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.

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 ...

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 ...

Post-genome assembly quality control workflow using Quast, BUSCO, Meryl, Merqury and Fasta Statistics. Updates November 2023. Inputs: reads as fastqsanger.gz (not fastq.gz), and assembly.fasta. New default settings for BUSCO: lineage = eukaryota; for Quast: lineage = eukaryotes, genome = large. Reports assembly stats into a table called metrics.tsv, including selected metrics from Fasta Stats, and read coverage; reports BUSCO versions and dependencies; and displays these tables in the workflow ...

Post-genome assembly quality control workflow using Quast, BUSCO, Meryl, Merqury and Fasta Statistics. Updates November 2023. Inputs: reads as fastqsanger.gz (not fastq.gz), and assembly.fasta. New default settings for BUSCO: lineage = eukaryota; for Quast: lineage = eukaryotes, genome = large. Reports assembly stats into a table called metrics.tsv, including selected metrics from Fasta Stats, and read coverage; reports BUSCO versions and dependencies; and displays these tables in the workflow ...

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 ...

Welcome to the pipesnake. Let's get started.

Introduction

pipesnake is a bioinformatics best-practice analysis pipeline for phylogenomic reconstruction starting from short-read 'second-generation' sequencing data.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity ...

This is a Nextflow implementaion of the GATK Somatic Short Variant Calling workflow. This workflow can be used to discover somatic short variants (SNVs and indels) from tumour and matched normal BAM files following GATK's Best Practices Workflow. The workflowis currently optimised to run efficiently and at scale on the National Compute Infrastructure, Gadi.

GermlineStructuralV-nf is a pipeline for identifying structural variant events in human Illumina short read whole genome sequence data. GermlineStructuralV-nf identifies structural variant and copy number events from BAM files using Manta, Smoove, and TIDDIT. Variants are then merged using SURVIVOR, ...

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.

The aim of this workflow is to handle the routine part of shotgun metagenomics data processing on Galaxy Australia.

The workflow is using the tools MetaPhlAn2 for taxonomy classification and HUMAnN2 for functional profiling of the metagenomes. The workflow is based on the Galaxy Training tutorial 'Analyses of metagenomics data - The global picture' (Saskia Hiltemann, Bérénice Batut) https://training.galaxyproject.org/training-material/topics/metagenomics/tutorials/general-tutorial/tutorial.html#shotgun-metagenomics-data. ...

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 pipeline 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 ...

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 pipeline 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 ...

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 pipeline 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 ...

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 pipeline 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 ...

16S Microbial Analysis with mothur (short)

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 ...

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 ...

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)

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.

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

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.

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