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Trim Galore RNA-Seq pipeline single-read strand specific
Note: should be updated The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **single-end** experiment. A corresponded input [FASTQ](http://maq.sourceforge.net/fastq.shtml) file has to be provided. Current workflow should be used only with the single-end RNA-Seq data. It performs the following steps: 1. Trim adapters from input FASTQ file 2. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 4. Use 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 |
![]() Path: workflows/trim-rnaseq-se-dutp.cwl Branch/Commit ID: 6bf56698c6fe6e781723dea32bc922b91ef49cf3 |
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DESeq2 Multi-factor Analysis
DESeq2 Multi-factor Analysis ============================ Runs DeSeq2 multi-factor analysis with manual control over major parameters |
![]() Path: workflows/deseq-multi-factor.cwl Branch/Commit ID: 935a78f1aff757f977de4e3672aefead3b23606b |
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RNA-seq (VCF) alelle specific pipeline for single-read data
Allele specific RNA-Seq (using vcf) single-read workflow |
![]() Path: workflows/allele-vcf-rnaseq-se.cwl Branch/Commit ID: 6bf56698c6fe6e781723dea32bc922b91ef49cf3 |
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RNA-Seq pipeline paired-end strand specific
The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **paired-end** experiment. A corresponded input [FASTQ](http://maq.sourceforge.net/fastq.shtml) file has to be provided. Current workflow should be used only with the paired-end RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 3. Use samtools sort to generate coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 1 (after running STAR) 4. Generate BigWig file on the base of sorted BAM file 5. Map input FASTQ files to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 6. Calculate isoform expression level for the sorted BAM file and GTF/TAB annotation file using GEEP reads-counting utility; export results to file |
![]() Path: workflows/rnaseq-pe-dutp.cwl Branch/Commit ID: ad948b2691ef7f0f34de38f0102c3cd6f5182b29 |
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workflow.cwl
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![]() Path: flow_md5checksums/workflow.cwl Branch/Commit ID: 0ecf492419ddaa015f14a163381948c53b3deea6 |
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Build Bowtie indices
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 |
![]() Path: workflows/bowtie-index.cwl Branch/Commit ID: 1a46cb0e8f973481fe5ae3ae6188a41622c8532e |
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bam_filtering
BAM filtering |
![]() Path: structuralvariants/cwl/subworkflows/bam_filtering.cwl Branch/Commit ID: 572d9e6b9264d98967b33d18110b0e1979b21d6c |
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align_sort_sa
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![]() Path: task_types/tt_align_sort_sa.cwl Branch/Commit ID: c18a7e5164cb6b19f06b3d1e869407c118a87f7e |
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GAT - Genomic Association Tester
GAT: Genomic Association Tester ============================================== A common question in genomic analysis is whether two sets of genomic intervals overlap significantly. This question arises, for example, in the interpretation of ChIP-Seq or RNA-Seq data. The Genomic Association Tester (GAT) is a tool for computing the significance of overlap between multiple sets of genomic intervals. GAT estimates significance based on simulation. Gat implemements a sampling algorithm. Given a chromosome (workspace) and segments of interest, for example from a ChIP-Seq experiment, gat creates randomized version of the segments of interest falling into the workspace. These sampled segments are then compared to existing genomic annotations. The sampling method is conceptually simple. Randomized samples of the segments of interest are created in a two-step procedure. Firstly, a segment size is selected from to same size distribution as the original segments of interest. Secondly, a random position is assigned to the segment. The sampling stops when exactly the same number of nucleotides have been sampled. To improve the speed of sampling, segment overlap is not resolved until the very end of the sampling procedure. Conflicts are then resolved by randomly removing and re-sampling segments until a covering set has been achieved. Because the size of randomized segments is derived from the observed segment size distribution of the segments of interest, the actual segment sizes in the sampled segments are usually not exactly identical to the ones in the segments of interest. This is in contrast to a sampling method that permutes segment positions within the workspace. |
![]() Path: workflows/gat-run.cwl Branch/Commit ID: 935a78f1aff757f977de4e3672aefead3b23606b |
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Compute library complexity
This workflow compute library complexity |
![]() Path: workflows/File-formats/bedtools-bam-pbc.cwl Branch/Commit ID: 6eb7fec3bd018addf02bb3285cb56d9453319d5d |