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Graph	Name	Retrieved From	View
	cache_asnb_entries	https://github.com/ncbi/pgap.git Path: task_types/tt_cache_asnb_entries.cwl Branch/Commit ID: cd97086739ae5988bab09b05e9259675c4b6bce6
	Running cellranger count and lineage inference	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/single_cell_rnaseq.cwl Branch/Commit ID: a23f42ef49c10a588fd35a3afaad5de03e253533
	exome alignment and somatic variant detection for cle purpose	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/somatic_exome_cle.cwl Branch/Commit ID: 40097e1ed094c5b42b68f3db2ff2cbe78c182479
	Filter Protein Seeds I; Find ProSplign Alignments I	https://github.com/ncbi/pgap.git Path: protein_alignment/wf_compart_filter_prosplign.cwl Branch/Commit ID: 62464fecfe319398affdec50d46054a97466f61a
	kmer_top_n_extract	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_top_n_extract.cwl Branch/Commit ID: 6d8d29a2156b93a75f1d1c6952738bd63f6bd98e
	RNA-Seq pipeline single-read strand specific Note: should be updated The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) RNA-Seq basic analysis for strand specific single-read experiment. A corresponded input [FASTQ](http://maq.sourceforge.net/fastq.shtml) file has to be provided. Current workflow should be used only with the single-read RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 3. Use samtools sort to generate coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 1 (after running STAR) 5. Generate BigWig file on the base of sorted BAM file 6. Map input FASTQ file to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 7. Calculate isoform expression level for the sorted BAM file and GTF/TAB annotation file using GEEP reads-counting utility; export results to file	https://github.com/datirium/workflows.git Path: workflows/rnaseq-se-dutp.cwl Branch/Commit ID: ddc35c559d1ac6aab4972fe1a2b63300c4373f54
	bulk_analysis.cwl	https://github.com/hubmapconsortium/sc-atac-seq-pipeline.git Path: steps/bulk_analysis.cwl Branch/Commit ID: 5465f666aac446a6d66fed1819e48a79395f6b02
	Monte Carlo Pi Approximation Workflow This workflow approximates the value of Pi using the Monte Carlo method. It generates random points in a square and calculates how many fall within a unit circle inscribed in the square.	https://github.com/aldbr/dirac-cwl-proto.git Path: test/workflows/pi/pi_complete/description.cwl Branch/Commit ID: 749e1418857109364d02bb43cc8ab524a331daeb
	MAnorm SE - quantitative comparison of ChIP-Seq single-read data What is MAnorm? -------------- MAnorm is a robust model for quantitative comparison of ChIP-Seq data sets of TFs (transcription factors) or epigenetic modifications and you can use it for: * Normalization of two ChIP-seq samples * Quantitative comparison (differential analysis) of two ChIP-seq samples * Evaluating the overlap enrichment of the protein binding sites(peaks) * Elucidating underlying mechanisms of cell-type specific gene regulation How MAnorm works? ---------------- MAnorm uses common peaks of two samples as a reference to build the rescaling model for normalization, which is based on the empirical assumption that if a chromatin-associated protein has a substantial number of peaks shared in two conditions, the binding at these common regions will tend to be determined by similar mechanisms, and thus should exhibit similar global binding intensities across samples. The observed differences on common peaks are presumed to reflect the scaling relationship of ChIP-Seq signals between two samples, which can be applied to all peaks. What do the inputs mean? ---------------- ### General Experiment short name/Alias * short name for you experiment to identify among the others ChIP-Seq SE sample 1 * previously analyzed ChIP-Seq single-read experiment to be used as Sample 1 ChIP-Seq SE sample 2 * previously analyzed ChIP-Seq single-read experiment to be used as Sample 2 Genome * Reference genome to be used for gene assigning ### Advanced Reads shift size for sample 1 * This value is used to shift reads towards 3' direction to determine the precise binding site. Set as half of the fragment length. Default 100 Reads shift size for sample 2 * This value is used to shift reads towards 5' direction to determine the precise binding site. Set as half of the fragment length. Default 100 M-value (log2-ratio) cutoff * Absolute M-value (log2-ratio) cutoff to define biased (differential binding) peaks. Default: 1.0 P-value cutoff * P-value cutoff to define biased peaks. Default: 0.01 Window size * Window size to count reads and calculate read densities. 2000 is recommended for sharp histone marks like H3K4me3 and H3K27ac, and 1000 for TFs or DNase-seq. Default: 2000	https://github.com/datirium/workflows.git Path: workflows/manorm-se.cwl Branch/Commit ID: 87f213456b3f966b773d396cce1fe5a272dad858
	tt_fscr_calls_pass1	https://github.com/ncbi/pgap.git Path: task_types/tt_fscr_calls_pass1.cwl Branch/Commit ID: 7b5130d2408bce82ee15c666b37d931ef6f452e3