Workflows
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Assembly and quantification metatranscriptome using metagenome data.
Version: see VERSION
Introduction
MetaGT is a bioinformatics analysis pipeline used for improving and quantification metatranscriptome assembly using metagenome data. The pipeline supports Illumina sequencing data and complete metagenome and metatranscriptome assemblies. The pipeline involves the alignment of metatranscriprome assembly to the metagenome assembly with further extracting CDSs, which are covered by ...
Workflow for LongRead Quality Control and Filtering
- NanoPlot (read quality control) before and after filtering
- Filtlong (read trimming)
- Kraken2 taxonomic read classification before and after filtering
- Minimap2 read filtering based on given references
Other UNLOCK workflows on WorkflowHub: https://workflowhub.eu/projects/16/workflows?view=default
All tool CWL files and other workflows can be found here: https://gitlab.com/m-unlock/cwl/workflows
**How to setup and use an UNLOCK ...
Type: Common Workflow Language
Creators: Bart Nijsse, Jasper Koehorst, Germán Royval
Submitter: Bart Nijsse
Workflow for Illumina Quality Control and Filtering
Multiple paired datasets will be merged into single paired dataset.
Summary:
- FastQC on raw data files
- fastp for read quality trimming
- BBduk for phiX and (optional) rRNA filtering
- Kraken2 for taxonomic classification of reads (optional)
- BBmap for (contamination) filtering using given references (optional)
- FastQC on filtered (merged) data
Other UNLOCK workflows on WorkflowHub: https://workflowhub.eu/projects/16/workflows?view=default ...
Type: Common Workflow Language
Creators: Bart Nijsse, Jasper Koehorst, Changlin Ke
Submitter: Bart Nijsse
RASflow: RNA-Seq Analysis Snakemake Workflow
RASflow is a modular, flexible and user-friendly RNA-Seq analysis workflow.
RASflow can be applied to both model and non-model organisms. It supports mapping RNA-Seq raw reads to both genome and transcriptome (can be downloaded from public database or can be homemade by users) and it can do both transcript- and gene-level Differential Expression Analysis (DEA) when transcriptome is used as mapping reference. It requires little programming skill for ...
The containerised pipeline for profiling shotgun metagenomic data is derived from the MGnify pipeline raw-reads analyses, a well-established resource used for analyzing microbiome data. Key components:
- Quality control and decontamination
- rRNA and ncRNA detection using Rfam database
- Taxonomic classification of SSU and LSU regions
- Abundance analysis with mOTUs
GRAVI: Gene Regulatory Analysis using Variable Inputs
This is a snakemake
workflow for:
- Performing sample QC
- Calling ChIP peaks
- Performing Differential Binding Analysis
- Comparing results across ChIP targets
The minimum required input is one ChIP target with two conditions.
Full documentation can be found here
Snakemake Implementation
The basic workflow is written snakemake
, requiring at least v7.7, and can be called using the following
...
SNP-Calling
GATK Variant calling pipeline for genomic data using Nextflow
Quickstart
Install Nextflow using the following command:
curl -s https://get.nextflow.io | bash
Index reference genome:
$ bwa index /path/to/reference/genome.fa
$ samtools faidx /path/to/reference/genome.fa
$ gatk CreateSequenceDictionary -R /path/to/genome.fa -O genome.dict
Launch the pipeline execution with ...
IGVreport-nf
- Description
- Diagram
- User guide
- Workflow summaries
- Metadata
- Component tools
- Required (minimum) inputs/parameters
- Additional notes
- Help/FAQ/Troubleshooting
- Acknowledgements/citations/credits
Description
Quickly generate [IGV .html
...
ROIforMSI
Source codes for manuscript "Delineating Regions-of-interest for Mass Spectrometry Imaging by Multimodally Corroborated Spatial Segmentation"
"ExampleWorkflow.ipynb" is a methods document to demonstrate the workflow of our multimodal fusion-based spatial segmentation.
"Utilities.py" contains all the tools to implement our method.
"gui.py" and "registration_gui.py" are files to implement linear and nonlinear registration.
(Licence: GPL-3)
To discover causal mutations of inherited diseases it’s common practice to do a trio analysis. In a trio analysis DNA is sequenced of both the patient and parents. Using this method, it’s possible to identify multiple inheritance patterns. Some examples of these patterns are autosomal recessive, autosomal dominant, and de-novo variants, which are represented in the figure below. To elaborate, the most left tree shows an autosomal dominant inhertitance pattern where the offspring inherits a faulty ...