else {
//valid paramters for this command
- string AlignArray[] = {"template","fasta","search","ksize","method","processors","taxonomy","match","mismatch","gapopen","gapextend","numwanted","cutoff"};
+ string AlignArray[] = {"template","fasta","search","ksize","method","processors","taxonomy","match","mismatch","gapopen","gapextend","numwanted","cutoff","probs","iters"};
vector<string> myArray (AlignArray, AlignArray+(sizeof(AlignArray)/sizeof(string)));
OptionParser parser(option);
temp = validParameter.validFile(parameters, "cutoff", false); if (temp == "not found"){ temp = "0"; }
convert(temp, cutoff);
+
+ temp = validParameter.validFile(parameters, "probs", false); if (temp == "not found"){ temp = "true"; }
+ probs = isTrue(temp);
+
+ temp = validParameter.validFile(parameters, "iters", false); if (temp == "not found") { temp = "100"; }
+ convert(temp, iters);
+
if ((method == "bayesian") && (search != "kmer")) {
void ClassifySeqsCommand::help(){
try {
mothurOut("The classify.seqs command reads a fasta file containing sequences and creates a .taxonomy file and a .tax.summary file.\n");
- mothurOut("The classify.seqs command parameters are template, fasta, search, ksize, method, taxonomy, processors, match, mismatch, gapopen, gapextend and numwanted.\n");
+ mothurOut("The classify.seqs command parameters are template, fasta, search, ksize, method, taxonomy, processors, match, mismatch, gapopen, gapextend, numwanted and probs.\n");
mothurOut("The template, fasta and taxonomy parameters are required.\n");
mothurOut("The search parameter allows you to specify the method to find most similar template. Your options are: suffix, kmer and blast. The default is kmer.\n");
mothurOut("The method parameter allows you to specify classification method to use. Your options are: bayesian and knn. The default is bayesian.\n");
mothurOut("The processors parameter allows you to specify the number of processors to use. The default is 1.\n");
mothurOut("The match parameter allows you to specify the bonus for having the same base. The default is 1.0.\n");
mothurOut("The mistmatch parameter allows you to specify the penalty for having different bases. The default is -1.0.\n");
- mothurOut("The gapopen parameter allows you to specify the penalty for opening a gap in an alignment. The default is -1.0.\n");
- mothurOut("The gapextend parameter allows you to specify the penalty for extending a gap in an alignment. The default is -2.0.\n");
+ mothurOut("The gapopen parameter allows you to specify the penalty for opening a gap in an alignment. The default is -2.0.\n");
+ mothurOut("The gapextend parameter allows you to specify the penalty for extending a gap in an alignment. The default is -1.0.\n");
mothurOut("The numwanted parameter allows you to specify the number of sequence matches you want with the knn method. The default is 10.\n");
mothurOut("The cutoff parameter allows you to specify a bootstrap confidence threshold for your taxonomy. The default is 0.\n");
+ mothurOut("The probs parameter shut off the bootstrapping results for the bayesian method. The default is true, meaning you want the bootstrapping to be run.\n");
+ mothurOut("The iters parameter allows you to specify how many iterations to do when calculating the bootstrap confidence score for your taxonomy with the bayesian method. The default is 100.\n");
mothurOut("The classify.seqs command should be in the following format: \n");
mothurOut("classify.seqs(template=yourTemplateFile, fasta=yourFastaFile, method=yourClassificationMethod, search=yourSearchmethod, ksize=yourKmerSize, taxonomy=yourTaxonomyFile, processors=yourProcessors) \n");
mothurOut("Example classify.seqs(fasta=amazon.fasta, template=core.filtered, method=knn, search=gotoh, ksize=8, processors=2)\n");
try {
if (abort == true) { return 0; }
-
-
- if(method == "bayesian") { classify = new Bayesian(taxonomyFileName, templateFileName, search, kmerSize, cutoff); }
+ if(method == "bayesian") { classify = new Bayesian(taxonomyFileName, templateFileName, search, kmerSize, cutoff, iters); }
else if(method == "knn") { classify = new Knn(taxonomyFileName, templateFileName, search, kmerSize, gapOpen, gapExtend, match, misMatch, numWanted); }
else {
mothurOut(search + " is not a valid method option. I will run the command using bayesian.");
mothurOutEndLine();
- classify = new Bayesian(taxonomyFileName, templateFileName, search, kmerSize, cutoff);
+ classify = new Bayesian(taxonomyFileName, templateFileName, search, kmerSize, cutoff, iters);
}
int numFastaSeqs = 0;
- string newTaxonomyFile = getRootName(fastaFileName) + "taxonomy";
+ string newTaxonomyFile = getRootName(fastaFileName) + getRootName(taxonomyFileName) + "taxonomy";
string tempTaxonomyFile = getRootName(fastaFileName) + "taxonomy.temp";
- string taxSummary = getRootName(fastaFileName) + "tax.summary";
+ string taxSummary = getRootName(fastaFileName) + getRootName(taxonomyFileName) + "tax.summary";
int start = time(NULL);
#if defined (__APPLE__) || (__MACH__) || (linux) || (__linux)
for(int i=0;i<line->numSeqs;i++){
Sequence* candidateSeq = new Sequence(inFASTA);
-
- taxonomy = classify->getTaxonomy(candidateSeq);
- if (taxonomy != "bad seq") {
- outTax << candidateSeq->getName() << '\t' << taxonomy << endl;
- outTaxSimple << candidateSeq->getName() << '\t' << classify->getSimpleTax() << endl;
- }
-
+ if (candidateSeq->getName() != "") {
+ taxonomy = classify->getTaxonomy(candidateSeq);
+
+ if (taxonomy != "bad seq") {
+ //output confidence scores or not
+ if (probs) {
+ outTax << candidateSeq->getName() << '\t' << taxonomy << endl;
+ }else{
+ outTax << candidateSeq->getName() << '\t' << classify->getSimpleTax() << endl;
+ }
+
+ outTaxSimple << candidateSeq->getName() << '\t' << classify->getSimpleTax() << endl;
+ }
+ }
delete candidateSeq;
if((i+1) % 100 == 0){
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