Quikr's Matlab Implementation

The Quikr implementation works in Matlab and also works in Octave, but the Octave version will run much slower.

Quikr Example

This is an example of how to run Quikr. Before you try the example please make make sure that you are in the quikr's matlab directory (src/matlab/):

cd quikr/src/matlab

Using Quikr with the default databse

This is the full path name to your data file:

fastafilename='/path/to/quikr-code/testfastafile.fa';

This will give the predicted reconstruction frequencies using the default training database trainset7_112011.fa from RDP version 2.4 Xstar will be on the same basis as trainset7_112011.fa, so to get the sequences that are predicted to be present in your sample:

xstar=quikr(fastafilename);

Read in the training database.

Note fastaread is not by default included in Octave. The fastaread.m file is included in the Quikr download directory and is directly compatible with Matlab.

[headers,~]=fastaread('trainset7\_112011.fa');

Get the indicies of the sequences quikr predicts are in your sample

nonzeroentries=find(xstar);

Convert the concentrations into a cell array

proportionscell=num2cell(xstar(nonzeroentries));

Get the names of the sequences

namescell=headers(nonzeroentries);

This cell array contains the (unsorted) names of the reconstructed sequences and their concentrations (in the first and second columns respectively) so to find which sequence is the most abundant in your mixture:

namesandproportions={namescell{:}; proportionscell{:}};

Get the maximum value and it's position

[val,ind]=max(xstar(nonzeroentries));

Note that this does not imply this specific strain or species is in your sample, just that phylum/class/order/family/genus this species belongs to is in your sample.

namesandproportions{1:2,ind}

Using Quikr With A Custom Trained Database

If you would like to use a custom training database, follow the following steps:

Full path name to your data file:

fastafilename='/path/to/your/fastafile.fasta';

Full path to the FASTA file you wish to use as a training database

trainingdatabasefilename='/path/to/your/trainingdatabase.fasta';

Pick a k-mer size (typically 6 )

k=6;

This will return the training database then to do the reconstruction

trainingmatrix=quikrTrain(trainingdatabasefilename,k);

Pick a lambda (larger lambda -> theoretically predicted concentrations are closer to actual concentrations), this depends on k-mer size picked, also size and condition of the TrainingMatrix

lambda=10000;

Get the predicted reconstruction frequencies Again, xstar is on the same basis as the TrainingMatrix, so to get the sequences that are predicted to be present in your sample:

xstar=quikrCustomTrained(trainingmatrix,fastafilename,k,lambda);

Read in the training database

[headers,~]=fastaread(trainingdatabasefilename);

Get the indices of the sequences quikr predicts are in your sample

nonzeroentries=find(xstar);

Convert the concentrations into a cell array

proportionscell=num2cell(xstar(nonzeroentries));

Get the names of the sequences

namescell=headers(nonzeroentries);

This cell array contains the (unsorted) names of the reconstructed sequences and their concentrations (in the first and second columns respectively)

namesandproportions={namescell{:}; proportionscell{:}};

Using Multifasta2otu.m

Usage tips: * 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) * Note: When making your QIIME Metadata file, the sample id's must match the fasta file prefix names * Fasta files of reads must have a suffix that starts with .fa (e.g.: .fasta and .fa are valid while .fna is NOT) * Modify the top of the Matlab/Octave scripts for \<input_directory>, \<output_directory>, \<output_filename>, and \<training_database_filename>

To use with QIIME, one must run the QIIME conversion tool on our OTU table output:

convert_biom.py -i <quikr_otu_table.txt> -o <quikr_otu>.biom
--biom_table_type="otu table"

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: 1. \<quikr_otu_table.txt> 2. the tree of the database sequences that were used (e.g. dp7_mafft.fasttree, gg_94_otus_4feb2011.tre, etc.) 3. your-defined \

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>