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.