The Sydney Informatics Hub is a Core Research Facility of The University of Sydney. We work towards enabling excellence in data and compute intensive research. We provide support, training, and expertise in statistics, data science, artificial intelligence, bioinformatics, software engineering, simulation, visualisation, and research computing. We are creating reusable workflows for bioinformatics on Australia's national supercompute resources & commercial cloud, as an official node of the ...

The National Computational Infrastructure (NCI) is one of Australia’s leading Tier-1 facilities for high-performance computing, data, and storage. It provides specialised services to support bioinformatics workflows, alongside a wide range of applications in science, government, and industry.

The Australian BioCommons enhances digital life science research through world class collaborative distributed infrastructure. It aims to ensure that Australian life science research remains globally competitive, through sustained strategic leadership, research community engagement, digital service provision, training and support.

Working closely with researchers, the QCIF Bioinformatics team apply data management, processing, integration, analysis and visualisation techniques to maximise the potential value of biological and clinical data sets. QCIF Bioinformatics is a partner in the Australian BioCommons.

Galaxy is an open, web-based platform for accessible, reproducible, and transparent computational biological research.

• Accessible: Users can easily run tools without writing code or using the CLI; all via a user-friendly web interface.
• Reproducible: Galaxy captures all the metadata from an analysis, making it completely reproducible.
• Transparent: Users share and publish analyses via interactive pages that can enhance analyses with user annotations.
• Scalable: Galaxy ...

Janis is an open-source Python framework that aims to address the portability and interoperability problems between workflow specifications, by abstracting both the workflow and execution model in order to generate CWL, WDL or Nextflow workflows.

Funding sources:

• Institutional financial support for software engineering and academic contributions from Peter Mac and Melbourne Bioinformatics
• Richard Lupat was supported by a grant from the Peter Mac Foundation
• Bernard Pope was supported by a ...

We are a team of Academic Specialists who collaborate with researchers to enable data-intensive research across the University. We work with researchers at all stages of the research lifecycle, from research design and data collection, all the way through to analysis, visualisation, and interpretation.

This document is adapted from the 16S tutorials available at Galaxy [https://training.galaxyproject.org/training-material/topics/metagenomics/ tutorials/mothur-miseq-sop-short/tutorial.html] and [https://training.galaxyproject.org/training-material/ topics/metagenomics/tutorials/mothur-miseq-sop/tutorial.html]. Please also go through these tutorials for better understandings. Note: The steps mentioned in this document are well suited for V3-V4 regions. However the parameters could be varied if ...

A rapid and portable workflow for pond-side sequencing of bacterial pathogens for sustainable aquaculture using ONT long-read sequencing.

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

Nextflow Pipeline for DeepVariant

This repository contains a Nextflow pipeline for Google’s DeepVariant, optimised for execution on NCI Gadi.

Quickstart Guide

1. Edit the pipeline_params.yml file to include:

• samples: a list of samples, where each sample includes the sample name, BAM file path (ensure corresponding .bai is in the same directory), path to an optional regions-of-interest BED file (set to '' if not required), and the model type.
• ref: path to the reference FASTA (ensure ...

Post-genome assembly quality control workflow using Quast, BUSCO, Meryl, Merqury and Fasta Statistics, with updates November 2024.

Workflow inputs: reads as fastqsanger.gz (not fastq.gz), and primary assembly.fasta. (To change reads format: click on the pencil icon next to the file in the Galaxy history, then "Datatypes", then set "New type" as fastqsanger.gz). Note: the reads should be those that were used for the assembly (i.e., the filtered/cleaned reads), not the raw reads.

What it does: ...

This is part of a series of workflows to annotate a genome, tagged with TSI-annotation. These workflows are based on command-line code by Luke Silver, converted into Galaxy Australia workflows.

The workflows can be run in this order:

• Repeat masking
• RNAseq QC and read trimming
• Find transcripts
• Combine transcripts
• Extract transcripts
• Convert formats
• Fgenesh annotation

Inputs required: assembled-genome.fasta, hard-repeat-masked-genome.fasta, and (because this workflow maps known mRNA ...

Genome assembly workflow for nanopore reads, for TSI

Input:

• Nanopore reads (can be in format: fastq, fastq.gz, fastqsanger, or fastqsanger.gz)

Optional settings to specify when the workflow is run:

• [1] how many input files to split the original input into (to speed up the workflow). default = 0. example: set to 2000 to split a 60 GB read file into 2000 files of ~ 30 MB.
• [2] filtering: min average read quality score. default = 10
• [3] filtering: min read length. default = 200
• [4] ...

Scaffolding using HiC data with YAHS

This workflow has been created from a Vertebrate Genomes Project (VGP) scaffolding workflow.

• For more information about the VGP project see https://galaxyproject.org/projects/vgp/.
• The scaffolding workflow is at https://dockstore.org/workflows/github.com/iwc-workflows/Scaffolding-HiC-VGP8/main:main?tab=info
• Please see that link for the workflow diagram.

Some minor changes have been made to better fit with TSI project data:

• optional inputs of SAK info ...

This is part of a series of workflows to annotate a genome, tagged with TSI-annotation. These workflows are based on command-line code by Luke Silver, converted into Galaxy Australia workflows.

The workflows can be run in this order:

• Repeat masking
• RNAseq QC and read trimming
• Find transcripts
• Combine transcripts
• Extract transcripts
• Convert formats
• Fgenesh annotation

Workflow information:

• Input = genome.fasta.
• Outputs = soft_masked_genome.fasta, hard_masked_genome.fasta, ...

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)

This is part of a series of workflows to annotate a genome, tagged with TSI-annotation. These workflows are based on command-line code by Luke Silver, converted into Galaxy Australia workflows.

The workflows can be run in this order:

• Repeat masking
• RNAseq QC and read trimming
• Find transcripts
• Combine transcripts
• Extract transcripts
• Convert formats
• Fgenesh annotation

About this workflow:

• Inputs: transdecoder-peptides.fasta, transdecoder-nucleotides.fasta
• Runs many steps ...

This is part of a series of workflows to annotate a genome, tagged with TSI-annotation. These workflows are based on command-line code by Luke Silver, converted into Galaxy Australia workflows.

The workflows can be run in this order:

• Repeat masking
• RNAseq QC and read trimming
• Find transcripts
• Combine transcripts
• Extract transcripts
• Convert formats
• Fgenesh annotation

About this workflow:

• Input: merged_transcriptomes.fasta.
• Runs TransDecoder to produce longest_transcripts.fasta ...

This is part of a series of workflows to annotate a genome, tagged with TSI-annotation. These workflows are based on command-line code by Luke Silver, converted into Galaxy Australia workflows.

The workflows can be run in this order:

• Repeat masking
• RNAseq QC and read trimming
• Find transcripts
• Combine transcripts
• Extract transcripts
• Convert formats
• Fgenesh annotation

About this workflow:

• Inputs: multiple transcriptome.gtfs from different tissues, genome.fasta, coding_seqs.fasta, ...

This is part of a series of workflows to annotate a genome, tagged with TSI-annotation. These workflows are based on command-line code by Luke Silver, converted into Galaxy Australia workflows.

The workflows can be run in this order:

• Repeat masking
• RNAseq QC and read trimming
• Find transcripts
• Combine transcripts
• Extract transcripts
• Convert formats
• Fgenesh annotation

This collection houses some scanpy-based scRNAseq workflows on galaxy Australia.

The aim of these workflows is to handle the routine ‘boring’ part of single cell RNAseq data processing. It will produces an ‘AnnData’ object, which can then be used as a base for downstream analysis – either within galaxy or outside of it. AnnData is a standard format used by the ‘scanpy’ python package.

These workflows represent just one way of processing data for a ‘typical’ scRNAseq experiment – there are many ...

The workflows in this collection are from the '16S Microbial Analysis with mothur' tutorial for analysis of 16S data (Saskia Hiltemann, Bérénice Batut, Dave Clements), adapted for piepline use on galaxy australia (Ahmed Mehdi). The workflows developed in galaxy use mothur software package developed by Schloss et al https://pubmed.ncbi.nlm.nih.gov/19801464/.

Please also refer to the 16S tutorials available at Galaxy https://training.galaxyproject.org/training-material/topics/metagenomics/tutorials/mothur-miseq-sop-short/tutorial.html ...

This ARDC and BioCommons sponsored project delivers a key component of BioCommon’s vision for an ecosystem of platforms providing researchers with sophisticated data analysis and digital asset stewardship capabilities. The Bring Your Own Data (BYOD) Platform (https://www.biocommons.org.au/byod-expansion) has enabled highly accessible, highly available, highly scalable analysis and data sharing capabilities for the benefit of life science researchers nationally.

**This WorkflowHub collection ...