Explore Workflows

View already parsed workflows here or click here to add your own

Graph	Name	Retrieved From	View
	Feature expression merge - combines feature expression from several experiments Feature expression merge - combines feature expression from several experiments ========================================================================= Workflows merges RPKM (by default) gene expression from several experiments based on the values from GeneId, Chrom, TxStart, TxEnd and Strand columns (by default). Reported unique columns are renamed based on the experiments names.	https://github.com/datirium/workflows.git Path: workflows/feature-merge.cwl Branch/Commit ID: 1131f82a53315cca217a6c84b3bd272aa62e4bca
	FASTQ to BQSR	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/fastq_to_bqsr.cwl Branch/Commit ID: 295e7b7f51727c0f2d6cc86ce817449b2e8dba3c
	bam to trimmed fastqs and biscuit alignments	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/bam_to_trimmed_fastq_and_biscuit_alignments.cwl Branch/Commit ID: 1249b5d4e23d57ca5e3b8ad6d8e5f10ddb019f68
	Gathered Downsample and HaplotypeCaller	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/gathered_downsample_and_recall.cwl Branch/Commit ID: 049f4aeff4c4a1b8421cac9b1c1c1f0da5848315
	transcriptome_assemble.cwl	https://github.com/yyoshiaki/dat2-cwl.git Path: workflow/transcriptome_assemble/transcriptome_assemble.cwl Branch/Commit ID: 4c90bc270e271b24caacaf000116727ff8ec81f8
	umi molecular alignment workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/molecular_alignment.cwl Branch/Commit ID: 441b85003fdc10cf4cbf333d89acb4d23b0fef32
	bird_scatter_values.cwl	https://github.com/ReddyLab/bird-workflow.git Path: 03_bird_scatter_values/bird_scatter_values.cwl Branch/Commit ID: 5e08b9b8a0323a1f4740a65bdb356e9b75074093
	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: ee66d03be8a7fd61367db40c37a973ff55ece4da
	RNA-Seq alignment and transcript/gene abundance workflow	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/rnaseq.cwl Branch/Commit ID: 049f4aeff4c4a1b8421cac9b1c1c1f0da5848315
	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: 1131f82a53315cca217a6c84b3bd272aa62e4bca