Genome sequencing yields many short reads of DNA from a genome. Genome assembly attempts to reconstruct the original genome from these reads. Most genome assembler software tools are pipelines of many stages. It's not however typical to mix and match stages from different tools. Our Hackseq project aims to create a better assembly tool by mixing and matching components of various assembly tools.

We will work together to create modular assembler components for AssemblerFlow, a tool which builds pipelines of tools for genome assembly using Nextflow. Each participant will create and run a genome assembly pipeline using AssemblerFlow. We will assess the quality of each assembly, using Quast. Finally, we'll create a leader board of awesomeness to compare the assembly results!

Genome sequencing yields many short reads of DNA from a genome. Genome assembly attempts to reconstruct the original genome from these reads. Most genome assembler software tools are pipelines of many stages. These tools all work towards the same goal using different methods, and new tools allow for interoperability between all the different stages of different assemblers.

It is not common to mix and match stages from all the different assembly tools into one and understand the benefits or the disadvantages of doing that, specifically towards understanding the improvement of the assembly quality. Therefore our Hackseq project aims to create a better assembly tool by mixing and matching components of various assembly tools, and visualising the results of this work. The assembly tools that we are considering are displayed here.

We have attempted to solve this problem by creating modular assembler components for AssemblerFlow, a tool which builds pipelines of tools for genome assembly using Nextflow, and we have created components for a number of assembly tools.

1. Create assembly components
   Package up existing assembly tools
2. Develop the pipeline glue that sticks components together
   using Flowcraft, Nextflow, Snakemake, GNU Make, or shell
3. Compose an assembly pipeline of specific tools
   for example, Bcalm → ABySS bubble popping → ABySS repeat resolution → …
4. Assess assembly correctness and contiguity
   Display a constantly-updating leader board of awesomeness

Create assembler components for AssemblerFlow.

• ABySS
• BCALM
• SPAdes
• Unicycler

Bake off!

• Each participant will create and run an AssemblerFlow pipeline
• Assess the quality of each assembly (using Quast)
• Create a leader board of awesomeness! (using RStudio's Shiny or Flexdashboard)
• For inspiration, see http://nucleotid.es/benchmarks/

I created a FlowCraft component for ABySS! I added an option to disable repeat resolution in the SPAdes FlowCraft component. I generated bandage plots and quast statistics for ABySS using FlowCraft. I also created the franken-assembler SPAdes-ABySS (or SPABySS), where SPAdes is run without error correction or repeat resolution, and the unitigs are sent to ABySS for processing all the way to scaffolds.

I worked on creating a flowcraft component for Unicycler, but got into too many errors, and couldn't manage to debug everything on time! For reference of picking up on it for future Frankenstein assemblers here is my commit: https://github.com/figalit/flowcraft/commit/e554d87dc75d19b1aa635752f26b84059ee45e31. I also helped out Reva with R, and joining together all the data for displaying the leader board. I also added the project abstract and initial slides for the group.

I created a KmerGenie component for Flowcraft to determine the optimal k-mer size.

I created a Unicycler component with QUAST for assessment for the text-based workflow system Snakemake. The workflow system produces bacterial genome assemblies from short sequencing reads using Unicycler and evaluates the assembly using QUAST.

I worked on updating contigvalidator to use snakemake, getting it to install smoothly, and getting it into a bioconda package.

I created a BCALM recipe for Bioconda and a BCALM component for Flowcraft. I ran long-read only assemblies with Canu and Miniasm.

During the hackathon I modularized those scripts into 5 components: abyss_contigs_from_unitigs.sh, abyss_scaffolding.sh, abyss_unitigs.sh, bcalm.sh, gfaview_contigs_from_unitigs.sh, and I used these components to build 3 pipelines: ABySS BCALM then ABySS BCALM then gfaview then ABySS. They've been uploaded to https://github.com/GFA-spec/assembler-components Corresponding Shigella sonnei assemblies were created and visualized using Bandage.

I worked on the “Awesome Assemblies” R Shiny web application (along with Figali, and with help from team lead Shaun & Emre). The website currently has a main page which displays a graph showing the Quality versus Accuracy of our newly developed Franken-assemblers.

I created Flowcraft components for QUAST and Bandage. I used Flowcraft to assemble the Shigella sonnei short reads using SPAdes, assess the assembly quality using QUAST, and visualize the assembly graph using Bandage.

I worked on a hybrid assembly using ABySS and LINKS for three Shigella sonnei plasmids. Assembled short reads with ABySS (k=96). Scaffolded the contigs with LINKS, using long reads.

AssemblerFlow builds pipelines of tools for genome assembly. "A Nextflow pipeline assembler for genomics. Pick your modules. Assemble them. Run the pipeline."

Install Homebrew on macOS and Linuxbrew on Linux and Windows 10, using Windows Subsystem for Linux (WSL).

Install XCode.

xcode-select --install
brew cask install bandage docker igv java r-app rstudio
open -a Docker

Install Windows Subsystem for Linux (WSL).

Install RStudio.

brew install jdk@8 r linuxbrew/extra/singularity brewsci/bio/bandage

brew tap brewsci/bio
brew install abyss bcalm bwa flowcraft igv miniasm minimap2 ntcard pilon samtools spades unicycler

Unicycler sample data

• familiarity with the command line and shell scripts is needed
• familiarity with a scripting language such as Python or R is useful
• some familiarity with genome assembly is helpful, but not required
• no need for prior knowledge in AssemblerFlow or NextFlow: they can be learned during the hackathon