pick_otus_through_otu_table.py – A workflow script for picking OTUs through building OTU tables

Description:

This script takes a sequence file and performs all processing steps through building the OTU table.

REQUIRED: You must add values for the following parameters in a custom parameters file:

align_seqs:template_fp filter_alignment:lane_mask_fp

These are the values that you would typically pass as –template_fp to align_seqs.py and lane_mask_fp to filter_alignment.py, respectively.

Usage: pick_otus_through_otu_table.py [options]

Input Arguments:

Note

[REQUIRED]

-i, --input_fp

The input fasta file [REQUIRED]

-o, --output_dir

The output directory [REQUIRED]

-p, --parameter_fp

Path to the parameter file [REQUIRED]

[OPTIONAL]

-f, --force

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

-w, --print_only

Print the commands but don’t call them – useful for debugging [default: False]

-a, --parallel

Run in parallel where available [default: False]

-m, --mapping_fp

The mapping filepath [REQUIRED for denoising]

-s, --sff_fp

The sff file [REQUIRED for denoising]

Output:

This script will produce a set of cluster centroids (as a FASTA file) and a cluster mapping file (from denoise.py if sff.txt and mapping file were provided), an OTU mapping file (pick_otus.py), a representative set of sequences (FASTA file from pick_rep_set.py), a sequence alignment file (FASTA file from align_seqs.py), taxonomy assignment file (from assign_taxonomy.py), a filtered sequence alignment (from filter_alignment.py), a phylogenetic tree (Newick file from make_phylogeny.py) and an OTU table (from make_otu_table.py).

Simple example:

The following command will start an analysis on inseq1.fasta (-i), which is a post-split_libraries fasta file. The sequence identifiers in this file should be of the form <sample_id>_<unique_seq_id>. The following steps, corresponding to the preliminary data preparation, are applied.

1. Pick OTUs with uclust at similarity of 0.97;
2. Pick a representative set with the most_abundant method;
3. Align the representative set with PyNAST (REQUIRED: SPECIFY TEMPLATE ALIGNMENT with align_seqs:template_fp in the parameters file);
4. Assign taxonomy with RDP classifier;
5. Filter the alignment prior to tree building - remove positions which are all gaps, and specified as 0 in the lanemask (REQUIRED: SPECIFY LANEMASK with filter_alignment:lane_mask_fp in the parameters file);
6. Build a phylogenetic tree with FastTree;
7. Build an OTU table.

All output files will be written to the directory specified by -o, and subdirectories as appropriate.

pick_otus_through_otu_table.py -i inseqs1.fasta -o wf1/ -p custom_parameters.txt

Simple example with denoising:

This command will do the same steps as the previous example and additionally denoise the data set prior to OTU picking. Only flowgrams in the input sff.txt file that have a matching identifier in inseqs1.fasta are considered here, the rest is discarded.

1. Denoise flowgrams in inseqs1.sff.txt;
2. Pick OTUs with uclust at similarity of 0.97;
3. Pick a representative set with the most_abundant method;
4. Align the representative set with PyNAST (REQUIRED: SPECIFY TEMPLATE ALIGNMENT with align_seqs:template_fp in the parameters file);
5. Assign taxonomy with RDP classifier;
6. Filter the alignment prior to tree building - remove positions which are all gaps, and specified as 0 in the lanemask (REQUIRED: SPECIFY LANEMASK with filter_alignment:lane_mask_fp in the parameters file);
7. Build a phylogenetic tree with FastTree;
8. Build an OTU table.

All output files will be written to the directory specified by -o, and subdirectories as appropriate.

pick_otus_through_otu_table.py -s inseqs1.sff.txt -m metadata_mapping.txt -i inseqs1.fasta -o wf2/ -p custom_parameters.txt