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: 4]
Output:
Run serial analysis using a custom parameters file (-p), and guess the even sampling depth (no -e provided). ALWAYS SPECIFY ABSOLUTE FILE PATHS (absolute path represented here as $PWD, but will generally look something like /home/ubuntu/my_analysis/).
core_qiime_analyses.py -i $PWD/Fasting_Example.fna -q $PWD/Fasting_Example.qual -o $PWD/FastingStudy_w_sl -m $PWD/Fasting_Map.txt -c BarcodeSequence -p $PWD/params.txt
Run serial analysis using a custom parameters file (-p), and guess the even sampling depth (no -e provided). Skip split libraries by starting with already demultiplexed sequences. ALWAYS SPECIFY ABSOLUTE FILE PATHS (absolute path represented here as $PWD, but will generally look something like /home/ubuntu/my_analysis/).
core_qiime_analyses.py -i $PWD/seqs.fna -o $PWD/FastingStudy -m $PWD/Fasting_Map.txt -c BarcodeSequence --suppress_split_libraries -p $PWD/params.txt