QCIF Bioinformatics

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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 ...

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)

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 ...

MetaDEGalaxy: Galaxy workflow for differential abundance analysis of 16s metagenomic data

microPIPE was developed to automate high-quality complete bacterial genome assembly using Oxford Nanopore Sequencing in combination with Illumina sequencing.

To build microPIPE we evaluated the performance of several tools at each step of bacterial genome assembly, including basecalling, assembly, and polishing. Results at each step were validated using the high-quality ST131 Escherichia coli strain EC958 (GenBank: HG941718.1). After appraisal of each step, we selected the best combination of ...

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 ...