Snakemake workflow: Reconstructing raw tomography data

A Snakemake worfklow for tomographically reconstructing raw data using tomopy.

Installation

First download this repo and navigate to it

git clone https://codebase.helmholtz.cloud/gernha62/reconstructing-raw-tomography-data.git 

cd /path/to/repo 

(Optional) Download the example folder with:

wget -m -np https://doi2.psi.ch/datasets/das/work/p15/p15869/compression/MI04_02/tif
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GermlineStructuralV-nf

:wrench: This pipeline is currently under development :wrench:

• Description
• Diagram
• User guide
• Infrastructure usage and recommendations
• Benchmarking
• Workflow summaries
• Metadata
• Component tools
• Additional notes
• Help/FAQ/Troubleshooting

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This workflow take as input a collection of paired fastq. Remove adapters with cutadapt, map pairs with bowtie2 allowing dovetail. Keep MAPQ30 and concordant pairs. BAM to BED. MACS2 with "ATAC" parameters.

This workflow take as input a collection of paired fastq. It uses HiCUP to go from fastq to validPair file. The pairs are filtered for MAPQ and sorted by cooler to generate a tabix dataset. Cooler is used to generate a balanced cool file to the desired resolution.

TronFlow BAM preprocessing pipeline

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TronFlow alignment pipeline

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CoVigator pipeline: variant detection pipeline for Sars-CoV-2

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This workflow take as input a collection of paired fastq. It will remove bad quality and adapters with cutadapt. Map with Bowtie2 end-to-end. Will remove reads on MT and unconcordant pairs and pairs with mapping quality below 30 and PCR duplicates. Will compute the pile-up on 5' +- 100bp. Will call peaks and count the number of reads falling in the 1kb region centered on the summit. Will plot the number of reads for each fragment length.

This workflow takes as input a list of single-read fastqs. Adapters and bad quality bases are removed with cutadapt. Reads are mapped with STAR with ENCODE parameters and genes are counted simultaneously. The counts are reprocess to be similar to HTSeq-count output. FPKM are computed with cufflinks. Coverage (per million mapped reads) are computed with bedtools on uniquely mapped reads.

This workflow takes as input a list of paired-end fastqs. Adapters and bad quality bases are removed with cutadapt. Reads are mapped with STAR with ENCODE parameters and genes are counted simultaneously. The counts are reprocess to be similar to HTSeq-count output. FPKM are computed with cufflinks. Coverage (per million mapped reads) are computed with bedtools on uniquely mapped reads (with R2 orientation inverted).