out_bg.write("{}\n".format(i)) sys.stderr.write("Printing the foreground scores at {}\n".format(output_filename_fg)) for i in m.windows_score([x for x in m.signal_separation(testing_filename)],args.order,args.wsize, back_dict,sign_dict): out_fg.write("{}\n".format(i)) sys.stderr.write("To plot the evaluation result, use the R script attached.\n") sys.stderr.write("MMID have finished the model building step succesfully.\n") if args.identify: if args.model == None: model_file=output_mm else: model_file=args.model sys.stderr.write("\t\tMMID v.4\n\t\tAdvanced Genome Bioinformatics\n\t\tAndreu Bofill & David Mas\n\nThe input Markov Model data is from {}\n".format(model_file)) sys.stderr.write("Identifying motifs in the FASTA file {}\n".format(args.query)) sys.stdout.write("Motif Identifier.\nDefined Threshold:{0}\tFasta file input:{1}\tMarkov Model input:{2}\n".format(args.threshold,args.query,model_file)) bg_MM,sg_MM=m.read_MM(model_file) for i in m.FASTA_iterator(args.query): for j,x in m.windows_score_query(i, args.order, args.wsize, bg_MM, sg_MM,args.threshold): sys.stdout.write("Sequence:{0}\tScore:{1:.3f}\n".format(j,x)) if args.weblogo: if args.model == None: model_file=output_mm else: model_file=args.model sys.stdout.write("Input MSA to generate a web logo:\n") bg_MM,sg_MM=m.read_MM(model_file) m.generate_weblogo(sg_MM,bg_MM) sys.stdout.write("In order to get the actual logo go to weblogo.berkeley.edu and paste the generated k-mers.\n") if not args.identify and not args.train and not args.weblogo: