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Graph Name Retrieved From View
workflow graph bwa_index

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/subworkflows/bwa_index.cwl

Branch/Commit ID: 0864e19de6a1732a1376c6a64a93794d2fc45d23
workflow graph cnv_codex

CNV CODEX2 calling

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/subworkflows/cnv_codex.cwl

Branch/Commit ID: 7fe278136146cbe6567816f1819f0725afeba021
workflow graph samtools_sort

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/subworkflows/samtools_sort.cwl

Branch/Commit ID: 7fe278136146cbe6567816f1819f0725afeba021
workflow graph cnv_codex

CNV CODEX2 calling

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/subworkflows/cnv_codex.cwl

Branch/Commit ID: 0864e19de6a1732a1376c6a64a93794d2fc45d23
workflow graph GSEApy - Gene Set Enrichment Analysis in Python

GSEAPY: Gene Set Enrichment Analysis in Python ============================================== Gene Set Enrichment Analysis is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). GSEA requires as input an expression dataset, which contains expression profiles for multiple samples. While the software supports multiple input file formats for these datasets, the tab-delimited GCT format is the most common. The first column of the GCT file contains feature identifiers (gene ids or symbols in the case of data derived from RNA-Seq experiments). The second column contains a description of the feature; this column is ignored by GSEA and may be filled with “NA”s. Subsequent columns contain the expression values for each feature, with one sample's expression value per column. It is important to note that there are no hard and fast rules regarding how a GCT file's expression values are derived. The important point is that they are comparable to one another across features within a sample and comparable to one another across samples. Tools such as DESeq2 can be made to produce properly normalized data (normalized counts) which are compatible with GSEA.

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

Path: workflows/gseapy.cwl

Branch/Commit ID: e45ab1b9ac5c9b99fdf7b3b1be396dc42c2c9620
workflow graph trimmed_fastq

Quality Control and Raw Data trimming

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/subworkflows/trimmed_fastq.cwl

Branch/Commit ID: 7fe278136146cbe6567816f1819f0725afeba021
workflow graph samtools_sort

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/subworkflows/samtools_sort.cwl

Branch/Commit ID: 0864e19de6a1732a1376c6a64a93794d2fc45d23
workflow graph picard_markduplicates

Mark duplicates

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/subworkflows/picard_markduplicates.cwl

Branch/Commit ID: 7fe278136146cbe6567816f1819f0725afeba021
workflow graph gcaccess_from_list

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

Path: task_types/tt_gcaccess_from_list.cwl

Branch/Commit ID: d39017c63dd8e088f1ad3809d709529df602e05f
workflow graph wf_trim_and_map_se.cwl

This workflow takes in appropriate trimming params and demultiplexed reads, and performs the following steps in order: trimx1, trimx2, fastq-sort, filter repeat elements, fastq-sort, genomic mapping, sort alignment, index alignment, namesort, PCR dedup, sort alignment, index alignment

https://github.com/YeoLab/eclip.git

Path: cwl/wf_trim_and_map_se.cwl

Branch/Commit ID: 49a9bcda10de8f55fab2481f424eb9cdf2e5b256