# HG changeset patch # User bgruening # Date 1370604782 14400 # Node ID 8624069d7a0ee2bc386b167e83d4ebd837786fff Uploaded diff -r 000000000000 -r 8624069d7a0e glimmer2gff.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/glimmer2gff.py Fri Jun 07 07:33:02 2013 -0400 @@ -0,0 +1,36 @@ +#!/usr/bin/env python + +""" +Input: Glimmer3 prediction +Output: GFF3 file +Return a GFF3 file with the genes predicted by Glimmer3 +Bjoern Gruening + +Note: Its not a full-fledged GFF3 file, its a really simple one. + +""" + +import sys, re + +def __main__(): + input_file = open(sys.argv[1], 'r') + + print '##gff-version 3\n' + for line in input_file: + line = line.strip() + if line[0] == '>': + header = line[1:] + else: + (id, start, end, frame, score) = re.split('\s+', line) + if int(end) > int(start): + strand = '+' + else: + strand = '-' + (start, end) = (end, start) + + rest = 'frame=%s;score=%s' % (frame, score) + print '\t'.join([header, 'glimmer_prediction', 'predicted_gene', start, end, '.', strand, '.', rest]) + + +if __name__ == "__main__" : + __main__() diff -r 000000000000 -r 8624069d7a0e glimmer2gff.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/glimmer2gff.xml Fri Jun 07 07:33:02 2013 -0400 @@ -0,0 +1,63 @@ + + Converts Glimmer Files to GFF Files + + glimmer2gff.py + $input > $output + + + + + + + + + + + + + + +**What it does** + +Converts a Glimmer3 output File to an GFF Annotation File:: + +**Example** + +Input:: + >contig00097 sbe.0.234 + orf00003 2869 497 -2 5.60 + orf00005 3894 2875 -1 7.05 + orf00007 4242 4826 +3 8.04 + orf00010 4846 5403 +1 8.57 + orf00012 6858 5413 -1 10.87 + orf00013 6857 7594 +2 3.61 + orf00014 7751 9232 +2 11.34 + orf00015 9374 10357 +2 10.66 + orf00017 10603 11196 +1 13.39 + orf00021 11303 11911 +2 8.81 + orf00025 14791 12050 -2 13.51 + orf00026 15216 16199 +3 6.37 + orf00028 16333 16935 +1 8.86 + + +Output: + contig00097 sbe.0.234 glimmer gene 497 2869 . - . -2 5.60 + contig00097 sbe.0.234 glimmer gene 2875 3894 . - . -1 7.05 + contig00097 sbe.0.234 glimmer gene 4242 4826 . + . +3 8.04 + contig00097 sbe.0.234 glimmer gene 4846 5403 . + . +1 8.57 + contig00097 sbe.0.234 glimmer gene 5413 6858 . - . -1 10.87 + contig00097 sbe.0.234 glimmer gene 6857 7594 . + . +2 3.61 + contig00097 sbe.0.234 glimmer gene 7751 9232 . + . +2 11.34 + contig00097 sbe.0.234 glimmer gene 9374 10357 . + . +2 10.66 + contig00097 sbe.0.234 glimmer gene 10603 11196 . + . +1 13.39 + contig00097 sbe.0.234 glimmer gene 11303 11911 . + . +2 8.81 + contig00097 sbe.0.234 glimmer gene 12050 14791 . - . -2 13.51 + contig00097 sbe.0.234 glimmer gene 15216 16199 . + . +3 6.37 + contig00097 sbe.0.234 glimmer gene 16333 16935 . + . +1 8.86 + + +----- + + + + diff -r 000000000000 -r 8624069d7a0e glimmer3-build-icm-wrapper.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/glimmer3-build-icm-wrapper.xml Fri Jun 07 07:33:02 2013 -0400 @@ -0,0 +1,119 @@ + + (glimmer3) + + glimmer + + + build-icm + --depth $depth + #if $no_stops: + --no_stops + #end if + --period $period + --width $width + + #if $stop_codon_opts.stop_codon_opts_selector == "gb": + --trans_table "${stop_codon_opts.genbank_gencode}" + #else: + --stop_codons "${stop_codon_opts.stop_codons}" + #end if + + $outfile < $infile + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +**What it does** + + This program constructs an interpolated context model (ICM) from an input set of sequences. + This model can be used by Glimmer3 to predict genes. + +----- + + +**Example** + +* input:: + + -Genome Sequence + + >CELF22B7 C.aenorhabditis elegans (Bristol N2) cosmid F22B7 + GATCCTTGTAGATTTTGAATTTGAAGTTTTTTCTCATTCCAAAACTCTGT + GATCTGAAATAAAATGTCTCAAAAAAATAGAAGAAAACATTGCTTTATAT + TTATCAGTTATGGTTTTCAAAATTTTCTGACATACCGTTTTGCTTCTTTT + TTTCTCATCTTCTTCAAATATCAATTGTGATAATCTGACTCCTAACAATC + GAATTTCTTTTCCTTTTTCTTTTTCCAACAACTCCAGTGAGAACTTTTGA + ATATCTTCAAGTGACTTCACCACATCAGAAGGTGTCAACGATCTTGTGAG + AACATCGAATGAAGATAATTTTAATTTTAGAGTTACAGTTTTTCCTCCGA + CAATTCCTGATTTACGAACATCTTCTTCAAGCATTCTACAGATTTCTTGA + TGCTCTTCTAGGAGGATGTTGAAATCCGAAGTTGGAGAAAAAGTTCTCTC + AACTGAAATGCTTTTTCTTCGTGGATCCGATTCAGATGGACGACCTGGCA + GTCCGAGAGCCGTTCGAAGGAAAGATTCTTGTGAGAGAGGCGTGAAACAC + AAAGGGTATAGGTTCTTCTTCAGATTCATATCACCAACAGTTTGAATATC + CATTGCTTTCAGTTGAGCTTCGCATACACGACCAATTCCTCCAACCTAAA + AAATTATCTAGGTAAAACTAGAAGGTTATGCTTTAATAGTCTCACCTTAC + GAATCGGTAAATCCTTCAAAAACTCCATAATCGCGTTTTTATCATTTTCT + ..... + +* output: + interpolated context model (ICM) + + +------- + +**References** + +A.L. Delcher, K.A. Bratke, E.C. Powers, and S.L. Salzberg. Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics (Advance online version) (2007). + + + + diff -r 000000000000 -r 8624069d7a0e glimmer3-main-wrapper.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/glimmer3-main-wrapper.xml Fri Jun 07 07:33:02 2013 -0400 @@ -0,0 +1,232 @@ + + Predict ORFs in prokaryotic genomes (knowlegde-based) + + glimmer + biopython + GLIMMER_SCRIPT_PATH + + + #import tempfile, os + #set $temp = tempfile.NamedTemporaryFile( delete=False ) + # $temp.close() + + glimmer3 + --max_olap $max_olap + --gene_len $gene_len + --threshold $threshold + #if float( $gc_percent ) > 0.0: + --gc_percent $gc_percent + #end if + + #if $stop_codon_opts.stop_codon_opts_selector == "gb": + --trans_table "${stop_codon_opts.genbank_gencode}" + #else: + --stop_codons "${stop_codon_opts.stop_codons}" + #end if + + $linear + $no_indep + $extend + $seq_input + $icm_input + $temp 2>&1; + + ## convert prediction to FASTA sequences + \$GLIMMER_SCRIPT_PATH/glimmer_orf_to_seq.py $temp".predict" $seq_input $genes_output + + #if $report: + mv $temp".predict" $prediction; + #else: + rm $temp".predict"; + #end if + + #if $detailed_report: + mv $temp".detail" $detailed; + #else: + rm $temp".detail"; + #end if + + rm $temp + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + report == True + + + detailed_report == True + + + + + + + + + + + + + + + + + +**What it does** + + This is the main program that makes gene preditions based on an interpolated context model (ICM). + The ICM can be generated either with a de novo prediction (see glimmer Overview) or with extracted CDS from related organisms. + +----- + +**TIP** To extract CDS from a GenBank file use the tool *Extract ORF from a GenBank file*. + +----- + +**Glimmer Overview** + +:: + +************** ************** ************** ************** +* * * * * * * * +* long-orfs * ===> * Extract * ===> * build-icm * ===> * glimmer3 * +* * * * * * * * +************** ************** ************** ************** + +**Example** + +* input:: + + -Genome Sequence + + CELF22B7 C.aenorhabditis elegans (Bristol N2) cosmid F22B7 + GATCCTTGTAGATTTTGAATTTGAAGTTTTTTCTCATTCCAAAACTCTGT + GATCTGAAATAAAATGTCTCAAAAAAATAGAAGAAAACATTGCTTTATAT + TTATCAGTTATGGTTTTCAAAATTTTCTGACATACCGTTTTGCTTCTTTT + TTTCTCATCTTCTTCAAATATCAATTGTGATAATCTGACTCCTAACAATC + GAATTTCTTTTCCTTTTTCTTTTTCCAACAACTCCAGTGAGAACTTTTGA + ATATCTTCAAGTGACTTCACCACATCAGAAGGTGTCAACGATCTTGTGAG + AACATCGAATGAAGATAATTTTAATTTTAGAGTTACAGTTTTTCCTCCGA + CAATTCCTGATTTACGAACATCTTCTTCAAGCATTCTACAGATTTCTTGA + TGCTCTTCTAGGAGGATGTTGAAATCCGAAGTTGGAGAAAAAGTTCTCTC + AACTGAAATGCTTTTTCTTCGTGGATCCGATTCAGATGGACGACCTGGCA + GTCCGAGAGCCGTTCGAAGGAAAGATTCTTGTGAGAGAGGCGTGAAACAC + AAAGGGTATAGGTTCTTCTTCAGATTCATATCACCAACAGTTTGAATATC + CATTGCTTTCAGTTGAGCTTCGCATACACGACCAATTCCTCCAACCTAAA + AAATTATCTAGGTAAAACTAGAAGGTTATGCTTTAATAGTCTCACCTTAC + GAATCGGTAAATCCTTCAAAAACTCCATAATCGCGTTTTTATCATTTTCT + ..... + + + - interpolated context model (ICM) 92: glimmer3-build-icm on data 89 + - maximum overlap length 50 + - minimum gene length. 90 + - threshold score 30 + - linear True + +* output:: + + .predict file + >CELF22B7 C.aenorhabditis elegans (Bristol N2) cosmid F22B7. + orf00001 40137 52 +2 8.68 + orf00004 603 34 -1 2.91 + orf00006 1289 1095 -3 3.16 + orf00007 1555 1391 -2 2.33 + orf00008 1809 1576 -1 1.02 + orf00010 1953 2066 +3 3.09 + orf00011 2182 2304 +1 0.89 + orf00013 2390 2521 +2 0.60 + orf00018 2570 3073 +2 2.54 + orf00020 3196 3747 +1 2.91 + orf00022 3758 4000 +2 0.83 + orf00023 4399 4157 -2 1.31 + orf00025 4463 4759 +2 2.92 + orf00026 4878 5111 +3 0.78 + orf00027 5468 5166 -3 1.64 + orf00029 5590 5832 +1 0.29 + orf00032 6023 6226 +2 6.02 + orf00033 6217 6336 +1 3.09 + ........ + + + .details file + >CELF22B7 C.aenorhabditis elegans (Bristol N2) cosmid F22B7. + Sequence length = 40222 + + ----- Start ----- --- Length ---- ------------- Scores ------------- + ID Frame of Orf of Gene Stop of Orf of Gene Raw InFrm F1 F2 F3 R1 R2 R3 NC + 0001 +2 40137 40137 52 135 135 9.26 96 - 96 - - 3 - 0 + 0002 +1 58 64 180 120 114 5.01 69 69 - - 30 - - 0 + +3 300 309 422 120 111 -0.68 20 - - 20 38 - - 41 + +3 423 432 545 120 111 1.29 21 - 51 21 13 - 8 5 + 0003 +2 401 416 595 192 177 2.51 93 - 93 - 5 - - 1 + 0004 -1 645 552 34 609 516 2.33 99 - - - 99 - - 0 + +1 562 592 762 198 168 -2.54 1 1 - - - - - 98 + +1 763 772 915 150 141 -1.34 1 1 - - - - 86 11 + +3 837 846 1007 168 159 1.35 28 - 50 28 - - 17 3 + 0005 -3 1073 977 654 417 321 0.52 84 - - - - - 84 15 + 0006 -3 1373 1319 1095 276 222 3.80 99 - - - - - 99 0 + 0007 -2 1585 1555 1391 192 162 2.70 98 - - - - 98 - 1 + 0008 -1 1812 1809 1576 234 231 1.26 94 - - - 94 - - 5 + 0009 +2 1721 1730 1945 222 213 0.68 80 - 80 - - - - 19 + ..... + +------- + +**References** + +A.L. Delcher, K.A. Bratke, E.C. Powers, and S.L. Salzberg. Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics (Advance online version) (2007). + + + + + diff -r 000000000000 -r 8624069d7a0e glimmer_acgt_content.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/glimmer_acgt_content.xml Fri Jun 07 07:33:02 2013 -0400 @@ -0,0 +1,55 @@ + + of windows in each sequence + + glimmer + + + window-acgt + $percentage + $input_win_len + $input_win_skip + < $infile > $output + + ##TODO prettify the output + + + + + + + + + + + + + + + + + + +**What it does** + +This tool calculates the ACGT-Content from a given Sequence, given a sliding window. + +------- + +**Output** + +Output is in the format: + + window-start window-len A's C's G's T's #other %GC + +Note the last window in the sequence can be shorter than *window-len* if the sequence ends prematurely + + + + +**References** + +A.L. Delcher, K.A. Bratke, E.C. Powers, and S.L. Salzberg. Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics (Advance online version) (2007). + + + + diff -r 000000000000 -r 8624069d7a0e glimmer_orf_to_seq.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/glimmer_orf_to_seq.py Fri Jun 07 07:33:02 2013 -0400 @@ -0,0 +1,44 @@ +#!/usr/bin/env python +""" +Input: DNA FASTA file + Glimmer ORF file +Output: ORF sequences as FASTA file +Author: Bjoern Gruening +""" +import sys, os +import Bio.SeqIO +from Bio.SeqRecord import SeqRecord + +def glimmer2seq( glimmer_prediction = sys.argv [1], genome_sequence = sys.argv[2], outfile = sys.argv[3] ): + if len(sys.argv) >= 4: + glimmerfile = open( glimmer_prediction, "r") + sequence = open( genome_sequence ) + else: + print "Missing input values." + sys.exit() + + fastafile = Bio.SeqIO.parse(sequence, "fasta") + + sequences = dict() + seq_records = list() + for entry in fastafile: + sequences[entry.description] = entry + + for line in glimmerfile: + if line.startswith('>'): + entry = sequences[ line[1:].strip() ] + else: + orf_start = int(line[8:17]) + orf_end = int(line[18:26]) + + orf_name = line[0:8] + if orf_start <= orf_end: + seq_records.add( SeqRecord( entry.seq[ orf_start-1 : orf_end ], id = orf_name, description = entry.description ) ) + else: + seq_records.add( SeqRecord( entry.seq[ orf_end-1 : orf_start ].reverse_complement(), id = orf_name, description = entry.description ) ) + + SeqIO.write( seq_records, outfile, "fasta" ) + glimmerfile.close() + sequence.close() + +if __name__ == "__main__" : + glimmer2seq() diff -r 000000000000 -r 8624069d7a0e glimmer_orf_to_seq.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/glimmer_orf_to_seq.xml Fri Jun 07 07:33:02 2013 -0400 @@ -0,0 +1,30 @@ + + assigns ORF to its DNA sequence + + biopython + + + glimmer_orf_to_seq.py + $glimmer_orfs + $input_fasta + $output + + + + + + + + + + + + + + +**What it does** + +This tool extract all gene sequences from a genome, which are predicted with Glimmer3. + + + diff -r 000000000000 -r 8624069d7a0e glimmer_predict.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/glimmer_predict.py Fri Jun 07 07:33:02 2013 -0400 @@ -0,0 +1,73 @@ +#!/usr/bin/env python +""" +Input: DNA Fasta File +Output: Tabular +Return Tabular File with predicted ORF's +Bjoern Gruening +""" +import sys, os +import tempfile +import subprocess +import shutil +from glimmer_orf_to_seq import glimmer2seq + +def main(): + genome_seq_file = sys.argv[1] + outfile_classic_glimmer = sys.argv[2] + outfile_ext_path = sys.argv[3] + oufile_genes = sys.argv[8] + + tag = 'glimmer_non_knowlegde_based_prediction' + tempdir = tempfile.gettempdir() + + trainingset = os.path.join( tempdir, tag + ".train" ) + icm = os.path.join( tempdir, tag + ".icm" ) + + longorfs = tempfile.NamedTemporaryFile() + trainingset = tempfile.NamedTemporaryFile() + icm = tempfile.NamedTemporaryFile() + + #glimmeropts = "-o0 -g110 -t30 -l" + glimmeropts = "-o%s -g%s -t%s" % (sys.argv[4], sys.argv[5], sys.argv[6]) + if sys.argv[7] == "true": + glimmeropts += " -l" + + """ + 1. Find long, non-overlapping orfs to use as a training set + """ + subprocess.Popen(["long-orfs", "-n", "-t", "1.15", + genome_seq_file, "-"], stdout = longorfs, + stderr = subprocess.PIPE).communicate() + + """ + 2. Extract the training sequences from the genome file + """ + subprocess.Popen(["extract", "-t", + genome_seq_file, longorfs.name], stdout=trainingset, + stderr=subprocess.PIPE).communicate() + + """ + 3. Build the icm from the training sequences + """ + + # the "-" parameter is used to redirect the output to stdout + subprocess.Popen(["build-icm", "-r", "-"], + stdin=open(trainingset.name), stdout = icm, + stderr=subprocess.PIPE).communicate() + + """ + Run Glimmer3 + """ + b = subprocess.Popen(["glimmer3", glimmeropts, + genome_seq_file, icm.name, os.path.join(tempdir, tag)], + stdout = subprocess.PIPE, stderr=subprocess.PIPE).communicate() + + shutil.copyfile( os.path.join( tempdir, tag + ".predict" ), outfile_classic_glimmer ) + if outfile_ext_path.strip() != 'None': + shutil.copyfile( os.path.join( tempdir, tag + ".detail" ), outfile_ext_path ) + + glimmer2seq( outfile_classic_glimmer, genome_seq_file, oufile_genes ) + + +if __name__ == "__main__" : + main() diff -r 000000000000 -r 8624069d7a0e glimmer_predict.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/glimmer_predict.xml Fri Jun 07 07:33:02 2013 -0400 @@ -0,0 +1,94 @@ + + Predict ORFs in prokaryotic genomes (not knowlegde-based) + + glimmer + biopython + + + glimmer_predict.py + $input + $prediction + #if $detailed_report: + $output_ext + #else: + "None" + #end if + $overlap + $gene_length + $threshold + $linear + $genes_output + + + + + + + + + + + + + report == True + + + detailed_report == True + + + + + + + + + + +**What it does** + +This tool predicts open reading frames (orfs) from a given DNA Sequence. That tool is not knowlegde-based. + +The recommended way is to use a trained Glimmer3 with ICM model. Use the knowlegde-based version for that and insert/generate a training set. + +----- + +**Example** + +Suppose you have the following DNA formatted sequences:: + + >SQ Sequence 8667507 BP; 1203558 A; 3121252 C; 3129638 G; 1213059 T; 0 other; + cccgcggagcgggtaccacatcgctgcgcgatgtgcgagcgaacacccgggctgcgcccg + ggtgttgcgctcccgctccgcgggagcgctggcgggacgctgcgcgtcccgctcaccaag + cccgcttcgcgggcttggtgacgctccgtccgctgcgcttccggagttgcggggcttcgc + cccgctaaccctgggcctcgcttcgctccgccttgggcctgcggcgggtccgctgcgctc + ccccgcctcaagggcccttccggctgcgcctccaggacccaaccgcttgcgcgggcctgg + +Running this tool will produce this:: + + >SQ Sequence 8667507 BP; 1203558 A; 3121252 C; 3129638 G; 1213059 T; 0 other; + orf00001 577 699 +1 5.24 + orf00003 800 1123 +2 5.18 + orf00004 1144 3813 +1 10.62 + orf00006 3857 6220 +2 6.07 + orf00007 6226 7173 +1 1.69 + orf00008 7187 9307 +2 8.95 + orf00009 9424 10410 +1 8.29 + orf00010 10515 11363 +3 7.00 + orf00011 11812 11964 +1 2.80 + orf00012 12360 13457 +3 4.80 + orf00013 14379 14044 -1 7.41 + orf00015 15029 14739 -3 12.43 + orf00016 15066 15227 +3 1.91 + orf00020 16061 15351 -3 2.83 + orf00021 17513 17391 -3 2.20 + orf00023 17529 17675 +3 0.11 + + +------- + +**References** + +A.L. Delcher, K.A. Bratke, E.C. Powers, and S.L. Salzberg. Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics (Advance online version) (2007). + + + diff -r 000000000000 -r 8624069d7a0e readme.rst --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/readme.rst Fri Jun 07 07:33:02 2013 -0400 @@ -0,0 +1,70 @@ +Galaxy wrapper for RepeatMasker +=============================== + +This wrapper is copyright 2012-2013 by Björn Grüning. + +This is a wrapper for the command line tool of Glimmer3. +http://www.cbcb.umd.edu/software/glimmer/ + +Glimmer is a system for finding genes in microbial DNA, +especially the genomes of bacteria, archaea, and viruses. +Glimmer (Gene Locator and Interpolated Markov ModelER) uses interpolated +Markov models (IMMs) to identify the coding regions and distinguish them from noncoding DNA. + +A.L. Delcher, D. Harmon, S. Kasif, O. White, and S.L. Salzberg. Improved microbial gene identification with GLIMMER, Nucleic Acids Research 27:23 (1999), 4636-4641. +S. Salzberg, A. Delcher, S. Kasif, and O. White. Microbial gene identification using interpolated Markov models, Nucleic Acids Research 26:2 (1998), 544-548. +A.L. Delcher, K.A. Bratke, E.C. Powers, and S.L. Salzberg. Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics (Advance online version) (2007). + + + +Installation +============ + +Since version 0.2 the recommended installation procedure is via the Galaxy Tool Shed. + +To install Glimmer3 manually, please download Glimmer3 from http://www.cbcb.umd.edu/software/glimmer/glimmer302.tar.gz +and follow the installation instructions. You can also use packages from your distribution like http://packages.debian.org/stable/science/tigr-glimmer + +To install the wrapper copy the glimmer3 folder in the galaxy tools +folder and modify the tools_conf.xml file to make the tool available to Galaxy. +For example: + + + + + + + + + + + + +History +======= + +- v0.1: Initial public release +- v0.2: Add tool shed integration + + +Wrapper Licence (MIT/BSD style) +=============================== + +Permission to use, copy, modify, and distribute this software and its +documentation with or without modifications and for any purpose and +without fee is hereby granted, provided that any copyright notices +appear in all copies and that both those copyright notices and this +permission notice appear in supporting documentation, and that the +names of the contributors or copyright holders not be used in +advertising or publicity pertaining to distribution of the software +without specific prior permission. + +THE CONTRIBUTORS AND COPYRIGHT HOLDERS OF THIS SOFTWARE DISCLAIM ALL +WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL THE +CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY SPECIAL, INDIRECT +OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS +OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE +OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE +OR PERFORMANCE OF THIS SOFTWARE. + diff -r 000000000000 -r 8624069d7a0e tool_dependencies.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool_dependencies.xml Fri Jun 07 07:33:02 2013 -0400 @@ -0,0 +1,30 @@ + + + + + + + $REPOSITORY_INSTALL_DIR + + + + + http://www.cbcb.umd.edu/software/glimmer/glimmer302b.tar.gz + tar xfvz glimmer302b.tar.gz + cd ./glimmer3.02/src && make + + + ./glimmer3.02/bin + $INSTALL_DIR/bin + + + $INSTALL_DIR/bin + + + + To compile glimmer you need a C compiler (usually gcc). +Glimmer is a system for finding genes in microbial DNA, especially the genomes of bacteria, archaea, and viruses. +Glimmer (Gene Locator and Interpolated Markov ModelER) uses interpolated Markov models (IMMs) to identify the coding regions and distinguish them from noncoding DNA. +http://www.cbcb.umd.edu/software/glimmer/ + +