This release Wednesday we are pleased to launch wf-human-sv, a new Nextflow pipeline for the prediction and review of structural variants from whole human genome sequencing data. We also provide a cornucopia of updates, improvements and a couple of bug-fixes to our LabsLauncher product, the EPI2ME Labs tutorials and the wf-artic pipeline.

wf-human-sv provides the core functionality from our research tool, pipeline-structural-variation. These pipelines both work by mapping reads to the reference genome using LRA before identifying candidate SVs using cuteSV. The VCF file of candidate SVs is filtered on the basis of sequence coverage and length characteristics to enrich for high-quality candidate insertions and deletions. The wf-human-sv workflow is implemented using Nextflow and the requisite bioinformatics software has been crafted into a docker container; there is no need to wrangle with the installation of the LRA, cuteSV or other analysis components. In addition to collating the VCF file of quality SVs the workflow prepares an HTML format report summarising the experiment. Please see the example report prepared from the example dataset included with the workflow. The EPI2ME Labs SV tutorial will be updated to reflect these best-practices in an up-coming release.

wf-artic has been updated to version v0.3.0:

Improvements and updates for the targeted genotyping functionality. The
medaka software bundled in the container has been updated and a selection of recommended models have also been included. The workflow’s default medaka model has been updated to r941_prom_variant_g360; this is the recommended model for sequence data base-called using guppy version 4.x. For Guppy 5.x based sequence data, a medaka model should be specified based on pore, platform and base-calling model. Suitable models include e.g. r941_min_hac_variant_g507 and r941_prom_hac_variant_g507 for HAC data from R9.4.1 flowcells sequenced on MinION and PromethION flowcells respectively or r103_hac_variant_g507 for sequences prepared from R10.3 flowcells. The model used can be controlled using the —medaka_model parameter.
The workflow changelog provides additional information on the changes in this version.

The EPI2ME Labs Launcher software has been updated to version v1.2.0:

The LabsLauncher communicates with the Jupyter notebook server in the epi2melabs-notebook (please see below) to better report when the notebook server has successfully started; this avoids the issue where a connected web-browser would report that the server is temporarily unavailable.
The update also includes new functionality for selecting appropriate network ports for the container. This simplifies the usage of the multiple instances of the LabsLauncher software on a larger shared computer.
The updated LabsLauncher is available from the download page.

The epi2melabs-notebook container has been updated to version v1.1.1:

This update includes usability and code updates to improve several tutorials.
The SARS-CoV-2 analysis workflow has been better aligned with the wf-artic workflow and now also includes both NextClade and Pangolin strain identifications.
The EPI2ME_Labs_Tutorial provides a great resource for learning how to write functional bioinformatics notebooks. Do you have a notebook idea that you would like to share? The epi2melabs-notebook container is available through dockerhub and is best installed and controlled using the EPI2ME Labs Launcher.

We look forwards to any feedback and recommendations for new workflows, tutorials or improvements to our current offerings.