Workflow for gene set enrichment analsysis (GSEA) and co-expression analysis (WGCNA) on transcriptomics data to analyze pathways affected in Porto-Sinusoidal Vascular Disease.

Predict variants and drug resistance from M. tuberculosis sequence samples (Illumina)

Variant Interpretation Pipeline (VIP) that annotates, filters and reports prioritized causal variants in humans, see https://github.com/molgenis/vip for more information.

CNVand

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pod5_by_pore

A Snakemake workflow to take the POD5 files produced by an Oxford Nanopore sequencing run and re-batch them by pore (ie. by channel).

This is useful if you want to run duplex basecalling because you can meaningfully run "dorado duplex" on a single (or a subset of) the POD5 files.

Know issues

It is assumed all POD5 input files are from the same sequencing run, but this is not checked.

beacon-omop-worker-workflows

GBMatch_CNN

Work in progress... Predicting TS & risk from glioblastoma whole slide images

Reference

Upcoming paper: stay tuned...

Dependencies

python 3.7.7

randaugment by Khrystyna Faryna: https://github.com/tovaroe/pathology-he-auto-augment

tensorflow 2.1.0

scikit-survival 0.13.1

pandas 1.0.3

lifelines 0.25.0

Description

The pipeline implemented here predicts transcriptional subtypes and survival of glioblastoma patients based on H&E stained whole slide scans. Sample data is ...

A hecatomb is a great sacrifice or an extensive loss. Heactomb the software empowers an analyst to make data driven decisions to 'sacrifice' false-positive viral reads from metagenomes to enrich for true-positive viral reads. This process frequently results in a great loss of suspected viral sequences / contigs.

For information about installation, usage, tutorial etc please refer to the documentation: https://hecatomb.readthedocs.io/en/latest/

Quick start guide

Install Hecatomb from Bioconda ...

JAX NGS Operations Nextflow DSL2 Pipelines

This repository contains production bioinformatic analysis pipelines for a variety of bulk 'omics data analysis. Please see the Wiki documentation associated with this repository for all documentation and available analysis workflows.

Complete workflow for TANGO as reported in Lecomte et al (2024), "Revealing the dynamics and mechanisms of bacterial interactions in cheese production with metabolic modelling", Metabolic Eng. 83:24-38 https://doi.org/10.1016/j.ymben.2024.02.014

1. Parameters for individual models are obtained by optimization
2. Individual dynamics and community dynamics are simulated
3. Figures for the manuscript are assembled from the results.