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| author | bgruening |
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| date | Sat, 06 Jul 2013 10:09:30 -0400 |
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<tool id="glimmer_knowlegde-based" name="Glimmer3" version="0.2"> <description>Predict ORFs in prokaryotic genomes (knowlegde-based)</description> <requirements> <requirement type="package" version="3.02b">glimmer</requirement> <requirement type="package" version="1.61">biopython</requirement> <requirement type="set_environment">GLIMMER_SCRIPT_PATH</requirement> </requirements> <command> #import tempfile, os #set $temp = tempfile.NamedTemporaryFile( delete=False ) #silent $temp.close() #set $temp = $temp.name glimmer3 --max_olap $max_olap --gene_len $gene_len --threshold $threshold #if float( str($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 --start_codons $start_codons $linear $no_indep $extend $seq_input $icm_input $temp 2>&1; ## convert prediction to FASTA sequences \$GLIMMER_SCRIPT_PATH/glimmer2seq.py $temp".predict" $seq_input $genes_output; #if $report: mv $temp".predict" $report_output; #else: rm $temp".predict"; #end if #if $detailed_report: mv $temp".detail" $detailed_output; #else: rm $temp".detail"; #end if rm $temp </command> <inputs> <param name="seq_input" type="data" format="fasta" label="Genome Sequence" /> <param name="icm_input" type="data" format="data" label="Interpolated context model (ICM)" /> <param name="max_olap" type="integer" value="50" label="Set maximum overlap length" help="Overlaps this short or shorter are ignored." /> <param name="gene_len" type="integer" value="90" label="Set the minimum gene length to n nucleotides" hrlp="This does not include the bases in the stop codon."/> <param name="threshold" type="integer" value="30" label="Set threshold score for calling as gene" help="If the in-frame score >= N, then the region is given a number and considered a potential gene." /> <param name="gc_percent" type="float" value="0.0" label="Set the GC percentage of the independent model, i.e., the model of intergenic sequence" help="If 0.0 specified, the GC percentage will be counted from the input file." /> <param name="linear" type="boolean" truevalue="--linear" falsevalue="" checked="true" label="Assume linear rather than circular genome, i.e., no wraparound" /> <param name="no_indep" type="boolean" truevalue="--no_indep" falsevalue="" checked="false" label="Don’t use the independent probability score column at all" help="Using this option will produce more short gene predictions." /> <param name="extend" type="boolean" truevalue="--extend" falsevalue="" checked="false" label="Also score orfs that extend off the end of the sequence(s)" /> <param name="start_codons" type="text" value="atg,gtg,ttg" label="Specify start codons as a comma-separated list" /> <conditional name="stop_codon_opts"> <param name="stop_codon_opts_selector" type="select" label="Specify start codons as"> <option value="gb" selected="True">Genbank translation table entry</option> <option value="free_form">Comma-separated list</option> </param> <when value="gb"> <param name="genbank_gencode" type="select" label="Use Genbank translation table to specify stop codons"> <option value="1" select="True">1. Standard</option> <option value="2">2. Vertebrate Mitochondrial</option> <option value="3">3. Yeast Mitochondrial</option> <option value="4">4. Mold, Protozoan, and Coelenterate Mitochondrial Code and the Mycoplasma/Spiroplasma Code</option> <option value="5">5. Invertebrate Mitochondrial</option> <option value="6">6. Ciliate, Dasycladacean and Hexamita Nuclear Code</option> <option value="9">9. Echinoderm Mitochondrial</option> <option value="10">10. Euplotid Nuclear</option> <option value="11">11. Bacteria and Archaea</option> <option value="12">12. Alternative Yeast Nuclear</option> <option value="13">13. Ascidian Mitochondrial</option> <option value="14">14. Flatworm Mitochondrial</option> <option value="15">15. Blepharisma Macronuclear</option> <option value="16">16. Chlorophycean Mitochondrial</option> <option value="21">21. Trematode Mitochondrial</option> <option value="22">22. Scenedesmus obliquus mitochondrial</option> <option value="23">23. Thraustochytrium Mitochondrial</option> <option value="24">24. Pterobranchia mitochondrial</option> </param> </when> <when value="free_form"> <param name="stop_codons" type="text" value="tag,tga,taa" label="Specify stop codons as a comma-separated list" /> </when> </conditional> <param name="report" type="boolean" truevalue="" falsevalue="" checked="false" label="Report the classic glimmer table output"/> <param name="detailed_report" type="boolean" truevalue="" falsevalue="" checked="false" label="Output a detailed gene prediction report as separate file"/> </inputs> <outputs> <data name="genes_output" format="fasta" label="Glimmer3 on ${on_string} (Gene Prediction FASTA)" /> <data name="report_output" format="txt" label="Glimmer3 on ${on_string} (Gene Prediction table)"> <filter>report == True</filter> </data> <data name="detailed_output" format="txt" label="Glimmer3 on ${on_string} (detailed report)"> <filter>detailed_report == True</filter> </data> </outputs> <tests> <test> <param name="seq_input" value='streptomyces_Tu6071_genomic.fasta' /> <param name="icm_input" value='streptomyces_Tu6071_plasmid_genes.icm' /> <param name="max_olap" value="50" /> <param name="gene_len" value="90" /> <param name="threshold" value="30" /> <param name="gc_percent" value="0.0" /> <param name="linear" value="--linear" /> <param name="no_indep" value="" /> <param name="extend" value="" /> <param name="start_codons" value="atg,gtg,ttg" /> <param name="genbank_gencode" value="11" /> <param name="detailed_report" value="" /> <param name="report" value="" /> <output name="genes_output" file='glimmer_w_icm_trans-table-11_genomic.fasta' ftype="fasta" /> </test> </tests> <help> **What it does** This is the main program that makes gene preditions based on an interpolated context model (ICM). The ICM can be generated with extracted CDS from related organisms (ICM builder). If you can't generate an ICM model you can use the non knowlegde-based Glimmer with a de novo prediction. ----- **Example** *Input*:: - interpolated context model (ICM): Use the 'Glimmer ICM builder' tool to create one - Genome Sequence in FASTA format >CELF22B7 C.aenorhabditis elegans (Bristol N2) cosmid F22B7 GATCCTTGTAGATTTTGAATTTGAAGTTTTTTCTCATTCCAAAACTCTGT GATCTGAAATAAAATGTCTCAAAAAAATAGAAGAAAACATTGCTTTATAT TTATCAGTTATGGTTTTCAAAATTTTCTGACATACCGTTTTGCTTCTTTT TTTCTCATCTTCTTCAAATATCAATTGTGATAATCTGACTCCTAACAATC GAATTTCTTTTCCTTTTTCTTTTTCCAACAACTCCAGTGAGAACTTTTGA ATATCTTCAAGTGACTTCACCACATCAGAAGGTGTCAACGATCTTGTGAG AACATCGAATGAAGATAATTTTAATTTTAGAGTTACAGTTTTTCCTCCGA CAATTCCTGATTTACGAACATCTTCTTCAAGCATTCTACAGATTTCTTGA TGCTCTTCTAGGAGGATGTTGAAATCCGAAGTTGGAGAAAAAGTTCTCTC AACTGAAATGCTTTTTCTTCGTGGATCCGATTCAGATGGACGACCTGGCA GTCCGAGAGCCGTTCGAAGGAAAGATTCTTGTGAGAGAGGCGTGAAACAC AAAGGGTATAGGTTCTTCTTCAGATTCATATCACCAACAGTTTGAATATC CATTGCTTTCAGTTGAGCTTCGCATACACGACCAATTCCTCCAACCTAAA AAATTATCTAGGTAAAACTAGAAGGTTATGCTTTAATAGTCTCACCTTAC GAATCGGTAAATCCTTCAAAAACTCCATAATCGCGTTTTTATCATTTTCT ..... *Output*:: - FASTA file with predicted proteins - Glimmer prediction file (optional) >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 ........ - Glimmer detailed report (optional) >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). </help> </tool>