Example Pipeline Qiime - RCChan5/BioLockJ GitHub Wiki
Typical QIIME Pipeline
- The following pipeline is generated from the Config file: chinaQiimeClosedPaired.properties.
- Only 6 BioModules are listed in the Config file
- The pipeline executes 15 BioModules at runtime after 6 implicit and 3 dependent modules are added by the system.
- ImportMetadata is a prerequisite implicit module that is always run as the 1st module.
- RegisterNumReads counts the number of reads per sample and stores the total in the metadata file in a field named Num_Reads.
- PearMergeReads is a prerequisite module added when input.dirs contains paired sequence files since QIIME only accepts forward reads.
- AwkFastaConverter is a prerequisite module added when input.dirs contains fastq sequence files since QIIME only accepts fasta format.
- BuildQiimeMapping is an implicit module added to all QIIME pipelines to convert the metadata file into a QIIME mapping file.
- Select 1 OTU picking module [ QiimeClosedRefClassifier / QiimeDeNovoClassifier / QiimeOpenRefClassifier ]
- Each runs add_qiime_labels.py to create the required multiplexed input file before calling the OTU picking script.
- Each outputs 1+ otu_table.biom files.
- MergeQiimeOtuTables is a post-requisite module after QiimeClosedRefClassifier (if selected) used to combine batched output into a single otu_table.biom file.
- Cannot be run after other OTU picking modules.
- Only necessary if #samples > script.batchSize.
- QiimeClassifier is an implicit post-requisite module that runs after the OTU-picking module to produce taxonomy-level OTU reports.
- QiimeParser is a post-requisite module that runs after the classifier to create OTU abundance tables from the taxonomy-level reports.
- Normalizer log-normalizes the OTU abundance tables.
- AddMetadataToOtuTables is a pre-requisite module that runs before the 1st R module.
- CalculateStats is a pre-requisite module that runs before any other R module.
- BuildMdsPlots generates PCOA analysis and plots the top MDS axis.
- BuildPvalHistograms plots p-value histograms to identify significant metadata fields.
- BuildOtuPlots generates box plots and scatterplots to identify significant OTU-metadata correlations.