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@@ -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.