ONTViSc (ONT-based Viral Screening for Biosecurity)

Introduction

eresearchqut/ontvisc is a Nextflow-based bioinformatics pipeline designed to help diagnostics of viruses and viroid pathogens for biosecurity. It takes fastq files generated from either amplicon or whole-genome sequencing using Oxford Nanopore Technologies as input.

The pipeline can either: 1) perform a direct search on the sequenced reads, 2) generate clusters, 3) assemble the reads to generate longer contigs or 4) directly ...

The aim of this workflow is to handle the routine part of shotgun metagenomics data processing. The workflow is using the tools Kraken2 and Bracken for taxonomy classification and the KrakenTools to evaluate diversity metrics. This workflow was tested on Galaxy Australia. A How-to guide for the workflow can be found at: https://github.com/vmurigneu/kraken_howto_ga_workflows/blob/main/pages/taxonomy_kraken2_wf_guide.md

From the R1 and R2 fastq files of a single samples, make a scRNAseq counts matrix, and perform basic QC with scanpy. Then, do further processing by making a UMAP and clustering. Produces a processed AnnData Depreciated: use individual workflows insead for multiple samples

Takes fastqs and reference data, to produce a single cell counts matrix into and save in annData format - adding a column called sample with the sample name.

Take a scRNAseq counts matrix from a single sample, and perform basic QC with scanpy. Then, do further processing by making a UMAP and clustering. Produces a processed AnnData object.

Depreciated: use individual workflows insead for multiple samples

From the R1 and R2 fastq files of a single samples, make a scRNAseq counts matrix, and perform basic QC with scanpy. Then, do further processing by making a UMAP and clustering. Produces a processed AnnData

Depreciated: use individual workflows insead for multiple samples

Basic processing of a QC-filtered Anndata Object. UMAP, clustering e.t.c

Take an anndata file, and perform basic QC with scanpy. Produces a filtered AnnData object.

Takes fastqs and reference data, to produce a single cell counts matrix into and save in annData format - adding a column called sample with the sample name.

Loads a single cell counts matrix into an annData format - adding a column called sample with the sample name. (Input format - matrix.mtx, features.tsv and barcodes.tsv)