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 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: 664de58d95728edbf7d369d894f9037ebe2475fa

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

Path: tests/wf/count-lines1-wf.cwl

Branch/Commit ID: 55ccde7c2fe3e7899136ce8606a341e292d7050a

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

Path: definitions/subworkflows/strelka_and_post_processing.cwl

Branch/Commit ID: b7d9ace34664d3cedb16f2512c8a6dc6debfc8ca

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

Path: definitions/subworkflows/phase_vcf.cwl

Branch/Commit ID: da335d9963418f7bedd84cb2791a0df1b3165ffe

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/count-lines11-extra-step-wf.cwl

Branch/Commit ID: 664835e83eb5e57eee18a04ce7b05fb9d70d77b7

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

Path: definitions/subworkflows/phase_vcf.cwl

Branch/Commit ID: a3e26136043c03192c38c335316d2d36e3e67478

Slightly changed original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for **strand specific single-read** experiment. An additional steps were added to map data to mitochondrial chromosome only and then merge the output. Experiment files in [FASTQ](http://maq.sourceforge.net/fastq.shtml) format either compressed or not can be used. Current workflow should be used only with single-read strand specific RNA-Seq data. It performs the following steps: 1. `STAR` to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. `fastx_quality_stats` to analyze input FASTQ file and generate quality statistics file 3. `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 file 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/rnaseq-se-dutp-mitochondrial.cwl

Branch/Commit ID: 5561f7ee11dd74848680351411a19aa87b13d27b

Workflow runs [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886) to build indices for reference genome provided in a single FASTA file as fasta_file input and GTF annotation file from annotation_gtf_file input. Generated indices are saved in a folder with the name that corresponds to the input genome.

https://github.com/datirium/workflows.git

Path: workflows/star-index.cwl

Branch/Commit ID: 282762f8bbaea57dd488115745ef798e128bade1

AltAnalyze ICGS ===============

https://github.com/datirium/workflows.git

Path: workflows/altanalyze-icgs.cwl

Branch/Commit ID: c6bfa0de917efb536dd385624fc7702e6748e61d

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

Path: task_types/tt_kmer_cache_retrieve.cwl

Branch/Commit ID: 4a44218a713aecc488359be275409414ae8c1434