core_qiime_analyses.py – A workflow for running a core set of QIIME analyses.

Description:

This script plugs several QIIME steps together to form a basic full data analysis workflow. The steps include quality filtering and demultiplexing sequences (optional), running the pick_otus_through_otu_table.py workflow (pick otus and representative sequences, assign taxonomy, align representative sequences, build a tree, and build the OTU table), generating 2d and 3d beta diversity PCoA plots, generating alpha rarefaction plots, identifying OTUs that are differentially represented in different categories, and several additional analysis. Beta diversity calculations will be run both with and without an even sampling step, where the depth of sampling can either be passed to the script or QIIME will try to make a reasonable guess.

Usage: core_qiime_analyses.py [options]

Input Arguments:

Note

[REQUIRED]

-i, --input_fnas

The input fasta file(s) – comma-separated if more than one [REQUIRED]

-o, --output_dir

The output directory [REQUIRED]

-m, --mapping_fp

The mapping filepath [REQUIRED]

[OPTIONAL]

-p, --parameter_fp

Path to the parameter file, which specifies changes to the default behavior. See http://www.qiime.org/documentation/file_formats.html#qiime-parameters. [if omitted, default values will be used]

-q, --input_quals

The 454 qual files. Comma-separated if more than one, and must correspond to the order of the fasta files. Not relevant if passing –suppress_split_libraries. [default: None]

-f, --force

Force overwrite of existing output directory (note: existing files in output_dir will not be removed) [default: None]

-a, --parallel

Run in parallel where available. Specify number of jobs to start with -O or in the parameters file. [default: False]

-e, --seqs_per_sample

Depth of coverage for diversity analyses that incorporate subsampling the OTU table to an equal number of sequences per sample. [default: determined automatically - bad choices can be made in some circumstances]

--even_sampling_keeps_all_samples

If -e/–seqs_per_sample is not provided, chose the even sampling depth to force retaining all samples (rather then default which will choose a sampling depth which may favor keeping more sequences by excluding some samples) [default: False]

-t, --reference_tree_fp

Path to the tree file if one should be used. Relevant for closed-reference-based OTU picking methods only (i.e., uclust_ref -C and BLAST) [default: de novo tree will be used]

-c, --categories

The metadata category or categories to compare (i.e., column headers in the mapping file) for the otu_category_significance, supervised_learning.py, and cluster_quality.py steps. Pass a comma-separated list if more than one category [default: None; skip these steps]

--suppress_split_libraries

Skip demultiplexing/quality filtering (i.e. split_libraries). This assumes that sequence identifiers are in post-split_libraries format (i.e., sampleID_seqID) [default: False]

-O, --jobs_to_start

Number of jobs to start. NOTE: you must also pass -a to run in parallel, this defines the number of jobs to be started if and only if -a is passed [default: 1]

Output:

Run serial analysis using default parameters, and guess the even sampling depth (no -e provided)

core_qiime_analyses.py -i Fasting_Example.fna -q Fasting_Example.qual -o FastingStudy -m Fasting_Map.txt -c Treatment,DOB

Run serial analysis, and guess the even sampling depth (no -e provided). Skip split libraries by starting with already demultiplexed sequences.

core_qiime_analyses.py -i seqs.fna -o FastingStudy -p custom_parameters.txt -m Fasting_Map.txt -c Treatment,DOB