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, 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.

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.
  -V, --version, print version.

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. Because of the sparse nature of the database, the matrix easily achieves a high compression ratio, even with gzip. It takes the gg94_database.fasta as an input and outputs the sensing matrix as gg94_sensing_databse.npy.gz

quikr_train -i gg94_database.fasta -o gg94_sensing_database.matrix.gz -k 6

Quikr

Quikr returns the estimated frequencies of batcteria present when given a 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:
-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.

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.

Pre-processing of Multifasta_to_otu

Please name fasta files of sample reads with .fa<*> and place them into one directory without any other file in that directory (for example, no hidden files that the operating system may generate, are allowed in that directory)
Fasta files of reads must have a suffix that starts with .fa (e.g.: .fasta and .fa are valid while .fna is NOT)

Usage

multifasta_to_otu's arguments:
-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

Note: When making your QIIME Metadata file, the sample id's must match the sample fasta file prefix names

4-step QIIME procedure after using Quikr to obtain 3D PCoA graphs: (Note: Our code works much better with WEIGHTED Unifrac as opposed to Unweighted.)

Pre-requisites:

quikr_otu_table.txt (The OTU Table generated with multifasta_to_otu
the tree of the database sequences that were used (e.g. dp7_mafft.fasttree, gg_94_otus_4feb2011.tre, etc.)
your-defined qiime_metadata_file.txt ( a Qiime Map file)

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

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:

mv /path/to/quikr/src/nbc/count /path/to/quikr/src/nbc/count-kmers
PATH = $PATH:/path/to/quikr/src/nbc/