sanger-tol/genomeassembly

[!WARNING] We are currently undertaking a large-scale restructuring of the development branch and this branch is not currently in a usable state. If you would like to run the pipeline, please run using the latest official release, or try using the last working commit in this branch.

Introduction

sanger-tol/genomeassembly is a bioinformatics pipeline for a genome assembly for HiFi, Illumina 10x (optional), and HiC data. It performs the following steps: raw assembly, purging from haplotigs, optional polishing, and scaffolding.

Original assembly of HiFi reads is performed using hifiasm assembler in two modes - original and using HiC data (optional). Then assembly is purged from alternative haplotigs using purge_dups. Next optional step is polishing of the purged assembly using Illumina 10X read sequencing. 10X reads are mapped to the full assembly (purged + haplotigs) using Longranger and polishing is implemented using Freebayes. HiC reads are further mapped with bwamem2 to the primary contigs, which are further scaffolded with YaHS using the provided Hi-C data. Polished and scaffolded assemblies are evaluated using GFASTATS, BUSCO and MERQURY.FK

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the LSF infrastructure. This ensures that the pipeline runs on LSF, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources.

Pipeline summary

While the steps are described in a sequential order, many of them can be executed as parallel jobs.

1. Process the input YAML file, combine them by semantics into the data structures required to pass further down the pipeline.
2. Run organnels subworkflow on the HiFi reads.
3. Run hifiasm in the original mode.
4. Produce numerical stats, BUSCO score and QV, completeness metrics, and kmer spectra for [3].
5. If hifiasmhicon option is set
   1. run hifiasm in HiC mode.
   2. produce numerical stats, BUSCO score and QV, completeness metrics, and kmer spectra for [5i].
6. Run purging subworkflow on the primary contigs from [3], i.e. produce the purged assembly and a set of haplotigs. Consider the purged contigs as the primary assembly for further steps.
7. Take haplotigs from [6], merge with haplotigs from [3] and run purging subworkfllow on it. Discard the contigs that were purged away, continue with the purged haplotigs as a representation of the haplotig assembly.
8. Produce numerical stats, BUSCO score and QV, completeness metrics, and kmer spectra for the primary and haplotigs from [6] and [7].
9. If polishing_on
   1. Illumina 10X reads to the joined primary and alt contigs.
   2. polish initial assembly based on the aligment produced in [9i]. Set polished primary contigs as the primary assembly and polished haplotigs as the haplotig assembly.
   3. produce numerical stats, BUSCO score and QV, completeness metrics, and kmer spectra for [9ii].
10. If organelles_on
    1. Run organelles subworkflow on the raw HiFi read data and the joined primary and haplotigs contigs.
11. Map HiC data onto primary contigs.
12. Run scaffolding for primary contigs.
13. Produce numerical stats, BUSCO score and QV, completeness metrics, and kmer spectra for [12].

Usage

[!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.

Currently, it is advised to run the pipeline with docker or singularity as some modules do not have a conda env associated with them.

Now, you can run the pipeline using:

bash nextflow run sanger-tol/genomeassembly \ -profile <docker/singularity/.../institute> \ --input input.yaml \ --outdir <OUTDIR>

[!WARNING] Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

Credits

sanger-tol/genomeassembly was originally written by Ksenia Krashennikova.

We thank the following people for their extensive assistance in the development of this pipeline:

@priyanka-surana for the code review, very helpful coding suggestions, and assistance with pushing this pipeline forward through development.

@mcshane and @c-zhou for the design and implementation of the original pipelines for purging (@mcshane), polishing (@mcshane) and scaffolding (@c-zhou).

TreeVal team Damon-Lee Pointon (@DLBPointon), Yumi Sims (@yumisims) and William Eagles (@weaglesBio) for implementation of the hic-mapping pipeline.

@muffato for help with nf-core integration, dealing with infrastructure and troubleshooting, for the code reviews and valuable suggestions at the different stages of the pipeline development.

@gq1 for the code review, valuable suggestions to the code improvement and contributions to the full test setup.

@mahesh-panchal for nextflow implementation of the purging pipeline, code review and valuable suggestions to the nf-core modules implementation.

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

Citations

If you use sanger-tol/genomeassembly for your analysis, please cite it using the following doi: 10.5281/zenodo.10391851.

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

This pipeline uses code and infrastructure developed and maintained by the nf-core community, reused here under the MIT license.

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.