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Graph	Name	Retrieved From	View
	Varscan Workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/varscan_pre_and_post_processing.cwl Branch/Commit ID: 0c4f4e59c265eb22aed3d2d37b173cb5430773d2
	1st-workflow.cwl	https://github.com/common-workflow-language/cwltool.git Path: tests/wf/1st-workflow.cwl Branch/Commit ID: d64178072bc4fc9700ab80cdf90146890b96587e
	Subworkflow to allow calling different SV callers which require bam files as inputs	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/single_sample_sv_callers.cwl Branch/Commit ID: 31602b94b34ff55876147c7299e1bec47e8d1a31
	Bismark Methylation SE Sequence reads are first cleaned from adapters and transformed into fully bisulfite-converted forward (C->T) and reverse read (G->A conversion of the forward strand) versions, before they are aligned to similarly converted versions of the genome (also C->T and G->A converted). Sequence reads that produce a unique best alignment from the four alignment processes against the bisulfite genomes (which are running in parallel) are then compared to the normal genomic sequence and the methylation state of all cytosine positions in the read is inferred. A read is considered to align uniquely if an alignment has a unique best alignment score (as reported by the AS:i field). If a read produces several alignments with the same number of mismatches or with the same alignment score (AS:i field), a read (or a read-pair) is discarded altogether. On the next step we extract the methylation call for every single C analysed. The position of every single C will be written out to a new output file, depending on its context (CpG, CHG or CHH), whereby methylated Cs will be labelled as forward reads (+), non-methylated Cs as reverse reads (-). The output of the methylation extractor is then transformed into a bedGraph and coverage file. The bedGraph counts output is then used to generate a genome-wide cytosine report which reports the number on every single CpG (optionally every single cytosine) in the genome, irrespective of whether it was covered by any reads or not. As this type of report is informative for cytosines on both strands the output may be fairly large (~46mn CpG positions or >1.2bn total cytosine positions in the human genome).	https://github.com/datirium/workflows.git Path: workflows/bismark-methylation-se.cwl Branch/Commit ID: 36fd18f11e939d3908b1eca8d2939402f7a99b0f
	Add snv and indel bam-readcount files to a vcf	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/vcf_readcount_annotator.cwl Branch/Commit ID: 44ada20f3eeb59005d5bd999d2435102e9bae991
	AltAnalyze CellHarmony AltAnalyze CellHarmony ======================	https://github.com/datirium/workflows.git Path: workflows/altanalyze-cellharmony.cwl Branch/Commit ID: bf80c9339d81a78aefb8de661bff998ed86e836e
	foreign_screening.cwl	https://github.com/ncbi/pgap.git Path: vecscreen/foreign_screening.cwl Branch/Commit ID: a3affd1b9e3e16f0644a25fee1a7b87b99df57b0
	align_merge_sas	https://github.com/ncbi/pgap.git Path: task_types/tt_align_merge_sas.cwl Branch/Commit ID: 7b21dc40840852f3942c31b9c472346ea3f9a3ca
	Alignment without BQSR	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/sequence_to_bqsr_nonhuman.cwl Branch/Commit ID: 2f65fc96207a71b1cda4e246f808bed056608cd0
	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: ee66d03be8a7fd61367db40c37a973ff55ece4da