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.
Web page: https://www.biocommons.org.au/
Funding details:Core funding for the Australian BioCommons comes from the National Collaborative Research Infrastructure Strategy (NCRIS) via Bioplatforms Australia, which is subcontracted to The University of Melbourne as the lead agent. This core funding is amplified through coinvestment from BioCommons partners https://www.biocommons.org.au/funding
Related items
Teams: Australian BioCommons
Organizations: Australian BioCommons
https://orcid.org/0000-0002-4032-5331Teams: QCIF Bioinformatics
Organizations: QCIF
Teams: Australian BioCommons
Organizations: Australian BioCommons
https://orcid.org/0000-0002-7396-5757Teams: Sydney Informatics Hub
Organizations: The University of Sydney
Teams: Australian BioCommons, Galaxy Australia, ELIXIR Training, ELIXIR Tools platform
Organizations: University of Melbourne, Australian BioCommons
https://orcid.org/0000-0002-2977-5032Expertise: Biochemistry, Proteomics, Mass Spectrometry Imaging
Tools: Mass spectrometry, Proteomics
Teams: Australian BioCommons
Organizations: University of Melbourne, Australian BioCommons
https://orcid.org/0000-0001-8198-9735Teams: QCIF Bioinformatics, Galaxy Australia
Organizations: QCIF
https://orcid.org/0000-0003-2439-8650Teams: Sydney Informatics Hub
Organizations: Australian BioCommons, The University of Sydney
https://orcid.org/0000-0003-2488-953XTeams: Sydney Informatics Hub
Organizations: Australian BioCommons
https://orcid.org/0000-0003-0419-1476Teams: Galaxy Australia, QCIF Bioinformatics
Organizations: QCIF
https://orcid.org/0000-0002-1480-3563Teams: Sydney Informatics Hub
Organizations: The University of Sydney
https://orcid.org/0000-0001-8449-1502Teams: QCIF Bioinformatics
Organizations: QCIF
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.
Space: Australian BioCommons
Public web page: https://www.biocommons.org.au/
Organisms: Not specified
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 ...
Space: Australian BioCommons
Public web page: https://www.sydney.edu.au/sydney-informatics-hub
Organisms: Not specified
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.
Space: Australian BioCommons
Public web page: https://www.qcif.edu.au/
Organisms: Not specified
Space: Australian BioCommons
Public web page: https://pawsey.org.au/
Organisms: Not specified
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 ...
Space: Australian BioCommons
Public web page: https://usegalaxy.org.au/
Organisms: Not specified
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 ...
Space: Australian BioCommons
Public web page: https://janis.readthedocs.io/
Organisms: Not specified
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.
Space: Australian BioCommons
Public web page: https://mdap.unimelb.edu.au/
Organisms: Not specified
Abstract (Expand)
Authors: V. Murigneux, L. W. Roberts, B. M. Forde, M. D. Phan, N. T. K. Nhu, A. D. Irwin, P. N. A. Harris, D. L. Paterson, M. A. Schembri, D. M. Whiley, S. A. Beatson
Date Published: 25th Jun 2021
Publication Type: Journal
PubMed ID: 34172000
Citation: BMC Genomics. 2021 Jun 25;22(1):474. doi: 10.1186/s12864-021-07767-z.
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 ...
Creators: Ahmed Mehdi, Saskia Hiltemann, Bérénice Batut, Dave Clements
Submitter: Sarah Williams
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] ...
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. (To change format: click on the pencil icon next to the file in the Galaxy history, then "Datatypes", then set "New type" as fastqsanger.gz).
- New default settings for BUSCO: lineage = eukaryota; for Quast: lineage = eukaryotes, genome = large.
- Reports assembly stats into a table called metrics.tsv, ...
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, ...
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
For this workflow:
Inputs:
- assembled-genome.fasta
- hard-repeat-masked-genome.fasta
- If using the mRNAs option, ...
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
About this workflow:
- Run this workflow per tissue.
- Inputs: masked_genome.fasta and the trimmed RNAseq reads ...
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:
- Repeat this workflow separately for datasets from different tissues.
- Inputs = collections ...
Parabricks-Genomics-nf is a GPU-enabled pipeline for alignment and germline short variant calling for short read sequencing data. The pipeline utilises NVIDIA's Clara Parabricks toolkit to dramatically speed up the execution of best practice bioinformatics tools. Currently, this pipeline is configured specifically for NCI's Gadi HPC.
NVIDIA's Clara Parabricks can deliver a significant ...
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 ...