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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: 549fac35bf6b8b1c25af0f4f6c3f162c40dc130e

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

Path: task_types/tt_align_sort_sa.cwl

Branch/Commit ID: f390475a4e0898d4933f0a28dae278aa35803eb1

Cell Ranger ARC Aggregate =========================

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

Path: workflows/cellranger-arc-aggr.cwl

Branch/Commit ID: c0ca7b140d776eec223ceb1c620eda17281860c4

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

Path: definitions/subworkflows/molecular_qc.cwl

Branch/Commit ID: 0805e8e0d358136468e0a9f49e06005e41965adc

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: 5561f7ee11dd74848680351411a19aa87b13d27b

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

Path: task_types/tt_kmer_seq_entry_extract_wnode.cwl

Branch/Commit ID: 3bec7182e39cb4af10ed8920639adfa78a28ed81

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: 36fd18f11e939d3908b1eca8d2939402f7a99b0f

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

Path: tests/wf/mut3.cwl

Branch/Commit ID: 6cfef62c21330672538fd5e9b45ec888569c0a6f

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

Path: task_types/tt_blastn_wnode.cwl

Branch/Commit ID: 7b5130d2408bce82ee15c666b37d931ef6f452e3

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

Path: task_types/tt_gcaccess_from_list.cwl

Branch/Commit ID: f390475a4e0898d4933f0a28dae278aa35803eb1