Explore Workflows

. This ChIP-Seq pipeline is based on 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 **single-read** experiment with Trim Galore. ### Data Analysis SciDAP starts from the .fastq files which most DNA cores and commercial NGS companies return. Starting from raw data allows us to ensure that all experiments have been processed in the same way and simplifies the deposition of data to GEO upon publication. The data can be uploaded from users computer, downloaded directly from an ftp server of the core facility by providing a URL or from GEO by providing SRA accession number. Our current pipelines include the following steps: 1. Trimming the adapters with TrimGalore. This step is particularly important when the reads are long and the fragments are short-resulting in sequencing adapters at the end of read. If adapter is not removed the read will not map. TrimGalore can recognize standard adapters, such as Illumina or Nexterra/Tn5 adapters. 2. QC 3. (Optional) trimming adapters on 5' or 3' end by the specified number of bases. 4. Mapping reads with BowTie. Only uniquely mapped reads with less than 3 mismatches are used in the downstream analysis. Results are saved as a .bam file. 5. (Optional) Removal of duplicates (reads/pairs of reads mapping to exactly same location). This step is used to remove reads overamplified in PCR. Unfortunately, it may also remove \"good\" reads. We usually do not remove duplicates unless the library is heavily duplicated. Please note that MACS2 will remove 'excessive' duplicates during peak calling ina smart way (those not supported by other nearby reads). 6. Peakcalling by MACS2. (Optionally), it is possible to specify read extension length for MACS2 to use if the length determined automatically is wrong. 7. Generation of BigWig coverage files for display on the browser. The coverage shows the number of fragments at each base in the genome normalized to the number of millions of mapped reads. In the case of PE sequencing the fragments are real, but in the case of single reads the fragments are estimated by extending reads to the average fragment length found by MACS2 or specified by the user in 6. ### Details _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. In outputs it returns coordinate sorted BAM file alongside with index BAI file, quality statistics of the input FASTQ file, 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 step *fastx\_quality\_stats* from FASTX-Toolkit to calculate quality statistics for input FASTQ file. At the same time `bowtie` is used to align reads from input FASTQ file to reference genome *bowtie\_aligner*. The output of this step is unsorted SAM file which is being sorted and indexed by `samtools sort` and `samtools index` *samtools\_sort\_index*. Based on workflow’s input parameters indexed and sorted BAM file can be processed by `samtools markdup` *samtools\_remove\_duplicates* to get rid of duplicated reads. Right after that `macs2 callpeak` performs peak calling *macs2\_callpeak*. On the base of returned outputs the next step *macs2\_island\_count* calculates the number of islands and estimated fragment size. If the last one is less that 80bp (hardcoded in the workflow) `macs2 callpeak` is rerun again with forced fixed fragment size value (*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 estimates fragment size (*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 (*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 (*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-se.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869

This ATAC pipeline is based on 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. The pipeline was adapted for ATAC-Seq single-read data analysis by updating genome coverage step. ### Data Analysis Steps SciDAP starts from the .fastq files which most DNA cores and commercial NGS companies return. Starting from raw data allows us to ensure that all experiments have been processed in the same way and simplifies the deposition of data to GEO upon publication. The data can be uploaded from users computer, downloaded directly from an ftp server of the core facility by providing a URL or from GEO by providing SRA accession number. Our current pipelines include the following steps: 1. Trimming the adapters with TrimGalore. This step is particularly important when the reads are long and the fragments are short as in ATAC -resulting in sequencing adapters at the end of read. If adapter is not removed the read will not map. TrimGalore can recognize standard adapters, such as Nexterra/Tn5 adapters. 2. QC 3. (Optional) trimming adapters on 5' or 3' end by the specified number of bases. 4. Mapping reads with BowTie. Only uniquely mapped reads with less than 3 mismatches are used in the downstream analysis. Results are saved as a .bam file. 5. Reads mapping to chromosome M are removed. Since there are many copies of chromosome M in the cell and it is not protected by histones, some ATAC libraries have up to 50% of reads mapping to chrM. We recommend using OMNI-ATAC protocol that reduces chrM reads and provides better specificity. 6. (Optional) Removal of duplicates (reads/pairs of reads mapping to exactly same location). This step is used to remove reads overamplified in PCR. Unfortunately, it may also remove \"good\" reads. We usually do not remove duplicates unless the library is heavily duplicated. Please note that MACS2 will remove 'excessive' duplicates during peak calling ina smart way (those not supported by other nearby reads). 7. Peakcalling by MACS2. (Optionally), it is possible to specify read extension length for MACS2 to use if the length determined automatically is wrong. 8. Generation of BigWig coverage files for display on the browser. Since the cuts by the Tn5 transposome are 9bp apart, we show coverage by 9bp reads rather than fragments as in ChIP-Seq. The coverage shows the number of fragments at each base in the genome normalized to the number of millions of mapped reads. This way the peak of coverage will be located at the most accessible site. ### Details _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 markdup` *samtools\_remove\_duplicates* to remove all possible read duplicates. 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. To adapt the pipeline for ATAC-Seq data analysis we calculate genome coverage using only the first 9 bp from every read. 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-atacseq-pe.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869

Set FADC pulse for high and low-gain channel. Apply transformations required by the simulation model (e.g., normalization, time shift)

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

Path: workflows/SetReadoutPulseShape.cwl

Branch/Commit ID: 789752af87eb190387ff2acb4c95c7a5cdb961e7

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

Path: task_types/tt_kmer_top_n_extract.cwl

Branch/Commit ID: 41d14ec5e2dfa0fac7eebeefda1f26ccea14c9a0

Workflow runs [Bowtie](http://bowtie-bio.sourceforge.net/tutorial.shtml) v1.2.0 (12/30/2016) to build indices for reference genome provided in a single FASTA file as fasta_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/bowtie-index.cwl

Branch/Commit ID: 57437c1e9f881411b65f79acd64b7cf14df5b901

https://github.com/hubmapconsortium/salmon-rnaseq.git

Path: steps/salmon-quantification.cwl

Branch/Commit ID: 16dd8ca3ce99b7a2660e9549d74344a5e542d567

Workflow runs [Bowtie](http://bowtie-bio.sourceforge.net/tutorial.shtml) v1.2.0 (12/30/2016) to build indices for reference genome provided in a single FASTA file as fasta_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/bowtie-index.cwl

Branch/Commit ID: aebf2355539fdf81fd9082616f8b21440d2691c6

Motif Finding with HOMER with target and background regions from peaks --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/)

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

Path: workflows/homer-motif-analysis-peak.cwl

Branch/Commit ID: 2caa50434966ebdf4b33e5ca689c2e4df32f9058

https://github.com/common-workflow-language/cwl-v1.2.git

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

Branch/Commit ID: 707ebcd2173889604459c5f4ffb55173c508abb3

https://github.com/hubmapconsortium/ome-tiff-pyramid.git

Path: pipeline.cwl

Branch/Commit ID: a063a3404a5f4345292508cb82313bd41ea6c615