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Graph	Name	Retrieved From	View
	Running cellranger count and lineage inference	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/single_cell_rnaseq.cwl Branch/Commit ID: 9161ef43f7bf0e22b365fde9ec92edcb8601798e
	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: b957a4f681bf0ca8ebba4e0d0ec3936bf79620c5
	alignment for mouse with qc	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/alignment_wgs_mouse.cwl Branch/Commit ID: 742dbafb5fb103d8578f48a0576c14dd8dae3b2a
	bact_get_kmer_reference	https://github.com/ncbi/pgap.git Path: task_types/tt_bact_get_kmer_reference.cwl Branch/Commit ID: 001e133e0eedaf0dd8447e3f8b3cc898ec6e3e1d
	phase VCF	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/phase_vcf.cwl Branch/Commit ID: 04d21c33a5f2950e86db285fa0a32a6659198d8a
	WGS QC workflow mouse	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/qc_wgs_mouse.cwl Branch/Commit ID: 844c10a4466ab39c02e5bfa7a210c195b8efa77a
	exome alignment and tumor-only variant detection	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/tumor_only_exome.cwl Branch/Commit ID: f0cdc773e31e4aa116838e8aba4954c31bd3d68b
	kmer_gc_extract_wnode	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_gc_extract_wnode.cwl Branch/Commit ID: 041a234a935c7af7d3db95353ef80c61c88fc010
	FastQC - a quality control tool for high throughput sequence data FastQC - a quality control tool for high throughput sequence data ===================================== FastQC aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing pipelines. It provides a modular set of analyses which you can use to give a quick impression of whether your data has any problems of which you should be aware before doing any further analysis. The main functions of FastQC are: - Import of data from FastQ files (any variant) - Providing a quick overview to tell you in which areas there may be problems - Summary graphs and tables to quickly assess your data - Export of results to an HTML based permanent report - Offline operation to allow automated generation of reports without running the interactive application	https://github.com/datirium/workflows.git Path: workflows/fastqc.cwl Branch/Commit ID: 799575ce58746813f066a665adeacdda252d8cab
	kmer_cache_store	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_cache_store.cwl Branch/Commit ID: 5463361069e263ad6455858e054c1337b1d9e752