Seqera Platform Feature Requests

1. Dataset schema validation at launch: Nextflow workflow schema files (e.g. RNASeq) can reference specific schemas for input files. Action: Only datasets that match the input file schema could be selected and/or launched. 2. Dataset schema validation on creation: Automatic schema validation for datasets can be achieved by either building our own, or using a third-party tool. Action: Users with elevated permissions can define one or more valid schemas, and each dataset added to the workspace is first validated against the schema before being approved for upload to Seqera Platform. 3. Support for Parquet, YAML and JSON formatted data: Support for non-tabular datasets (eg. arbitrary Parquet, YAML and JSON) is useful for users. Action: Allow upload and editing of Parquet, YAML and JSON datasets. Additional potential milestones Integration with new Nextflow data lineage

Datasets within Seqera Platform facilitate structured handling of input sample sheets required for genomics pipelines such as RNA-seq. Currently, researchers often face friction in assembling these datasets, needing to prepare CSV files externally. This could be improved by the following: 1. Dataset creation from Pipeline Launch: Currently, creating datasets requires users to navigate away from the pipeline launch page, disrupting workflow and causing friction. Action: Integrate dataset creation directly within the pipeline launch interface, so users can seamlessly upload or enter sample data without leaving their primary task. 2. Manual inline Dataset editing: Users currently must create or edit sample sheets externally, and upload these files to create or edit Datasets, which is inefficient. Action: An embedded spreadsheet editor within the platform would allow users to quickly and intuitively enter data and make edits directly.

As of Enterprise v25.1, Redis v8 is not officially supported. According to the (outdated) docs, only v7 is supported ( https://docs.seqera.io/platform-enterprise/25.1/enterprise/configuration/overview#seqera-and-redis-databases ) Redis v7 has a huge amount of critical- and high-level CVE vulnerabilities, which make it unsuitable for some production environments. Can we get confirmation that Redis v8 works fine?

Nextflow supports using an S3-compatible endpoint as the work directory, even when using an HPC executor such as SLURM/PBS. Despite this feature, Platform will prevent a user submitting runs using an HPC executor using an S3 bucket as the work directory. As object storage becomes more popular in academic settings, it may be helpful to make this error message a warning rather than a run-stopping error.

Currently, when the pipeline optimization feature is used on the Platform, there’s no clear indication in the UI that it has been applied to a given run. The only way to verify this is by inspecting the resolved configuration and manually checking for process directives associated with the optimized profile. It would be helpful to make this more visible directly in the run details, for example, in the Run Info section or alongside the run’s labels so users can quickly confirm whether optimization was applied without digging into the config. The resolved config can be very lengthy for certain pipelines. Why this matters: Helps users verify that performance improvements (e.g. faster runtimes, cost savings) are expected Helps in troubleshooting by confirming whether optimization was applied Supports reproducibility and auditability in production/regulated settings

AWS will be phasing out support for F1 FPGAs in the very near future in favor of purely supporting F2 instances. However, F2 instances do not benefit from the automated AWS configurations that Batch Forget provides. It would be convenient to have these instances configured automatically via Batch Forge.

It would be helpful to be able to shortcut from the Reports tab directly to Data Explorer, either to download a file that is too big to be downloaded from Reports or to further explore the results directory.

Users who have Azure Data Lake Storage (ADLS) should be able to use Data Explorer for selecting data sources for Nextflow pipeline Runs and Studio sessions.

I tried to build a container with this Bioconda package which has been online for more than 4 months: https://anaconda.org/bioconda/esme_mpich_4_2_3 but there is an error message indicating that it is not found (whereas I can install it on my laptop with conda). I guess that the Seqera Wave database or channel synchronization may not be up to date. Can you help sort it out?

Currently, all workflows in a shared workspace are shared with the entire organization. It would be helpful to provide more fine-grained sharing controls that allow both 1) a central way to modify/adjust pipelines across multiple workspaces 2) specific sharing options that provide access in a subset of the organisation's workspaces