Workflows
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atavide
is a complete workflow for metagenomics data analysis, including QC/QA, optional host removal, assembly and cross-assembly, and individual read based annotations. We have also built in some advanced analytics including tools to assign annotations from reads to contigs, and to generate metagenome-assembled genomes in several different ways, giving you the power to explore your data!
atavide
is 100% snakemake and conda, so you only need to install the snakemake workflow, and then
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Exome SAMtools Workflow
Exome Alignment Workflow
Summary
This notebook demonstrates how to retrieve metadata associated to the paper A SARS-CoV-2 cytopathicity dataset generated by high-content screening of a large drug repurposing collection and available in IDR at idr0094-ellinger-sarscov2. Over 300 compounds were used in this investigation. This notebook allows the user to calculate the half maximal inhibitory concentration (IC50) for ...
This notebook is about pre-processing the Auditory Brainstem Response (ABR) raw data files provided by Ingham et. al to create a data set for Deep Learning models.
The unprocessed ABR data files are available at Dryad.
Since the ABR raw data are available as zip-archives, these have to be unzipped and the extracted raw data files parsed so that the time ...
Workflow for quality assessment of paired reads and classification using NGTax 2.0 and functional annotation using picrust2. In addition files are exported to their respective subfolders for easier data management in a later stage. Steps:
- FastQC (read quality control)
- NGTax 2.0
- Picrust 2
- Export module for ngtax
Analysis of RNA-seq data starting from BAM and focusing on mRNA, lncRNA and miRNA
This workflow is based on the idea of comparing different gene sets through their semantic interpretation. In many cases, the user studies a specific phenotype (e.g. disease) by analyzing lists of genes resulting from different samples or patients. Their pathway analysis could result in different semantic networks, revealing mechanistic and phenotypic divergence between these gene sets. The workflow of BioTranslator Comparative Analysis compares quantitatively the outputs of pathway analysis, ...
BioTranslator performs sequentially pathway analysis and gene prioritization: A specific operator is executed for each task to translate the input gene set into semantic terms and pinpoint the pivotal-role genes on the derived semantic network. The output consists of the set of statistically significant semantic terms and the associated hub genes (the gene signature), prioritized according to their involvement in the underlying semantic topology.