Explore Workflows
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EMG pipeline's QIIME workflow
Step 1: Set environment PYTHONPATH, QIIME_ROOT, PATH Step 2: Run QIIME script pick_closed_reference_otus.py ${python} ${qiimeDir}/bin/pick_closed_reference_otus.py -i $1 -o $2 -r ${qiimeDir}/gg_13_8_otus/rep_set/97_otus.fasta -t ${qiimeDir}/gg_13_8_otus/taxonomy/97_otu_taxonomy.txt -p ${qiimeDir}/cr_otus_parameters.txt Step 3: Convert new biom format to old biom format (json) ${qiimeDir}/bin/biom convert -i ${resultDir}/cr_otus/otu_table.biom -o ${resultDir}/cr_otus/${infileBase}_otu_table_json.biom --table-type=\"OTU table\" --to-json Step 4: Convert new biom format to a classic OTU table. ${qiimeDir}/bin/biom convert -i ${resultDir}/cr_otus/otu_table.biom -o ${resultDir}/cr_otus/${infileBase}_otu_table.txt --to-tsv --header-key taxonomy --table-type \"OTU table\" Step 5: Create otu summary ${qiimeDir}/bin/biom summarize-table -i ${resultDir}/cr_otus/otu_table.biom -o ${resultDir}/cr_otus/${infileBase}_otu_table_summary.txt Step 6: Move one of the result files mv ${resultDir}/cr_otus/otu_table.biom ${resultDir}/cr_otus/${infileBase}_otu_table_hdf5.biom Step 7: Create a list of observations awk '{print $1}' ${resultDir}/cr_otus/${infileBase}_otu_table.txt | sed '/#/d' > ${resultDir}/cr_otus/${infileBase}_otu_observations.txt Step 8: Create a phylogenetic tree by pruning GreenGenes and keeping observed otus ${python} ${qiimeDir}/bin/filter_tree.py -i ${qiimeDir}/gg_13_8_otus/trees/97_otus.tree -t ${resultDir}/cr_otus/${infileBase}_otu_observations.txt -o ${resultDir}/cr_otus/${infileBase}_pruned.tree |
![]() Path: workflows/qiime-workflow.cwl Branch/Commit ID: 0fed1c959ab33112eae46fb02fceffa8bb26b811 |
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js-expr-req-wf.cwl#wf
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![]() Path: tests/js-expr-req-wf.cwl Branch/Commit ID: 368b562a1449e8cd39ae8b7f05926b2bfb9b22df Packed ID: wf |
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PCA - Principal Component Analysis
Principal Component Analysis --------------- Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy. |
![]() Path: workflows/pca.cwl Branch/Commit ID: ad948b2691ef7f0f34de38f0102c3cd6f5182b29 |
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EMG QC workflow, (paired end version). Benchmarking with MG-RAST expt.
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![]() Path: workflows/emg-qc-paired.cwl Branch/Commit ID: 930a2cf6fff820c2461b42dd79d71d9379343013 |
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kmer_top_n
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![]() Path: task_types/tt_kmer_top_n.cwl Branch/Commit ID: 33414c888997d558bdcb558ca33c3a728a3e6143 |
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kmer_ref_compare_wnode
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![]() Path: task_types/tt_kmer_ref_compare_wnode.cwl Branch/Commit ID: 5331b0836aa7c451d759ef39dc2062000ac21a47 |
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Downsample and HaplotypeCaller
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![]() Path: definitions/pipelines/downsample_and_recall.cwl Branch/Commit ID: 0b6e8fd8ead7644cf5398395b76af5cf4011686f |
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Gene expression merge - combines RPKM gene expression from several experiments
Gene expression merge - combines RPKM gene expression from several experiments =================================================================================== Workflows merges RPKM gene expression from several experiments based on the values from GeneId, Chrom, TxStart, TxEnd and Strand columns. Reported RPKM columns are renamed based on the experiments names. |
![]() Path: workflows/feature-merge.cwl Branch/Commit ID: e0a30aa1ad516dd2ec0e9ce006428964b840daf4 |
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Detect Variants workflow for WGS pipeline
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![]() Path: definitions/pipelines/detect_variants_wgs.cwl Branch/Commit ID: 0b6e8fd8ead7644cf5398395b76af5cf4011686f |
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Bacterial Annotation, pass 2, blastp-based functional annotation (first pass)
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![]() Path: bacterial_annot/wf_bacterial_annot_pass2.cwl Branch/Commit ID: 5463361069e263ad6455858e054c1337b1d9e752 |