//**********************************************************************************************************************
-vector<string> ClassifySeqsCommand::getValidParameters(){
+vector<string> ClassifySeqsCommand::setParameters(){
try {
- string AlignArray[] = {"template","fasta","name","group","search","ksize","method","processors","taxonomy","match","mismatch","gapopen","gapextend","numwanted","cutoff","probs","iters", "outputdir","inputdir"};
- vector<string> myArray (AlignArray, AlignArray+(sizeof(AlignArray)/sizeof(string)));
+ CommandParameter ptaxonomy("taxonomy", "InputTypes", "", "", "none", "none", "none",false,true); parameters.push_back(ptaxonomy);
+ CommandParameter ptemplate("reference", "InputTypes", "", "", "none", "none", "none",false,true); parameters.push_back(ptemplate);
+ CommandParameter pfasta("fasta", "InputTypes", "", "", "none", "none", "none",false,true); parameters.push_back(pfasta);
+ CommandParameter pname("name", "InputTypes", "", "", "none", "none", "none",false,false); parameters.push_back(pname);
+ CommandParameter pgroup("group", "InputTypes", "", "", "none", "none", "none",false,false); parameters.push_back(pgroup);
+ CommandParameter psearch("search", "Multiple", "kmer-blast-suffix-distance", "kmer", "", "", "",false,false); parameters.push_back(psearch);
+ CommandParameter pksize("ksize", "Number", "", "8", "", "", "",false,false); parameters.push_back(pksize);
+ CommandParameter pmethod("method", "Multiple", "bayesian-knn", "bayesian", "", "", "",false,false); parameters.push_back(pmethod);
+ CommandParameter pprocessors("processors", "Number", "", "1", "", "", "",false,false); parameters.push_back(pprocessors);
+ CommandParameter pmatch("match", "Number", "", "1.0", "", "", "",false,false); parameters.push_back(pmatch);
+ CommandParameter pmismatch("mismatch", "Number", "", "-1.0", "", "", "",false,false); parameters.push_back(pmismatch);
+ CommandParameter pgapopen("gapopen", "Number", "", "-2.0", "", "", "",false,false); parameters.push_back(pgapopen);
+ CommandParameter pgapextend("gapextend", "Number", "", "-1.0", "", "", "",false,false); parameters.push_back(pgapextend);
+ CommandParameter pcutoff("cutoff", "Number", "", "0", "", "", "",false,true); parameters.push_back(pcutoff);
+ CommandParameter pprobs("probs", "Boolean", "", "T", "", "", "",false,false); parameters.push_back(pprobs);
+ CommandParameter piters("iters", "Number", "", "100", "", "", "",false,true); parameters.push_back(piters);
+ CommandParameter pnumwanted("numwanted", "Number", "", "10", "", "", "",false,true); parameters.push_back(pnumwanted);
+ CommandParameter pinputdir("inputdir", "String", "", "", "", "", "",false,false); parameters.push_back(pinputdir);
+ CommandParameter poutputdir("outputdir", "String", "", "", "", "", "",false,false); parameters.push_back(poutputdir);
+
+ vector<string> myArray;
+ for (int i = 0; i < parameters.size(); i++) { myArray.push_back(parameters[i].name); }
return myArray;
}
catch(exception& e) {
- m->errorOut(e, "ClassifySeqsCommand", "getValidParameters");
- exit(1);
- }
-}
-//**********************************************************************************************************************
-ClassifySeqsCommand::ClassifySeqsCommand(){
- try {
- abort = true; calledHelp = true;
- vector<string> tempOutNames;
- outputTypes["taxonomy"] = tempOutNames;
- outputTypes["taxsummary"] = tempOutNames;
- outputTypes["matchdist"] = tempOutNames;
- }
- catch(exception& e) {
- m->errorOut(e, "ClassifySeqsCommand", "ClassifySeqsCommand");
+ m->errorOut(e, "ClassifySeqsCommand", "setParameters");
exit(1);
}
}
//**********************************************************************************************************************
-vector<string> ClassifySeqsCommand::getRequiredParameters(){
+string ClassifySeqsCommand::getHelpString(){
try {
- string Array[] = {"fasta","template","taxonomy"};
- vector<string> myArray (Array, Array+(sizeof(Array)/sizeof(string)));
- return myArray;
+ string helpString = "";
+ helpString += "The classify.seqs command reads a fasta file containing sequences and creates a .taxonomy file and a .tax.summary file.\n";
+ helpString += "The classify.seqs command parameters are reference, fasta, name, search, ksize, method, taxonomy, processors, match, mismatch, gapopen, gapextend, numwanted and probs.\n";
+ helpString += "The reference, fasta and taxonomy parameters are required. You may enter multiple fasta files by separating their names with dashes. ie. fasta=abrecovery.fasta-amzon.fasta \n";
+ helpString += "The search parameter allows you to specify the method to find most similar template. Your options are: suffix, kmer, blast and distance. The default is kmer.\n";
+ helpString += "The name parameter allows you add a names file with your fasta file, if you enter multiple fasta files, you must enter matching names files for them.\n";
+ helpString += "The group parameter allows you add a group file so you can have the summary totals broken up by group.\n";
+ helpString += "The method parameter allows you to specify classification method to use. Your options are: bayesian and knn. The default is bayesian.\n";
+ helpString += "The ksize parameter allows you to specify the kmer size for finding most similar template to candidate. The default is 8.\n";
+ helpString += "The processors parameter allows you to specify the number of processors to use. The default is 1.\n";
+#ifdef USE_MPI
+ helpString += "When using MPI, the processors parameter is set to the number of MPI processes running. \n";
+#endif
+ helpString += "The match parameter allows you to specify the bonus for having the same base. The default is 1.0.\n";
+ helpString += "The mistmatch parameter allows you to specify the penalty for having different bases. The default is -1.0.\n";
+ helpString += "The gapopen parameter allows you to specify the penalty for opening a gap in an alignment. The default is -2.0.\n";
+ helpString += "The gapextend parameter allows you to specify the penalty for extending a gap in an alignment. The default is -1.0.\n";
+ helpString += "The numwanted parameter allows you to specify the number of sequence matches you want with the knn method. The default is 10.\n";
+ helpString += "The cutoff parameter allows you to specify a bootstrap confidence threshold for your taxonomy. The default is 0.\n";
+ helpString += "The probs parameter shuts off the bootstrapping results for the bayesian method. The default is true, meaning you want the bootstrapping to be shown.\n";
+ helpString += "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";
+ helpString += "The classify.seqs command should be in the following format: \n";
+ helpString += "classify.seqs(reference=yourTemplateFile, fasta=yourFastaFile, method=yourClassificationMethod, search=yourSearchmethod, ksize=yourKmerSize, taxonomy=yourTaxonomyFile, processors=yourProcessors) \n";
+ helpString += "Example classify.seqs(fasta=amazon.fasta, reference=core.filtered, method=knn, search=gotoh, ksize=8, processors=2)\n";
+ helpString += "The .taxonomy file consists of 2 columns: 1 = your sequence name, 2 = the taxonomy for your sequence. \n";
+ helpString += "The .tax.summary is a summary of the different taxonomies represented in your fasta file. \n";
+ helpString += "Note: No spaces between parameter labels (i.e. fasta), '=' and parameters (i.e.yourFastaFile).\n\n";
+ return helpString;
}
catch(exception& e) {
- m->errorOut(e, "ClassifySeqsCommand", "getRequiredParameters");
+ m->errorOut(e, "ClassifySeqsCommand", "getHelpString");
exit(1);
}
}
//**********************************************************************************************************************
-vector<string> ClassifySeqsCommand::getRequiredFiles(){
+ClassifySeqsCommand::ClassifySeqsCommand(){
try {
- vector<string> myArray;
- return myArray;
+ abort = true; calledHelp = true;
+ setParameters();
+ vector<string> tempOutNames;
+ outputTypes["taxonomy"] = tempOutNames;
+ outputTypes["taxsummary"] = tempOutNames;
+ outputTypes["matchdist"] = tempOutNames;
}
catch(exception& e) {
- m->errorOut(e, "ClassifySeqsCommand", "getRequiredFiles");
+ m->errorOut(e, "ClassifySeqsCommand", "ClassifySeqsCommand");
exit(1);
}
}
if(option == "help") { help(); abort = true; calledHelp = true; }
else {
-
- //valid paramters for this command
- string AlignArray[] = {"template","fasta","name","group","search","ksize","method","processors","taxonomy","match","mismatch","gapopen","gapextend","numwanted","cutoff","probs","iters", "outputdir","inputdir"};
- vector<string> myArray (AlignArray, AlignArray+(sizeof(AlignArray)/sizeof(string)));
+ vector<string> myArray = setParameters();
OptionParser parser(option);
map<string, string> parameters = parser.getParameters();
if (inputDir == "not found"){ inputDir = ""; }
else {
string path;
- it = parameters.find("template");
+ it = parameters.find("reference");
//user has given a template file
if(it != parameters.end()){
path = m->hasPath(it->second);
//if the user has not given a path then, add inputdir. else leave path alone.
- if (path == "") { parameters["template"] = inputDir + it->second; }
+ if (path == "") { parameters["reference"] = inputDir + it->second; }
}
it = parameters.find("taxonomy");
}
//check for required parameters
- templateFileName = validParameter.validFile(parameters, "template", true);
+ templateFileName = validParameter.validFile(parameters, "reference", true);
if (templateFileName == "not found") {
- m->mothurOut("template is a required parameter for the classify.seqs command.");
+ m->mothurOut("reference is a required parameter for the classify.seqs command.");
m->mothurOutEndLine();
abort = true;
}
fastaFileName = validParameter.validFile(parameters, "fasta", false);
- if (fastaFileName == "not found") { m->mothurOut("fasta is a required parameter for the classify.seqs command."); m->mothurOutEndLine(); abort = true; }
+ if (fastaFileName == "not found") {
+ //if there is a current fasta file, use it
+ string filename = m->getFastaFile();
+ if (filename != "") { fastaFileNames.push_back(filename); m->mothurOut("Using " + filename + " as input file for the fasta parameter."); m->mothurOutEndLine(); }
+ else { m->mothurOut("You have no current fastafile and the fasta parameter is required."); m->mothurOutEndLine(); abort = true; }
+ }
else {
m->splitAtDash(fastaFileName, fastaFileNames);
temp = validParameter.validFile(parameters, "ksize", false); if (temp == "not found"){ temp = "8"; }
convert(temp, kmerSize);
- temp = validParameter.validFile(parameters, "processors", false); if (temp == "not found"){ temp = "1"; }
+ temp = validParameter.validFile(parameters, "processors", false); if (temp == "not found"){ temp = m->getProcessors(); }
+ m->setProcessors(temp);
convert(temp, processors);
search = validParameter.validFile(parameters, "search", false); if (search == "not found"){ search = "kmer"; }
}
//**********************************************************************************************************************
-
ClassifySeqsCommand::~ClassifySeqsCommand(){
-
if (abort == false) {
for (int i = 0; i < lines.size(); i++) { delete lines[i]; } lines.clear();
}
}
-
-//**********************************************************************************************************************
-
-void ClassifySeqsCommand::help(){
- try {
- m->mothurOut("The classify.seqs command reads a fasta file containing sequences and creates a .taxonomy file and a .tax.summary file.\n");
- m->mothurOut("The classify.seqs command parameters are template, fasta, name, search, ksize, method, taxonomy, processors, match, mismatch, gapopen, gapextend, numwanted and probs.\n");
- m->mothurOut("The template, fasta and taxonomy parameters are required. You may enter multiple fasta files by separating their names with dashes. ie. fasta=abrecovery.fasta-amzon.fasta \n");
- m->mothurOut("The search parameter allows you to specify the method to find most similar template. Your options are: suffix, kmer, blast and distance. The default is kmer.\n");
- m->mothurOut("The name parameter allows you add a names file with your fasta file, if you enter multiple fasta files, you must enter matching names files for them.\n");
- m->mothurOut("The group parameter allows you add a group file so you can have the summary totals broken up by group.\n");
- m->mothurOut("The method parameter allows you to specify classification method to use. Your options are: bayesian and knn. The default is bayesian.\n");
- m->mothurOut("The ksize parameter allows you to specify the kmer size for finding most similar template to candidate. The default is 8.\n");
- m->mothurOut("The processors parameter allows you to specify the number of processors to use. The default is 1.\n");
- #ifdef USE_MPI
- m->mothurOut("When using MPI, the processors parameter is set to the number of MPI processes running. \n");
- #endif
- m->mothurOut("The match parameter allows you to specify the bonus for having the same base. The default is 1.0.\n");
- m->mothurOut("The mistmatch parameter allows you to specify the penalty for having different bases. The default is -1.0.\n");
- m->mothurOut("The gapopen parameter allows you to specify the penalty for opening a gap in an alignment. The default is -2.0.\n");
- m->mothurOut("The gapextend parameter allows you to specify the penalty for extending a gap in an alignment. The default is -1.0.\n");
- m->mothurOut("The numwanted parameter allows you to specify the number of sequence matches you want with the knn method. The default is 10.\n");
- m->mothurOut("The cutoff parameter allows you to specify a bootstrap confidence threshold for your taxonomy. The default is 0.\n");
- m->mothurOut("The probs parameter shuts off the bootstrapping results for the bayesian method. The default is true, meaning you want the bootstrapping to be shown.\n");
- m->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");
- m->mothurOut("The classify.seqs command should be in the following format: \n");
- m->mothurOut("classify.seqs(template=yourTemplateFile, fasta=yourFastaFile, method=yourClassificationMethod, search=yourSearchmethod, ksize=yourKmerSize, taxonomy=yourTaxonomyFile, processors=yourProcessors) \n");
- m->mothurOut("Example classify.seqs(fasta=amazon.fasta, template=core.filtered, method=knn, search=gotoh, ksize=8, processors=2)\n");
- m->mothurOut("The .taxonomy file consists of 2 columns: 1 = your sequence name, 2 = the taxonomy for your sequence. \n");
- m->mothurOut("The .tax.summary is a summary of the different taxonomies represented in your fasta file. \n");
- m->mothurOut("Note: No spaces between parameter labels (i.e. fasta), '=' and parameters (i.e.yourFastaFile).\n\n");
- }
- catch(exception& e) {
- m->errorOut(e, "ClassifySeqsCommand", "help");
- exit(1);
- }
-}
-
-
//**********************************************************************************************************************
int ClassifySeqsCommand::execute(){
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