Mercurial > repos > bgruening > glimmer
view glimmer3-main-wrapper.xml @ 1:a6582d591d64
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| author | bgruening |
|---|---|
| date | Fri, 07 Jun 2013 07:45:02 -0400 |
| parents | 8624069d7a0e |
| children | 2d0c26885604 |
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<tool id="glimmer_knowlegde-based" name="Glimmer3" version="0.1"> <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 ) # $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 </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="prediction" format="text" label="Glimmer3 on ${on_string} (Gene Prediction table)"> <filter>report == True</filter> </data> <data name="detailed" format="text" label="Glimmer3 on ${on_string} (detailed report)"> <filter>detailed_report == True</filter> </data> </outputs> <tests> <test> <param name="seqInput" value='glimmer3/seqTest.fa' /> <param name="icmInput" value='glimmer3/icmTest.icm' /> <param name="overlaplen" value="50" /> <param name="genlen" value="90" /> <param name="thresh" value="30" /> <param name="linear" value="-l" /> <output name="output1" file='glimmer3/output1Test.dat' /> <output name="output2" file='glimmer3/output2Test.dat' /> </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 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). </help> </tool>