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| author | Calvin <calvin@EESI> | 2013-06-11 16:00:47 -0400 | 
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| committer | Calvin <calvin@EESI> | 2013-06-11 16:00:47 -0400 | 
| commit | 6939aac71c95c979aca4b5934c32f17377e7f7f6 (patch) | |
| tree | a14c6b8542f9f1ae6e22df2a3c4c22217188adcb | |
| parent | 026bd0accdbfec4ebeb91e11ed41eaed3326d284 (diff) | |
| parent | aeaccefd79d17b7742c5d7348ced3f8442eca9b5 (diff) | |
Merge branch 'master' of github.com:mutantturkey/quikr
| -rw-r--r-- | doc/cli.markdown | 58 | 
1 files changed, 30 insertions, 28 deletions
| diff --git a/doc/cli.markdown b/doc/cli.markdown index fcec55b..587d014 100644 --- a/doc/cli.markdown +++ b/doc/cli.markdown @@ -6,18 +6,17 @@ utilities are written in C and utilize OpenMP for multithreading.  ## 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 matqrix or -download a pretrained matrix from our database\_download.html. +Before running the quikr utility, you need to generate the sensing matrix.  ### Usage ###  quikr\_train returns a custom sensing matrix that can be used with the quikr  function. -quikr\_train's arguments: -  -i, --input, the database of sequences (fasta format) -  -o, --output, the sensing matrix (text file) -  -k, --kmer, specifiy wha size of kmer to use. (default value is 6) -  -v, --verbose, verbose mode. +    quikr_train's arguments: +      -i, --input, the database of sequences (fasta format) +      -o, --output, the sensing matrix (text file) +      -k, --kmer, specifiy wha size of kmer to use. (default value is 6) +      -v, --verbose, verbose mode.  ### Example ###  Here is an example on how to train a database. This uses the -z flag to compress @@ -35,13 +34,13 @@ input FASTA file. You need to train a matrix or download a new matrix  ### Usage ###  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) +    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)  ## Multifasta\_to\_otu ##  The Multifasta\_to\_otu tool is a handy wrapper for quikr which lets the user @@ -62,19 +61,20 @@ with aspecified number of jobs. Otherwise python with run one job per cpu core.    .fa are valid while .fna is NOT)  ### 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) - -# Post-processing of Multifasta\_to\_otu  # + +    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) + +### Post-processing of Multifasta\_to\_otu  ###  * Note: When making your QIIME Metadata file, the sample id's must match the    sample fasta file prefix names @@ -90,12 +90,14 @@ Pre-requisites:  3. your-defined <qiime_metadata_file.txt>  The QIIME procedue: +      convert_biom.py -i <quikr_otu_table.txt> -o <quikr_otu>.biom --biom_table_type="otu table"      beta_diversity.py -i <quikr_otu>.biom -m weighted_unifrac -o beta_div -t <tree file> (example: rdp7_mafft.fasttree)>      principal_coordinates.py -i beta_div/weighted_unifrac_<quikr_otu>.txt -o <quikr_otu_project_name>_weighted.txt      make_3d_plots.py -i <quikr_otu_project_name>_weighted.txt -o <3d_pcoa_plotdirectory> -m <qiime_metadata_file> -#### Broken Pipe Errors ####  + +#### Broken Pipe Errors ####  Make sure that you have the count-kmers and probablilties-by-read in your  $PATH, and that they are executable.  | 
