Explore Workflows

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Graph	Name	Retrieved From	View
	RNA-seq alelle specific pipeline for single-read data Allele specific RNA-Seq single-read workflow	https://github.com/datirium/workflows.git Path: workflows/allele-rnaseq-se.cwl Branch/Commit ID: e238d1756f1db35571e84d72e1699e5d1540f10c
	bgzip and index VCF	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/bgzip_and_index.cwl Branch/Commit ID: 293dc7b83639d21a56efff2baf9dfe4e97b9b806
	Trim Galore RNA-Seq pipeline paired-end The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) RNA-Seq basic analysis for a pair-end 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-end RNA-Seq data. It performs the following steps: 1. Trim adapters from input FASTQ files 2. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 4. 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 files 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/trim-rnaseq-pe.cwl Branch/Commit ID: 2b8146f76595f0c4d8bf692de78b21280162b1d0
	trim-chipseq-pe.cwl Runs ChIP-Seq BioWardrobe basic analysis with paired-end input data files.	https://github.com/Barski-lab/workflows.git Path: workflows/trim-chipseq-pe.cwl Branch/Commit ID: e706ffe742cfdf713c4315ab2fb56d07f7e688cb
	preprocess fasta Remove reads from fasta files based on sequence stats. Return fasta files with reads passed and reads removed.	https://github.com/MG-RAST/pipeline.git Path: CWL/Workflows/preprocess-fasta.workflow.cwl Branch/Commit ID: 3e967f035c10a176b9457331df0b3374a8562b26
	scatter-wf4.cwl#main	https://github.com/common-workflow-language/cwltool.git Path: tests/wf/scatter-wf4.cwl Branch/Commit ID: 478c2ffc09fb189c4f36ccb82aad945b3db5f9b3 Packed ID: main
	align_sort_sa	https://github.com/ncbi/pgap.git Path: task_types/tt_align_sort_sa.cwl Branch/Commit ID: 6a29751f2b16659c1592f1e94837c989e68f3b8b
	Pairwise genomic regions intersection Pairwise genomic regions intersection ============================================= Overlaps peaks from two ChIP/ATAC experiments	https://github.com/datirium/workflows.git Path: workflows/peak-intersect.cwl Branch/Commit ID: a8eaf61c809d76f55780b14f2febeb363cf6373f
	MAnorm PE - quantitative comparison of ChIP-Seq paired-end 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 PE sample 1 * previously analyzed ChIP-Seq paired-end experiment to be used as Sample 1 ChIP-Seq PE sample 2 * previously analyzed ChIP-Seq paired-end 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-pe.cwl Branch/Commit ID: a8eaf61c809d76f55780b14f2febeb363cf6373f
	abundance abundace profiles from annotated files, for protein and/or rna	https://github.com/MG-RAST/pipeline.git Path: CWL/Workflows/abundance-clca.workflow.cwl Branch/Commit ID: 3e967f035c10a176b9457331df0b3374a8562b26