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Diffstat (limited to 'doc/cli.markdown')
| -rw-r--r-- | doc/cli.markdown | 34 |
1 files changed, 30 insertions, 4 deletions
diff --git a/doc/cli.markdown b/doc/cli.markdown index 8d25337..b065240 100644 --- a/doc/cli.markdown +++ b/doc/cli.markdown @@ -5,7 +5,7 @@ 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 ## +## 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 @@ -15,13 +15,20 @@ download a pretrained matrix from our database\_download.html. quikr\_train returns a custom trained matrix that can be used with the quikr function. You must supply a kmer. -quikr\_train's optional arguments: +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) -## quikr ## +### 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. @@ -29,13 +36,32 @@ 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 optional arguments: +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 # |