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| -rw-r--r-- | doc/cli.markdown | 40 |
1 files changed, 24 insertions, 16 deletions
diff --git a/doc/cli.markdown b/doc/cli.markdown index 587d014..ac5a231 100644 --- a/doc/cli.markdown +++ b/doc/cli.markdown @@ -4,6 +4,9 @@ module and the matlab implementation. The advantage of this is ease of scripting and job management, as well as faster processing and lower memory usage. These utilities are written in C and utilize OpenMP for multithreading. +For more in-depth information about these tools please refer to their +respective manual pages. + ## 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 sensing matrix. @@ -17,6 +20,7 @@ function. -o, --output, the sensing matrix (text file) -k, --kmer, specifiy wha size of kmer to use. (default value is 6) -v, --verbose, verbose mode. + -V, --version, print version. ### Example ### Here is an example on how to train a database. This uses the -z flag to compress @@ -35,12 +39,15 @@ input FASTA file. You need to train a matrix or download a new matrix quikr returns the solution vector as a csv file. quikr's arguments: - -f, --fasta, the sample's fasta file of NGS READS - -o, --output OTU\_FRACTION\_PRESENT, a vector representing the percentage of - database sequence's presence in sample (csv output) - -s, --sensing-matrix the sensing matrix. (generated by quikr\_train) - -l, --lambda, the lambda size. (the default lambda value is 10,000) - -k, --kmer, this specifies the size of the kmer to use (default is 6) + -i, --input the sample's fasta file of NGS READS (fasta format) + -f, --sensing-fasta location of the fasta file database used to create the sensing matrix (fasta format) + -s, --sensing-matrix location of the sensing matrix. (trained from quikr_train) + -k, --kmer specify what size of kmer to use. (default value is 6) + -l, --lambda lambda value to use. (default value is 10000) + -o, --output OTU_FRACTION_PRESENT a vector representing the percentage of database sequence's presence in sample. (csv output) + -v, --verbose verbose mode. + -V, --version print version. + -d, --debug debug mode, read manpage for more details ## Multifasta\_to\_otu ## The Multifasta\_to\_otu tool is a handy wrapper for quikr which lets the user @@ -63,16 +70,17 @@ with aspecified number of jobs. Otherwise python with run one job per cpu core. ### Usage ### multifasta\_to\_otu's arguments: - -i, --input, the directory containing the samples' fasta files of - reads (note each fasta file should correspond to a separate sample) - -o, --otu-table, the OTU table, with OTU\_FRACTION\_PRESENT for each sample, - which is compatible with QIIME's convert\_biom.py (or sequence table if not - OTU's) - -s, --sensing-matrix, the sensing matrix - -f, --sensing-fasta, the fasta file database of sequences - -l, --lambda, specify what size of lambda to use (the default value is 10,000) - -k, --kmer, specify what size of kmer to use, (default value is 6) - -j, --jobs, specifies how many jobs to run at once, (default=number of CPUs) + -i, --input-directory the directory containing the samples' fasta files of + reads (note each file should correspond to a separate sample) + -f, --sensing-fasta location of the fasta file database used to create the sensing matrix (fasta format) + -s, --sensing-matrix location of the sensing matrix. (sensing from quikr_train) + -k, --kmer specify what size of kmer to use. (default value is 6) + -l, --lambda lambda value to use. (default value is 10000) + -j, --jobs specifies how many jobs to run at once. (default value is the number of CPUs) + -o, --output the OTU table, with NUM_READS_PRESENT for each sample which + is compatible with QIIME's convert_biom.py (or a sequence table if not OTU's) + -v, --verbose verbose mode. + -V, --version print version. ### Post-processing of Multifasta\_to\_otu ### |