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Graph Name Retrieved From View
workflow graph Motif Finding with HOMER with random background regions

Motif Finding with HOMER with random background regions --------------------------------------------------- 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. Here is how we generate background for Motifs Analysis ------------------------------------- 1. Take input file with regions in a form of “chr\" “start\" “end\" 2. Sort and remove duplicates from this regions file 3. Extend each region in 20Kb into both directions 4. Merge all overlapped extended regions 5. Subtract not extended regions from the extended ones 6. Randomly distribute not extended regions within the regions that we got as a result of the previous step 7. Get fasta file from these randomly distributed regions (from the previous step). Use it as background 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.cwl

Branch/Commit ID: f3e44d3b0f198cf5245c49011124dc3b6c2b06fd
workflow graph QuantSeq 3' FWD, FWD-UMI or REV for single-read mRNA-Seq data

### Devel version of QuantSeq 3' FWD, FWD-UMI or REV for single-read mRNA-Seq data

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

Path: workflows/trim-quantseq-mrnaseq-se-strand-specific.cwl

Branch/Commit ID: 2caa50434966ebdf4b33e5ca689c2e4df32f9058
workflow graph assm_assm_blastn_wnode

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

Path: task_types/tt_assm_assm_blastn_wnode.cwl

Branch/Commit ID: b4a6e46405c08e0b14ad92f0ab38bcc4a69caa5c
workflow graph bact_get_kmer_reference

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

Path: task_types/tt_bact_get_kmer_reference.cwl

Branch/Commit ID: 16e3915d2a357e2a861b30911c832e5ddc0c1784
workflow graph wgs alignment and somatic variant detection

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

Path: definitions/pipelines/somatic_wgs_nonhuman.cwl

Branch/Commit ID: 788bdc99c1d5b6ee7c431c3c011eb30d385c1370
workflow graph RNA-Seq pipeline single-read stranded mitochondrial

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: 92f1a6da9c4f85fb51340b01b32373a50fde0891
workflow graph Kraken2 Database installation pipeline

This workflow downloads the user-selected pre-built kraken2 database from: https://benlangmead.github.io/aws-indexes/k2 ### __Inputs__ Select a pre-built Kraken2 database to download and use for metagenomic classification: - Available options comprised of various combinations of RefSeq reference genome sets: - [Viral (0.5 GB)](https://genome-idx.s3.amazonaws.com/kraken/k2_viral_20221209.tar.gz), all refseq viral genomes - [MinusB (8.7 GB)](https://genome-idx.s3.amazonaws.com/kraken/k2_minusb_20221209.tar.gz), standard minus bacteria (archaea, viral, plasmid, human1, UniVec_Core) - [PlusPFP-16 (15.0 GB)](https://genome-idx.s3.amazonaws.com/kraken/k2_pluspfp_16gb_20221209.tar.gz), standard (archaea, bacteria, viral, plasmid, human1, UniVec_Core) + (protozoa, fungi & plant) capped at 16 GB (shrunk via random kmer downselect) - [EuPathDB46 (34.1 GB)](https://genome-idx.s3.amazonaws.com/kraken/k2_eupathdb48_20201113.tar.gz), eukaryotic pathogen genomes with contaminants removed (https://veupathdb.org/veupathdb/app) - [16S_gg_13_5 (73 MB)](https://genome-idx.s3.amazonaws.com/kraken/16S_Greengenes13.5_20200326.tgz), Greengenes 16S rRNA database ([release 13.5](https://greengenes.secondgenome.com/?prefix=downloads/greengenes_database/gg_13_5/), 20200326)\n - [16S_silva_138 (112 MB)](https://genome-idx.s3.amazonaws.com/kraken/16S_Silva138_20200326.tgz), SILVA 16S rRNA database ([release 138.1](https://www.arb-silva.de/documentation/release-1381/), 20200827) ### __Outputs__ - k2db, an upstream database used by kraken2 classification tool - compressed_k2db_tar, compressed and tarred kraken2 database directory file for download and use outside of scidap ### __Data Analysis Steps__ 1. download selected pre-built kraken2 database. 2. make available as upstream source for kraken2 metagenomic taxonomic classification. ### __References__ - Wood, D.E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol 20, 257 (2019). https://doi.org/10.1186/s13059-019-1891-0

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

Path: workflows/kraken2-databases.cwl

Branch/Commit ID: 57863b6131d8262c5ce864adaf8e4038401e71a2
workflow graph rnaseq-se.cwl

RNA-Seq basic analysis workflow for single-read experiment.

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

Path: workflows/rnaseq-se.cwl

Branch/Commit ID: 3ceeb2e90f49579369b2e10485908516348381a9
workflow graph kmer_ref_compare_wnode

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

Path: task_types/tt_kmer_ref_compare_wnode.cwl

Branch/Commit ID: 68058b108cb5b0b72ebe244c42eefa2747e1d64a
workflow graph format_rrnas_from_seq_entry

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

Path: task_types/tt_format_rrnas_from_seq_entry.cwl

Branch/Commit ID: 76a9637a06e2102645eae29aff10b6f7185892a5