Explore Workflows

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

Graph Name Retrieved From View
workflow graph WGS processing workflow for single sample

https://github.com/arvados/arvados-tutorial.git

Path: WGS-processing/cwl/helper/bwamem-gatk-report-wf.cwl

Branch/Commit ID: d147d1d1fafeeea06bd09d9479337b0f5aab43b0
workflow graph Downsample and HaplotypeCaller

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/downsample_and_recall.cwl

Branch/Commit ID: 389f6edccab082d947bee9c032f59dbdf9f7c325
workflow graph 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
workflow graph cache_asnb_entries

https://github.com/ncbi/pgap.git

Path: task_types/tt_cache_asnb_entries.cwl

Branch/Commit ID: f2bd4687f06f85ea848b6f1ce04ec97f48525334
workflow graph 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
workflow graph 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
workflow graph Tumor-Only Detect Variants workflow

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/tumor_only_detect_variants.cwl

Branch/Commit ID: ecac0fda44df3a8f25ddfbb3e7a023fcbe4cbd0f
workflow graph blastp_wnode_naming

https://github.com/ncbi/pgap.git

Path: task_types/tt_blastp_wnode_naming.cwl

Branch/Commit ID: 42df0c0f9a4e5697abadd9cb52440691fafc8f5d
workflow graph 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
workflow graph 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