Explore Workflows

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Graph Name Retrieved From View
workflow graph Exome QC workflow

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

Path: definitions/subworkflows/qc_exome_no_verify_bam.cwl

Branch/Commit ID: 18600518ce6539a2e29c1707392a4c5da5687fa3
workflow graph 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: 581156366f91861bd4dbb5bcb59f67d468b32af3
workflow graph workflow.cwl

https://github.com/nal-i5k/organism_onboarding.git

Path: flow_dispatch/2blat/workflow.cwl

Branch/Commit ID: b1e1b906fcfb2c0fad8811fb8ab03009282c1d19
workflow graph Unaligned BAM to BQSR

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

Path: definitions/subworkflows/bam_to_bqsr.cwl

Branch/Commit ID: 735be84cdea041fcc8bd8cbe5728b29ca3586a21
workflow graph downsample unaligned BAM and align

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

Path: definitions/subworkflows/downsampled_alignment.cwl

Branch/Commit ID: 18600518ce6539a2e29c1707392a4c5da5687fa3
workflow graph 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: 7fb8a1ebf8145791440bc2fed9c5f2d78a19d04c
workflow graph protein annotation

Proteins - predict, filter, cluster, identify, annotate

 https://github.com/MG-RAST/pipeline.git

Path: CWL/Workflows/protein-filter-annotation.workflow.cwl

Branch/Commit ID: 6c5d0068bdb4f19a36a653c39964aefb9e5a7b1b
workflow graph merge and annotate svs with population allele freq and vep

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

Path: definitions/subworkflows/merge_svs.cwl

Branch/Commit ID: 18600518ce6539a2e29c1707392a4c5da5687fa3
workflow graph Filter single sample sv vcf from paired read callers(Manta/Smoove)

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

Path: definitions/subworkflows/sv_paired_read_caller_filter.cwl

Branch/Commit ID: 889a077a20c0fdb01f4ed97aa4bc40f920c37a1a
workflow graph running cellranger mkfastq and count

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

Path: definitions/subworkflows/cellranger_mkfastq_and_count.cwl

Branch/Commit ID: 6949082038c1ad36d6e9848b97a2537aef2d3805