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# Quikr Command Line Utilities #
Quikr has three command-line utilities that mirror the behavior of the python
module and the matlab implementation. The advantage of this is ease of scripting
and job management. These utilities are written in python and wrap the quikr
module.
## Quikr\_train ##
The quikr\_train is a tool to train a database for use with the quikr tool.
Before running the quikr utility, you need to generate the trained matrix or
download a pretrained matrix from our database\_download.html.
### Usage ###
quikr\_train returns a custom trained matrix that can be used with the quikr
function. You must supply a kmer.
quikr\_train's arguments:
-i, --input, the database of sequences (fasta format)
-o, --output, the trained matrix (text file)
-k, --kmer, the kmer size (integer)
-z, --compress compress the output matrix with gzip (flag)
### Example ###
Here is an example on how to train a database. This uses the -z flag to compress
the output matrix since it can be very large. It takes the gg94\_database.fasta
as an input and outputs the trained matrix as gg94\_trained\_databse.npy.gz
quikr_train -i gg94_database.fasta -o gg94_trained_database.npy.gz -k 6 -z
## Quikr ##
Quikr returns the estimated frequencies of batcteria present when given a
input FASTA file. A default trained matrix will be used if none is supplied
You must supply a kmer and default lambda if using a custom trained matrix.
### Usage ###
quikr returns the solution vector as a csv file.
quikr's arguments:
-f, --fasta, the fasta file sample
-o, --output OUTPUT, the output path (csv output)
-t, --trained-matrix, the trained matrix
-l, --lamb, the lambda size. (the default lambda value is 10,000)
-k, --kmer, this specifies which kmer to use (default is 6)
## Multifasta\_to\_otu ##
The Multifasta\_to\_otu tool is a handy wrapper for quikr which lets the user
to input as many fasta files as they like, and then returns an OTU table of the
number of times a specimen was seen in all of the samples
Warning: this program will use a large amount of memory, and CPU time. You can
reduce the number of cores used, and thus memory, by specifying the -j flag
with aspecified number of jobs. Otherwise python with run one job per cpu core.
### Usage ###
multifasta\_to\_otu's arguments:
-i, --input-directory, the directory containing fasta files
-o, --otu-table, the output OTU table
-t, --trained-matrix, the trained database to use
-f, --trained-fasta, the fasta file used to train your matrix
-d, --output-directory, quikr output directory
-l, --lamb, specify what lambda to use (the default value is 10,000)
-k, --kmer, specify which kmer to use, (default value is 6)
-j, --jobs, specifies how many jobs to run at once, (default=number of CPUs)
# Troubleshooting #
If you are having trouble, and these solutions don't work. Please contact the
developers with questions and issues.
#### Broken Pipe Errors ####
Make sure that you have the count-kmers and probablilties-by-read in your
$PATH, and that they are executable.
If you have not installed quikr system-wide, you'll need to add the folder
location of these binaries in the terminal before running the command:
PATH = $PATH:/path/to/quikr/src/nbc/
Make sure that the binaries are executable by running:
chmod +x probabilities-by-read
chmod +x count-kmers
#### Python Cannot Find XYZ ####
Ensure that you have Python 2.7, Scipy, Numpy, and BIOpython installed
and that python is setup correctly. You should be able to do this from a python
prompt without any errors:
>>> import numpy
>>> import scipy
>>> from Bio import SeqIO
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