Explore Workflows

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: 1a46cb0e8f973481fe5ae3ae6188a41622c8532e

Inspect provided directory and return filenames. Generate a new directory and return it (including content).

https://github.com/common-workflow-language/cwltool.git

Path: tests/wf/directory.cwl

Branch/Commit ID: ead263db0e167db39ddbdc79b04d343943d129b6

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

Path: definitions/subworkflows/phase_vcf.cwl

Branch/Commit ID: 4bc0a4577d626b65a4b44683e5a1ab2f7d7faf4c

Modified 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-dutp.cwl

Branch/Commit ID: ee66d03be8a7fd61367db40c37a973ff55ece4da

Mark duplicates

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/cwl/subworkflows/picard_markduplicates.cwl

Branch/Commit ID: 572d9e6b9264d98967b33d18110b0e1979b21d6c

https://github.com/common-workflow-language/cwltool.git

Path: tests/wf/scatterfail.cwl

Branch/Commit ID: 56677117e7f7e2ebadfaf321aab7c6c45019dff1

https://github.com/uc-cdis/genomel_pipelines.git

Path: genomel/cwl/workflows/harmonization/harmonization_bwa_mem_prod.cwl

Branch/Commit ID: 13c106834d6c9031de08496faeff521740a0c95f

https://github.com/uc-cdis/genomel_pipelines.git

Path: genomel/cwl/workflows/variant_calling/giab_haplotypecaller.cwl

Branch/Commit ID: 13c106834d6c9031de08496faeff521740a0c95f

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/cwl/subworkflows/samtools_sort.cwl

Branch/Commit ID: 93fbc4e51770d953fc44104a7b09436f75719470

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

Path: definitions/pipelines/chipseq.cwl

Branch/Commit ID: 889a077a20c0fdb01f4ed97aa4bc40f920c37a1a