny.
• filter-features: Filter fragments in tree from table.
• sepp: Insert fragment sequences using SEPP into reference phylogenies.
13. gneiss: Plugin for building compositional models.
• Methods
• assign-ids: Assigns ids on internal nodes in the tree, and makes sure that they are consistent with the table columns.
• correlation-clustering: Hierarchical clustering using feature correlation.
• gradient-clustering: Hierarchical clustering using gradient information.
• ilr-hierarchical: Isometric Log-ratio Transform applied to a hierarchical clustering
• ilr-phylogenetic: Isometric Log-ratio Transform applied to a phylogenetic tree
• ilr-phylogenetic-differential: Differentially abundant Phylogenetic Log Ratios.
• ilr-phylogenetic-ordination: Ordination through a phylogenetic Isometric Log Ratio transform.
• Visualizers
• dendrogram-heatmap: Dendrogram heatmap.
14. longitudinal: Plugin for paired sample and time series analyses.
• Pipelines
• feature-volatility: Feature volatility analysis
• maturity-index: Microbial maturity index prediction.
• Methods
• first-differences: Compute first differences or difference from baseline between sequential states
• first-distances: Compute first distances or distance from baseline between sequential states
• nmit: Nonparametric microbial interdependence test
• Visualizers
• anova: ANOVA test
• linear-mixed-effects: Linear mixed effects modeling
• pairwise-differences: Paired difference testing and boxplots
• pairwise-distances: Paired pairwise distance testing and boxplots
• plot-feature-volatility: Plot longitudinal feature volatility and importances
• volatility: Generate interactive volatility plot
15. metadata: Plugin for working with Metadata.
• Methods
• distance-matrix: Create a distance matrix from a numeric Metadata column
• Visualizers
• tabulate: Interactively explore Metadata in an HTML table. Qaulity control status 파일을 확인할 때 사용 가능.
16. phylogeny: Plugin for generating and manipulating phylogenies.
• Pipelines
• align-to-tree-mafft-fasttree: Build a phylogenetic tree using fasttree and mafft alignment
• align-to-tree-mafft-iqtree: Build a phylogenetic tree using iqtree and mafft alignment.
• align-to-tree-mafft-raxml: Build a phylogenetic tree using raxml and mafft alignment.
• Methods
• fasttree: Construct a phylogenetic tree with FastTree.
• filter-table: Remove features from table if they’re not present in tree.
• iqtree: Construct a phylogenetic tree with IQ-TREE.
• iqtree-ultrafast-bootstrap: Construct a phylogenetic tree with IQ-TREE with bootstrap supports.
• midpoint-root: Midpoint root an unrooted phylogenetic tree.
• raxml: Construct a phylogenetic tree with RAxML.
• raxml-rapid-bootstrap: Construct a phylogenetic tree with bootstrap supports using RAxML.
• robinson-foulds: Calculate Robinson-Foulds distance between phylogenetic trees.
17. quality-control: Plugin for quality control of feature and sequence data.
• Methods
• bowtie2-build: Build bowtie2 index from reference sequences.
• exclude-seqs: Exclude sequences by alignment
• filter-reads: Filter demultiplexed sequences by alignment to reference database.
• Visualizers
• evaluate-composition: Evaluate expected vs. observed taxonomic composition of samples
• evaluate-seqs: Compare query (observed) vs. reference (expected) sequences.
• evaluate-taxonomy: Evaluate expected vs. observed taxonomic assignments
18. quality-filter: Plugin for PHRED-based filtering and trimming.
• Methods
• q-score: Quality filter based on sequence quality scores. Quality control할 때 사용 가능.
19. sample-classifier: Plugin for machine learning prediction of sample metadata.
• Pipelines
• classify-samples: Train and test a cross-validated supervised learning classifier.
• classify-samples-from-dist: Run k-nearest-neighbors on a labeled distance matrix.
• heatmap: Generate heatmap of important features.
• metatable: Convert (and merge) positive numeric metadata (in)to feature table.
• regress-samples: Train and test a cross-validated supervised learning regressor.
• Methods
• classify-samples-ncv: Nested cross-validated supervised learning classifier.
• fit-classifier: Fit a supervised learning classifier.
• fit-regressor: Fit a supervised learning regressor.
• predict-classification: Use trained classifier to predict target values for new samples.
• predict-regression: Use trained regressor to predict target values for new samples.
• regress-samples-ncv: Nested cross-validated supervised learning regressor.
• split-table: Split a feature table into training and testing sets.
• Visualizers
• confusion-matrix: Make a confusion matrix from sample classifier predictions.
• scatterplot: Make 2D scatterplot and linear regression of regressor predictions.
• summarize: Summarize parameter and feature extraction information for a trained estimator.
20. taxa: Plugin for working with feature taxonomy annotations.
• Methods
• collapse: Collapse features by their taxonomy at the specified level
• filter-seqs: Taxonomy-based feature sequence filter.
• filter-table: Taxonomy-based feature table filter.
• Visualizers
• barplot: Visualize taxonomy with an interactive bar plot. Bar plot으로 relative abundance를 나타내고 싶을 때 사용 가능.
21. types: Plugin defining types for microbiome analysis.
22. vsearch: Plugin for clustering and dereplicating with vsearch.
• Pipelines
• cluster-features-open-reference: Open-reference clustering of features.
• Methods
• cluster-features-closed-reference: Closed-reference clustering of features.
• cluster-features-de-novo: De novo clustering of features.
• dereplicate-sequences: Dereplicate sequences.
• join-pairs: Join paired-end reads.
• uchime-denovo: De novo chimera filtering with vsearch.
• uchime-ref: Reference-based chimera filtering with vsearch.
• Visualizers
• fastq-stats: Fastq stats with vsearch.

 

분석 예시

Getting ASV profile (Single-end)

Getting ASV profile (Single-end)

 

Getting ASV profile (Paired-end)

Getting ASV profile (Paired-end)

 

Taxonomic assignment

taxonomic assignment

 

Trimming sequences

Trimming sequences

 

Feature data filtering

Feature data filtering

 

Feature data merging

Feature data merging

 

Data export

  QIIME2 artifacts (.qza)를 일반적으로 사용하는 형식 (tsv, fasta 등)으로 바꿔줄 수 있다. 아래 예제 이외에도 다양한 QIIME2 artifacts를 export할 수 있으며, 어떤 artifacts이냐에 따라 export되는 형식이 달라진다.

Data export

 

Calculate alpha diversity metrics

Calculate alpha diversity metrics

 

 

Reference

• docs.qiime2.org/2021.4/plugins/available/