catastrophy 0.0.2

A fungal trophy classifier based on CAZymes

CATAStrophy

CATAStrophy is a classification method for describing lifestyles/trophic characteristics of filamentous plant pathogens using carbohydrate-active enzymes (CAZymes). The name CATAStrophy is a backronym portmanteau hybrid where "CATAS" means CAZyme Assisted Training And Sorting.

CATAStrophy takes HMMER3 files from searches against the dbCAN CAZyme database as input and returns a pseudo-probabilities called the relative centroid distance (RCD) of trophic class memberships for each file.

To "train" this models, we performed principal component analysis (PCA) on the frequencies of CAZymes for a set of curated proteomes with literature support for their trophic lifestyles. For each class in our classification system, we find the center/geometric mean of the class in the first 16 principal components.

New proteomes are classified by transforming the CAZyme frequencies using the same PCA loadings as in the training set. We then find the closest class center in PCA space, and set that RCD score to 1. Then for each of the other classes we find the distance between the new proteome and the class center divided (i.e. relative to) by the distance to the closest class center. If a new proteome is equidistant between two class centroids and they are closer than any other class, then both RCD scores will be one, so the RCD method is a kind of multi-label classifier. This is useful when evaluating your organism, because it might have characteristics of multiple classes (or be so dissimilar to any class that the distance is meaningless).

Installing

CATAStrophy is a python program which can be used as a python module or via a command-line interface. It can be installed from PyPI https://pypi.org/project/catastrophy/ using pip, or from anaconda https://anaconda.org/darcyabjones/catastrophy using conda.

Users that are less familiar with python and pip might like to read our INSTALL.md document, which explains things in more detail, including where this will be installed and using virtual environments. For details on installing and using conda see their getting-started guide.

CATAStrophy is tested to work with python 3.6+, and it depends on numpy and BioPython.

To install CATAStrophy and dependencies with pip:

# Windows users may need to subtitute "Python" instead of "python3"
python3 -m pip install --user catastrophy

To install CATAStrophy and dependencies using conda:

conda install -c darcyabjones catastrophy

Using CATAStrophy

The to run CATAStrophy you need to supply the input files and where to put the output. Catastrophy uses HMMER3 hmmscan searches against dbCAN as input.

Either the plain text (stdout) and "domain table" (--domtblout) can be used.

The easiest way to get a file like this is to annotate your proteome using the dbCAN online tool at http://cys.bios.niu.edu/dbCAN2/, and save the HMMER3 raw text results locally.

Assuming that you have this file locally you can run CATAStrophy like so:

catastrophy -i my_dbcan_results.txt -f hmmer -o my_catastrophy_results.csv

The output will be a tab-delimited file (which you can open in excel) with the first row containing column headers and subsequent rows containing a label and the pseudo-probabilities of membership to each trophic class. The -f/--format flag specified the format of the input file and defaults to hmmer.

NOTE: In this document I use the .csv extension to mean any plain text tabular format because excel doesn't recognise alternate extensions like .tsv. The domain table output is actually space delimited and the catastrophy output is a tab delimited file.

By default the filenames are used as the label but you can explicitly specify a label using the -l/--label flag. The output from the command above will have two lines, one containing the column headers and the other containing results for the file my_dbcan_results.txt which will have the label "my_dbcan_results.txt". If you don't specify a label for stdin input the label will be "".

To give it a nicer label you can run this.

catastrophy -i my_dbcan_results.txt -l prettier_label -o my_catastrophy_results.csv

Which would give the output line for my_dbcan_results.txt the label "prettier_label". Labels cannot contain spaces unless you explicitly escape them (quotes will not work).

Multiple input files can be provided using spaces to separate them:

catastrophy -i dbcan_1.txt dbcan_2.txt -o my_catastrophy_results.csv

The output from this will contain three rows, one containing the headers and the other two containing the results for the files dbcan_1.txt and dbcan_2.txt which will be labelled by the filenames. Note that standard bash "globbing" patterns expand into a space delimited array, so you can easily use * or subshells if you like (eg. $(find . -type f -name *.txt) etc). To explicitly label these files you can again supply the label flag with the space separated labels.

catastrophy -i dbcan_1.txt dbcan_2.txt -l label1 label2 -o my_catastrophy_results.csv

Note that if you do use the label flag, the number of labels must be the same as the number of input files.

Finally because dbCAN is updated as new CAZyme classes are created, merged, or split, catastrophy has a final parameter that allows you to select the model trained on a specific dbCAN version (starting from version 5).

To specify the version of the model to use, include the -m/--model flag with one of the valid options (see catastrophy -h for the options).

catastrophy -m v5 -i my_dbcan_results.txt -o my_catastrophy_results.csv

The model versions just reflect the version of dbCAN that the model was trained against.

Running dbCAN locally

If you have lots of proteomes to run (or you're a command-line snob like me) then you probably don't want to use the web interface. In that case you can run the dbCAN pipeline locally using HMMER.

The instructions for running the HMMER and the dbCAN parser can be found here http://bcb.unl.edu/dbCAN2/download/Databases/dbCAN-old@UGA/ in the readme.txt file. It isn't the most friendly documentation though so i'll repeat it here (assuming that you've installed HMMER and are using a unix-like OS).

First download the HMMs and the parser script.

cd <a directory that you can work in>

mkdir -p ./data
wget -qc -P ./data http://bcb.unl.edu/dbCAN2/download/Databases/dbCAN-HMMdb-V7.txt

Note that I'm downloading a specific version of the database rather that just the latest one. Now we can convert the file containing HMM definitions into a HMMER database.

hmmpress ./data/dbCAN-HMMdb-V7.txt

Now we can run HMMER to find matches to the dbCAN HMMs. For demonstration, we'll save both the domain table and plain text outputs.

hmmscan --domtblout my_fasta_hmmer.csv ./data/dbCAN-HMMdb-V7.txt my_fasta.fasta > my_fasta_hmmer.txt

The domain table is now in the file my_fasta_hmmer.csv and the plain hmmer text output is in my_fasta_hmmer.txt. Either one of these files is appropriate for use with CATAStrophy, (just remember to specify the --format flag).

catastrophy -m v7 -i my_fasta_hmmer.csv --format domtab -o my_catastrophy_results.csv

# or
catastrophy -m v7 -i my_fasta_hmmer.txt --format hmmer -o my_catastrophy_results.csv

Command line arguments

Only the input files are required and are provided as positional arguments. Other optional parameters are below:

Parameter	default	description
-h/--help	flag	Show help text and exit.
--version	flag	Show program version information and exit.
-f/--format	"hmmer_text"	The format that the input is provided in. All input files must be in the same format. HMMER raw (hmmer_text, default) and domain table (hmmer_domtab) formatted files are accepted. Files processed by the dbCAN formatter hmmscan-parser.sh are also accepted using the dbcan option.
-l/--label	filenames	Label to give the prediction for the input file(s). Specify more than one label by separating them with a space. The number of labels should be the same as the number of input files. By default, the filenames are used as labels.
-o/--outfile	stdout	File path to write tab delimited output to.
-m/--model	latest	The version of the model (matching the dbCAN database version) to use. The latest version is used by default. See catastrophy -h for list of valid options.
-m/--nomenclature	nomenclature3	The nomenclature type to use. Nomenclature1 is the classical symbiont, saprotroph, (hemi)biotroph, necrotroph system. Nomenclature2 separates wilts from necrotrophs, and considers symbionts as a class of biotroph. Nomenclature3 is the system proposed in the paper. See catastrophy -h for list of options.

Basic usage:

# To stdout aka "-o -"
catastrophy infile1.txt infile2.txt > results.csv

# To specify an output filename
catastrophy -o results.csv infile1.txt infile2.txt

# To take input from stdin use a "-"
cat infile1.txt | catastrophy - > results.csv

# If you provide labels you may need to use "--"
# to tell when you're done and that infiles should start.
# This is because both "--label" and infiles can take multiple arguments.

catastrophy -l mylabel1 mylabel2 -o results.csv infile1.txt infile2.txt  # Fine
catastrophy -o results.csv -l mylabel1 mylabel2 infile1.txt infile2.txt  # Dangerous
# Do this instead to tell where labels stops and infiles starts.
catastrophy -o results.csv -l mylabel1 mylabel2 -- infile1.txt infile2.txt

Output

Output will be as a tab-separated values file, with each input file as a row and each trophic class as a column.

For example, for nomenclature1 the table might look like.

catastrophy infile1.txt infile2.txt

label	saprotroph	biotroph 1	biotroph 2	biotroph 3	mesotroph - internal	mesotroph - external	necrotroph - narrow	necrotroph - broad	wilt
infile1.txt	0.1	0.1	0.9	1	0.3	0.3	0.1	0.1	0.1
infile2.txt	0.1	0.1	0.2	0.2	0.8	0.4	0.1	0.6	0.1