From 0773aaf89678b967588a902df1f5e6f9ccea393d Mon Sep 17 00:00:00 2001 From: Calvin Date: Tue, 14 May 2013 21:51:40 -0400 Subject: release1.0 --- .../multifasta2otu/multifasta2otutable_gg94_2011.m | 219 +++++++++++++++++++++ 1 file changed, 219 insertions(+) create mode 100644 src/matlab/multifasta2otu/multifasta2otutable_gg94_2011.m (limited to 'src/matlab/multifasta2otu/multifasta2otutable_gg94_2011.m') diff --git a/src/matlab/multifasta2otu/multifasta2otutable_gg94_2011.m b/src/matlab/multifasta2otu/multifasta2otutable_gg94_2011.m new file mode 100644 index 0000000..43e46a3 --- /dev/null +++ b/src/matlab/multifasta2otu/multifasta2otutable_gg94_2011.m @@ -0,0 +1,219 @@ +% Quikr multifasta->otu_table_(for_qiime_use) wrapper code written by Gail Rosen -- 2/1/2013 +%This is an example of how to run Multifasta Quikr with a custom +%training database (in this case Greengenes OTU's within 94% identity) + +%make sure Matlab/Octave is in your path +%cd /path/to/Quikr + +%User-defined variables +input_directory='../separated_samples'; %path to input directory of samples +output_directory='quikr_results'; %path to where want output files to go +otu_table_name='gg1194_otu_octave.txt'; %name of output otu_table filename +trainingdatabasefilename='gg_94_otus_4feb2011.fasta'; %full path to the FASTA file you wish to use as a training database + + +mkdir([output_directory]); +thedirs=dir([input_directory]); +thetime=zeros(numel(thedirs)-1,1); +names={}; + +if(exist('OCTAVE_VERSION')) %check to see if running Octave or Matlab + +%This is Octave Version + +tic(); +k=6; %pick a k-mer size +trainingmatrix=quikrTrain(trainingdatabasefilename,k); %this will return the training database +disp('Training time:') +[headers,~]=fastaread(trainingdatabasefilename); %read in the training database +lambda=10000; +training_time=toc() + +species=struct(); +keys={}; + +tic(); + + +i=0; +%for numdirs=3:5 +for numdirs=3:numel(thedirs) +i=i+1; +disp([num2str(i) ' out of ' num2str(numel(thedirs)-2)]) +fastafilename=[input_directory '/' thedirs(numdirs).name]; +[loadfasta,~]=fastaread(fastafilename); +numreads=numel(loadfasta); +xstar=quikrCustomTrained(trainingmatrix,fastafilename,k,lambda); + +nonzeroentries=find(xstar); %get the indicies of the sequences quikr predicts are in your sample +proportionscell=num2cell(xstar(nonzeroentries)); %convert the concentrations into a cell array +namescell=headers(nonzeroentries); %Get the names of the sequences +namesandproportions={namescell{:}; proportionscell{:}}; %This cell array contains the (unsorted) names of the reconstructed sequences and their concentrations (in the first and second columns respectively) + +[a cols]=size(namesandproportions); +amount=zeros(cols,1); +for j=1:cols + names{j}=['s' namesandproportions{1,j}]; + amount(j)=namesandproportions{2,j}; + if strcmp(keys,names{j}) + temp=species.(names{j}); + temp(i)=round(amount(j).*numreads); + species.(names{j})=temp; + else + temp=zeros(numel(thedirs)-3+1,1); + temp(i)=round(amount(j).*numreads); + if temp(i)==0 + %insignificant counts, do nothing + else + species.(names{j})=temp; + keys{end+1}=names{j}; + end + end +end + +thefa=strfind(thedirs(numdirs).name,'.fa'); + +if ~isempty(thedirs(numdirs).name(1:thefa-1)) + sampleid{i}=thedirs(numdirs).name(1:thefa-1); +else + sampleid{i}='empty_sampleid'; +end + +thetime(i+1)=toc(); +thetime(i+1) + +end + +disp('Total time to compute Quikr:') +toc() +disp('Quikr Average time per file:') +mean(diff(thetime(1:i+1))) + +tic() +numits=i; + +fid=fopen([output_directory '/' otu_table_name],'w'); +fprintf(fid,'# QIIME vGail OTU table\n'); +fprintf(fid,'#OTU_ID\t'); +for i=1:numits +if i