Explore Workflows

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

Graph	Name	Retrieved From	View
	exome alignment and germline variant detection, with optitype for HLA typing	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/germline_exome_hla_typing.cwl Branch/Commit ID: 97572e3a088d79f6a4166385f79e79ea77b11470
	cache_asnb_entries	https://github.com/ncbi/pgap.git Path: task_types/tt_cache_asnb_entries.cwl Branch/Commit ID: f2bd4687f06f85ea848b6f1ce04ec97f48525334
	Create Genomic Collection for Bacterial Pipeline, ASN.1 input	https://github.com/ncbi/pgap.git Path: genomic_source/wf_genomic_source_asn.cwl Branch/Commit ID: e2a6cbcc36212433d8fbc804919442787a5e2a49
	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: 389f6edccab082d947bee9c032f59dbdf9f7c325
	Tumor-Only Detect Variants workflow	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/tumor_only_detect_variants.cwl Branch/Commit ID: ecac0fda44df3a8f25ddfbb3e7a023fcbe4cbd0f
	blastp_wnode_naming	https://github.com/ncbi/pgap.git Path: task_types/tt_blastp_wnode_naming.cwl Branch/Commit ID: 42df0c0f9a4e5697abadd9cb52440691fafc8f5d
	Add snv and indel bam-readcount files to a vcf	https://github.com/litd/analysis-workflows.git Path: definitions/subworkflows/vcf_readcount_annotator.cwl Branch/Commit ID: 336f7d1af649f42543baa6be2594cd872919b5b5
	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: e0a30aa1ad516dd2ec0e9ce006428964b840daf4
	scatter GATK HaplotypeCaller over intervals	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/gatk_haplotypecaller_iterator.cwl Branch/Commit ID: b9e7392e72506cadd898a6ac4db330baf6535ab6
	Motif Finding with HOMER with target and background regions from peaks Motif Finding with HOMER with target and background regions from peaks --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/)	https://github.com/datirium/workflows.git Path: workflows/homer-motif-analysis-peak.cwl Branch/Commit ID: 564156a9e1cc7c3679a926c479ba3ae133b1bfd4