Explore Workflows

https://github.com/common-workflow-language/cwltool.git

Path: tests/wf/count-lines1-wf.cwl

Branch/Commit ID: 78fe9d41ee5a44f8725dfbd7028e4a5ee42949cf

https://github.com/ncbi/pgap.git

Path: task_types/tt_extract_gencoll_ids.cwl

Branch/Commit ID: 2229f26ec424f9ebeb3db7fec3bd3f84a38c7485

https://github.com/common-workflow-language/cwltool.git

Path: cwltool/schemas/v1.0/v1.0/any-type-compat.cwl

Branch/Commit ID: 814bd0405a7701efc7d63e8f0179df394c7766f7

https://github.com/tmooney/cancer-genomics-workflow.git

Path: definitions/pipelines/alignment_wgs_mouse.cwl

Branch/Commit ID: 233f026ffce240071edda526391be0c03186fce8

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/pvacseq.cwl

Branch/Commit ID: 195b4ab487c939eb32a55d9f78bc1befd100caae

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/somatic_exome_gathered.cwl

Branch/Commit ID: 04d21c33a5f2950e86db285fa0a32a6659198d8a

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

https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git

Path: workflows/qiime-workflow.cwl

Branch/Commit ID: 0fed1c959ab33112eae46fb02fceffa8bb26b811

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/js-expr-req-wf.cwl

Branch/Commit ID: 368b562a1449e8cd39ae8b7f05926b2bfb9b22df

Packed ID: wf

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.

https://github.com/datirium/workflows.git

Path: workflows/pca.cwl

Branch/Commit ID: ad948b2691ef7f0f34de38f0102c3cd6f5182b29

https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git

Path: workflows/emg-qc-paired.cwl

Branch/Commit ID: 930a2cf6fff820c2461b42dd79d71d9379343013