Assembly with Hifi reads and Trio Data

Generate phased assembly based on PacBio Hifi Reads using parental Illumina data for phasing

Inputs

1. Hifi long reads [fastq]
2. Concatenated Illumina reads : Paternal [fastq]
3. Concatenated Illumina reads : Maternal [fastq]
4. K-mer database [meryldb]
5. Paternal hapmer database [meryldb]
6. Maternal hapmer database [meryldb]
7. Genome profile summary generated by Genomescope [txt]
8. Genome model parameters generated by Genomescope [tabular]

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VGP Workflow #1

This workflow produces a Meryl database and Genomescope outputs that will be used to determine parameters for following workflows, and assess the quality of genome assemblies. Specifically, it provides information about the genomic complexity, such as the genome size and levels of heterozygosity and repeat content, as well about the data quality.

Inputs

• A collection of Hifi long reads in FASTQ format
• k-mer length
• Ploidy

Outputs

• Meryl Database of kmer counts

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Create Meryl Database used for the estimation of assembly parameters and quality control with Merqury. Part of the VGP pipeline.

Workflow for clinical metaproteomics database searching

Contiging Solo:

Generate assembly based on PacBio Hifi Reads.

Inputs

1. Hifi long reads [fastq]
2. K-mer database [meryldb]
3. Genome profile summary generated by Genomescope [txt]
4. Homozygous Read Coverage. Optional, use if you think the estimation from Genomescope is inacurate.
5. Genomescope Model Parameters generated by Genomescope [tabular]
6. Database for busco lineage (recommended: latest)
7. Busco lineage (recommended: vertebrata)
8. Name of first assembly
9. Name of second ...

This workflow will perform taxonomic and functional annotations using Unipept and statistical analysis using MSstatsTMT.

In proteomics research, verifying detected peptides is essential for ensuring data accuracy and biological relevance. This tutorial continues from the clinical metaproteomics discovery workflow, focusing on verifying identified microbial peptides using the PepQuery tool.

The workflow begins with the Database Generation process. The Galaxy-P team has developed a workflow that collects protein sequences from known disease-causing microorganisms to build a comprehensive database. This extensive database is then refined into a smaller, more relevant dataset using the Metanovo tool.

This workflow uses the decoupler tool in Galaxy to generate pseudobulk counts from an annotated AnnData file obtained from scRNA-seq analysis. Following the pseudobulk step, differential expression genes (DEG) are calculated using the edgeR tool. The workflow also includes data sanitation steps to ensure smooth operation of edgeR and minimizing potential issues. Additionally, a Volcano plot tool is used to visualize the results after the DEG analysis.

This workflow can only work on an experimental setup with exactly 2 conditions. It takes two collections of count tables as input and performs differential expression analysis. Additionally it filters for DE genes based on adjusted p-value and log2 fold changes thresholds. It also generates informative plots.