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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: 57863b6131d8262c5ce864adaf8e4038401e71a2

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

Path: task_types/tt_kmer_seq_entry_extract_wnode.cwl

Branch/Commit ID: 369e2b6c7f4db75099d258729dec1326f55d2cc5

Reverse the lines in a document, then sort those lines.

https://github.com/Duke-GCB/calrissian.git

Path: input-data/revsort-array.cwl

Branch/Commit ID: ceb1c2731dd4c3c20229a5cad06a64a487103c21

A basic differential methylation analysis workflow using BismarkCov formatted bed files as input to the RnBeads tool. Analysis is conducted on region and sites levels according to the sample groups specified by user (limited to 2 conditions in this workflow implementation). See report html files for detailed descriptions of analyses and results interpretation. ### __Inputs__ *General Info:* - Experiment short name/Alias* - a unique name for the sample (e.g. what was used on tubes while processing it) - Condition 1 name - name defining condition/group 1 - Condition 2 name - name defining condition/group 2 - Bismark coverage files* for condition1 - minumum of 2 is required for analysis - Bismark coverage files* for condition2 - minumum of 2 is required for analysis - Sample genome - available options: hg19, hg38, mm9, mm10, rn5 - Genome type - indicate mismark index used for upstream samples (input for conditions 1 and 2) *Advanced:* - Number of threads for steps that support multithreading - default set to `4` *[BismarkCov formatted bed](https://www.bioinformatics.babraham.ac.uk/projects/bismark/Bismark_User_Guide.pdf): The genome-wide cytosine report (optional) is tab-delimited in the following format (1-based coords): <chromosome> <position> <strand> <count methylated> <count unmethylated> <C-context> <trinucleotide context> ### __Outputs__ Intermediate and final downloadable outputs include: - sig_dm_sites.bed ([bed for IGV](https://genome.ucsc.edu/FAQ/FAQformat.html#format1); sig diff meth sites) - sig_dm_sites_annotated.tsv (tsv for TABLE; for each site above, closest single gene annotation) - Site_id, unique indentifer per methylated site - Site_Chr, chromosome of methylated site - Site_position, 1-based position in chr of methylated site - Site_strand, strand of methylated site - Log2_Meth_Quotient, log2 of the quotient in methylation: log2((mean.g1+epsilon)/(mean.g2+epsilon)), where epsilon:=0.01. In case of paired analysis, it is the mean of the pairwise quotients. - FDR, adjusted p-values, all <0.10 assumed to be significant - Coverage_score, value between 0-1000 reflects strength of mean coverage difference between conditions and equals [1000-(1000/(meancov_g1-meancov_g2)^2](https://www.wolframalpha.com/input?i=solve+1000-%281000%2F%28x%5E2%29%29), if meancov_g1-meancov_g2==0, score=0, elif score<1==1, else score - meancov_g1, mean coverage of condition1 - meancov_g2, mean coverage of condition2 - refSeq_id, RefSeq gene id - Gene_id, gene symbol - Chr, gene chromosome - txStart, gene transcription start position - tsEnd, gene transcription end position - txStrand, gene strand - stdout and stderr log files - Packaged RnBeads reports directory (reports.tar.gz) contains: reports/ ├── configuration ├── data_import.html ├── data_import_data ├── data_import_images ├── data_import_pdfs ├── differential_methylation.html ├── differential_methylation_data ├── differential_methylation_images ├── differential_methylation_pdfs ├── preprocessing.html ├── preprocessing_data ├── preprocessing_images ├── preprocessing_pdfs ├── quality_control.html ├── quality_control_data ├── quality_control_images ├── quality_control_pdfs ├── tracks_and_tables.html ├── tracks_and_tables_data ├── tracks_and_tables_images └── tracks_and_tables_pdfs Reported methylation is in the form of regions (genes, promoters, cpg, tiling) and specific sites: - genes - Ensembl gene definitions are downloaded using the biomaRt package. - promoters - A promoter is defined as the region spanning 1,500 bases upstream and 500 bases downstream of the transcription start site of the corresponding gene - cpg - the CpG islands from the UCSC Genome Browser - tiling - a window size of 5 kilobases are defined over the whole genome - sites - all cytosines in the context of CpGs in the respective genome ### __Data Analysis Steps__ 1. generate sample sheet with associated conditions for testing in RnBeads 2. setup rnbeads analyses in R, and run differential methylation analysis 3. process output diffmeth files for regions and sites 4. find single closest gene annotations for all significantly diffmeth sites 5. package and save rnbeads report directory 6. clean up report dir for html outputs ### __References__ - https://rnbeads.org/materials/example_3/differential_methylation.html - Makambi, K. (2003) Weighted inverse chi-square method for correlated significance tests. Journal of Applied Statistics, 30(2), 225234 - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4216143/ - Assenov Y, Müller F, Lutsik P, Walter J, Lengauer T, Bock C. Comprehensive analysis of DNA methylation data with RnBeads. Nat Methods. 2014 Nov;11(11):1138-1140. doi: 10.1038/nmeth.3115. Epub 2014 Sep 28. PMID: 25262207; PMCID: PMC4216143.

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

Path: workflows/diffmeth.cwl

Branch/Commit ID: 57863b6131d8262c5ce864adaf8e4038401e71a2

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

Path: task_types/tt_assm_assm_blastn_wnode.cwl

Branch/Commit ID: 122aba2dafbb63241413c82b725b877c04523aaf

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

Path: task_types/tt_kmer_top_n_extract.cwl

Branch/Commit ID: 369e2b6c7f4db75099d258729dec1326f55d2cc5

Cell Ranger ARC Count Gene Expression + ATAC ============================================

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

Path: workflows/cellranger-arc-count.cwl

Branch/Commit ID: 00ea05e22788029370898fd4c17798b11edf0e57

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 pair-end** 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 the pair-end 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-pe-dutp-mitochondrial.cwl

Branch/Commit ID: 4dcc405133f22c63478b6091fb5f591b6be8950f

The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **ChIP-Seq** basic analysis workflow for a **paired-end** experiment with Trim Galore. _Trim Galore_ is a wrapper around [Cutadapt](https://github.com/marcelm/cutadapt) and [FastQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to consistently apply adapter and quality trimming to FastQ files, with extra functionality for RRBS data. A [FASTQ](http://maq.sourceforge.net/fastq.shtml) input file has to be provided. In outputs it returns coordinate sorted BAM file alongside with index BAI file, quality statistics for both the input FASTQ files, reads coverage in a form of BigWig file, peaks calling data in a form of narrowPeak or broadPeak files, islands with the assigned nearest genes and region type, data for average tag density plot (on the base of BAM file). Workflow starts with running fastx_quality_stats (steps fastx_quality_stats_upstream and fastx_quality_stats_downstream) from FASTX-Toolkit to calculate quality statistics for both upstream and downstream input FASTQ files. At the same time Bowtie is used to align reads from input FASTQ files to reference genome (Step bowtie_aligner). The output of this step is unsorted SAM file which is being sorted and indexed by samtools sort and samtools index (Step samtools_sort_index). Depending on workflow’s input parameters indexed and sorted BAM file could be processed by samtools rmdup (Step samtools_rmdup) to remove all possible read duplicates. In a case when removing duplicates is not necessary the step returns original input BAM and BAI files without any processing. If the duplicates were removed the following step (Step samtools_sort_index_after_rmdup) reruns samtools sort and samtools index with BAM and BAI files, if not - the step returns original unchanged input files. Right after that macs2 callpeak performs peak calling (Step macs2_callpeak). On the base of returned outputs the next step (Step macs2_island_count) calculates the number of islands and estimated fragment size. If the last one is less that 80 (hardcoded in a workflow) macs2 callpeak is rerun again with forced fixed fragment size value (Step macs2_callpeak_forced). If at the very beginning it was set in workflow input parameters to force run peak calling with fixed fragment size, this step is skipped and the original peak calling results are saved. In the next step workflow again calculates the number of islands and estimated fragment size (Step macs2_island_count_forced) for the data obtained from macs2_callpeak_forced step. If the last one was skipped the results from macs2_island_count_forced step are equal to the ones obtained from macs2_island_count step. Next step (Step macs2_stat) is used to define which of the islands and estimated fragment size should be used in workflow output: either from macs2_island_count step or from macs2_island_count_forced step. If input trigger of this step is set to True it means that macs2_callpeak_forced step was run and it returned different from macs2_callpeak step results, so macs2_stat step should return [fragments_new, fragments_old, islands_new], if trigger is False the step returns [fragments_old, fragments_old, islands_old], where sufix \"old\" defines results obtained from macs2_island_count step and sufix \"new\" - from macs2_island_count_forced step. The following two steps (Step bamtools_stats and bam_to_bigwig) are used to calculate coverage on the base of input BAM file and save it in BigWig format. For that purpose bamtools stats returns the number of mapped reads number which is then used as scaling factor by bedtools genomecov when it performs coverage calculation and saves it in BED format. The last one is then being sorted and converted to BigWig format by bedGraphToBigWig tool from UCSC utilities. Step get_stat is used to return a text file with statistics in a form of [TOTAL, ALIGNED, SUPRESSED, USED] reads count. Step island_intersect assigns genes and regions to the islands obtained from macs2_callpeak_forced. Step average_tag_density is used to calculate data for average tag density plot on the base of BAM file.

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

Path: workflows/trim-chipseq-pe.cwl

Branch/Commit ID: 9ee330737f4603e4e959ffe786fbb2046db70a00

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: 64f7fe4438898218fd83133efa25251078f5b27e