Scaffolding with Bionano

Scaffolding using Bionano optical map data

Inputs

1. Bionano data [cmap]
2. Estimated genome size [txt]
3. Phased assembly generated by Hifiasm [gfa1]

Outputs

1. Scaffolds
2. Non-scaffolded contigs
3. QC: Assembly statistics
4. QC: Nx plot
5. QC: Size plot

Purge Duplicate Contigs

Purge contigs marked as duplicates by purge_dups in a single haplotype(could be haplotypic duplication or overlap duplication) This workflow is the 6th workflow of the VGP pipeline. It is meant to be run after one of the contigging steps (Workflow 3, 4, or 5)

Inputs

1. Genomescope model parameters [txt] (Generated by the k-mer profiling workflow)
2. Hifi long reads - trimmed [fastq] (Generated by Cutadapt in the contigging workflow)
3. Assembly to purge (e.g. hap1) ...

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 ...

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]

...

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

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

...

This workflow

• Reconstruct phylogeny (insert fragments in a reference)
• Alpha rarefaction analysis
• Taxonomic analysis

Microbiome - Variant calling and Consensus Building

Nanopore datasets analysis - Phylogenetic Identification - antibiotic resistance genes detection and contigs building

Microbiome - QC and Contamination Filtering