X-Git-Url: https://git.donarmstrong.com/?p=mothur.git;a=blobdiff_plain;f=classifyseqscommand.h;h=59d9ee275800195255961639d77fb6c57d97f417;hp=a73aa0c576f1dc6a3437d9c37557aa89cc0b451c;hb=d1c97b8c04bb75faca1e76ffad60b37a4d789d3d;hpb=7b3c9ca940891c1b20b3b7ec13e05d7e7b316b63

diff --git a/classifyseqscommand.h b/classifyseqscommand.h
index a73aa0c..59d9ee2 100644
--- a/classifyseqscommand.h
+++ b/classifyseqscommand.h
@@ -10,39 +10,228 @@
  *
  */
 
-#include "mothur.h"
+
 #include "command.hpp"
-#include "alignment.hpp"
 #include "classify.h"
+#include "referencedb.h"
+#include "sequence.hpp"
+#include "bayesian.h"
+#include "phylotree.h"
+#include "phylosummary.h"
+#include "knn.h"
+#include "kmertree.h"
+#include "aligntree.h"
+
+
+//KNN and Wang methods modeled from algorithms in
+//Naı¨ve Bayesian Classifier for Rapid Assignment of rRNA Sequences 
+//into the New Bacterial Taxonomy􏰎† 
+//Qiong Wang,1 George M. Garrity,1,2 James M. Tiedje,1,2 and James R. Cole1* 
+//Center for Microbial Ecology1 and Department of Microbiology and Molecular Genetics,2 Michigan State University, 
+//East Lansing, Michigan 48824 
+//Received 10 January 2007/Accepted 18 June 2007 
+
+
 
 class ClassifySeqsCommand : public Command {
 	
 public:
-	ClassifySeqsCommand(string);	
+	ClassifySeqsCommand(string);
+	ClassifySeqsCommand();
 	~ClassifySeqsCommand();
+	
+	vector<string> setParameters();
+	string getCommandName()			{ return "classify.seqs";		}
+	string getCommandCategory()		{ return "Phylotype Analysis";	}
+	
+	string getHelpString();	
+    string getOutputPattern(string);	
+	string getCitation() { return "Wang Q, Garrity GM, Tiedje JM, Cole JR (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73: 5261-7. [ for Bayesian classifier ] \nAltschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997). Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25: 3389-402. [ for BLAST ] \nDeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL (2006). Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72: 5069-72. [ for kmer ] \nhttp://www.mothur.org/wiki/Classify.seqs"; }
+	string getDescription()		{ return "classify sequences"; }
+	
 	int execute(); 
-	void help();	
+	void help() { m->mothurOut(getHelpString()); }	
+	
+	
 	
 private:
 	struct linePair {
-		int start;
-		int numSeqs;
-		linePair(int i, int j) : start(i), numSeqs(j) {}
+		unsigned long long start;
+		unsigned long long end;
+		linePair(unsigned long long i, unsigned long long j) : start(i), end(j) {}
 	};
+
 	vector<int> processIDS;   //processid
 	vector<linePair*> lines;
+	vector<string> fastaFileNames;
+	vector<string> namefileNames;
+    vector<string> countfileNames;
+	vector<string> groupfileNames;
+	vector<string> outputNames;
+	map<string, vector<string> > nameMap;
+	map<string,  vector<string> >::iterator itNames;
 	
 	Classify* classify;
+	ReferenceDB* rdb;
 	
-	string fastaFileName, templateFileName, distanceFileName, search, method, taxonomyFileName;
-	int processors, kmerSize, numWanted, cutoff;
+	string fastaFileName, templateFileName, countfile, distanceFileName, namefile, search, method, taxonomyFileName, outputDir, groupfile;
+	int processors, kmerSize, numWanted, cutoff, iters;
 	float match, misMatch, gapOpen, gapExtend;
-	bool abort;
+	bool abort, probs, save, flip, hasName, hasCount, writeShortcuts;
 	
-	int driver(linePair*, string, string);
-	void appendTaxFiles(string, string);
-	void createProcesses(string, string); 
+	int driver(linePair*, string, string, string, string);
+	int createProcesses(string, string, string, string); 
+	string addUnclassifieds(string, int);
+	
+	int MPIReadNamesFile(string);
+	#ifdef USE_MPI
+	int driverMPI(int, int, MPI_File&, MPI_File&, MPI_File&, MPI_File&, vector<unsigned long long>&);
+	#endif
+};
+
+/**************************************************************************************************/
+//custom data structure for threads to use.
+// This is passed by void pointer so it can be any data type
+// that can be passed using a single void pointer (LPVOID).
+struct classifyData {
+	string taxFName; 
+	string tempTFName; 
+	string filename;
+	string search, taxonomyFileName, templateFileName, method, accnos;
+	unsigned long long start;
+	unsigned long long end;
+	MothurOut* m;
+	float match, misMatch, gapOpen, gapExtend;
+	int count, kmerSize, threadID, cutoff, iters, numWanted;
+	bool probs, flip, writeShortcuts;
+	 
+	classifyData(){}
+	classifyData(string acc, bool p, string me, string te, string tx, string a, string r, string f, string se, int ks, int i, int numW, MothurOut* mout, unsigned long long st, unsigned long long en, float ma, float misMa, float gapO, float gapE, int cut, int tid, bool fli, bool wsh) {
+		accnos = acc;
+		taxonomyFileName = tx;
+		templateFileName = te;
+		taxFName = a;
+		tempTFName = r;
+		filename = f;
+		search = se;
+		method = me;
+		m = mout;
+		start = st;
+		end = en;
+		match = ma; 
+		misMatch = misMa;
+		gapOpen = gapO; 
+		gapExtend = gapE; 
+		kmerSize = ks;
+		cutoff = cut;
+		iters = i;
+		numWanted = numW;
+		threadID = tid;
+		probs = p;
+		count = 0;
+		flip = fli;
+        writeShortcuts = wsh;
+	}
 };
 
+/**************************************************************************************************/
+#if defined (__APPLE__) || (__MACH__) || (linux) || (__linux) || (__linux__) || (__unix__) || (__unix)
+#else
+static DWORD WINAPI MyClassThreadFunction(LPVOID lpParam){ 
+	classifyData* pDataArray;
+	pDataArray = (classifyData*)lpParam;
+	
+	try {
+		ofstream outTax;
+		pDataArray->m->openOutputFile(pDataArray->taxFName, outTax);
+		
+		ofstream outTaxSimple;
+		pDataArray->m->openOutputFile(pDataArray->tempTFName, outTaxSimple);
+		
+		ofstream outAcc;
+		pDataArray->m->openOutputFile(pDataArray->accnos, outAcc);
+		
+		ifstream inFASTA;
+		pDataArray->m->openInputFile(pDataArray->filename, inFASTA);
+		
+		string taxonomy;
+				
+		//print header if you are process 0
+		if ((pDataArray->start == 0) || (pDataArray->start == 1)) {
+			inFASTA.seekg(0);
+		}else { //this accounts for the difference in line endings. 
+			inFASTA.seekg(pDataArray->start-1); pDataArray->m->gobble(inFASTA); 
+		}
+		
+		//make classify
+		Classify* myclassify;
+        string outputMethodTag = pDataArray->method + ".";
+		if(pDataArray->method == "bayesian"){	myclassify = new Bayesian(pDataArray->taxonomyFileName, pDataArray->templateFileName, pDataArray->search, pDataArray->kmerSize, pDataArray->cutoff, pDataArray->iters, pDataArray->threadID, pDataArray->flip, pDataArray->writeShortcuts);		}
+		else if(pDataArray->method == "knn"){	myclassify = new Knn(pDataArray->taxonomyFileName, pDataArray->templateFileName, pDataArray->search, pDataArray->kmerSize, pDataArray->gapOpen, pDataArray->gapExtend, pDataArray->match, pDataArray->misMatch, pDataArray->numWanted, pDataArray->threadID);				}
+        else if(pDataArray->method == "zap"){	
+            outputMethodTag = pDataArray->search + "_" + outputMethodTag;
+            if (pDataArray->search == "kmer") {   myclassify = new KmerTree(pDataArray->templateFileName, pDataArray->taxonomyFileName, pDataArray->kmerSize, pDataArray->cutoff); }
+            else {  myclassify = new AlignTree(pDataArray->templateFileName, pDataArray->taxonomyFileName, pDataArray->cutoff);  }
+        }
+		else {
+			pDataArray->m->mothurOut(pDataArray->search + " is not a valid method option. I will run the command using bayesian.");
+			pDataArray->m->mothurOutEndLine();
+			myclassify = new Bayesian(pDataArray->taxonomyFileName, pDataArray->templateFileName, pDataArray->search, pDataArray->kmerSize, pDataArray->cutoff, pDataArray->iters, pDataArray->threadID, pDataArray->flip, pDataArray->writeShortcuts);	
+		}
+		
+		if (pDataArray->m->control_pressed) { delete myclassify; return 0; }
+		
+		pDataArray->count = 0;
+		for(int i = 0; i < pDataArray->end; i++){ //end is the number of sequences to process
+			
+			if (pDataArray->m->control_pressed) { delete myclassify; return 0; }
+			
+			Sequence* candidateSeq = new Sequence(inFASTA); pDataArray->m->gobble(inFASTA);
+			
+			if (candidateSeq->getName() != "") {
+				
+				taxonomy = myclassify->getTaxonomy(candidateSeq);
+				
+				if (pDataArray->m->control_pressed) { delete candidateSeq; return 0; }
+				
+				if (taxonomy == "unknown;") { pDataArray->m->mothurOut("[WARNING]: " + candidateSeq->getName() + " could not be classified. You can use the remove.lineage command with taxon=unknown; to remove such sequences."); pDataArray->m->mothurOutEndLine(); }
+
+				//output confidence scores or not
+				if (pDataArray->probs) {
+					outTax << candidateSeq->getName() << '\t' << taxonomy << endl;
+				}else{
+					outTax << candidateSeq->getName() << '\t' << myclassify->getSimpleTax() << endl;
+				}
+					
+				outTaxSimple << candidateSeq->getName() << '\t' << myclassify->getSimpleTax() << endl;
+					
+				if (myclassify->getFlipped()) { outAcc << candidateSeq->getName() << endl; }
+				
+				pDataArray->count++;
+			}
+			delete candidateSeq;
+			//report progress
+			if((pDataArray->count) % 100 == 0){	pDataArray->m->mothurOutJustToScreen("Processing sequence: " + toString(pDataArray->count)+"\n"); 	}
+			
+		}
+		//report progress
+		if((pDataArray->count) % 100 != 0){	pDataArray->m->mothurOutJustToScreen("Processing sequence: " + toString(pDataArray->count)+"\n"); 		}
+		
+		delete myclassify;
+		inFASTA.close();
+		outTax.close();
+		outTaxSimple.close();
+		
+	}
+	catch(exception& e) {
+		pDataArray->m->errorOut(e, "ClassifySeqsCommand", "MyClassThreadFunction");
+		exit(1);
+	}
+} 
+#endif
+
+
+
+
 #endif