LabDAO

Open your browser, pick a workflow, and hit run—that’s the typical first step. Start by creating a project, selecting a ready-to-use pipeline (e.g., RNA-seq differential expression, single-cell preprocessing, or protein design), and linking your data from local folders or cloud storage. Choose where to execute (local machine, shared cluster, or GPU pool), preview the resource estimate, and submit. While the job runs, you can watch logs stream in real time, pause or resume, and inspect intermediate files without leaving the page. When it finishes, download results, open a notebook to explore outputs, and save the exact environment for a clean rerun later.

For day-to-day analysis, keep everything organized and reproducible. Attach metadata to datasets, lock versions for reference genomes or compound libraries, and document parameters next to each run. Use notebooks to plot QC metrics, compare runs, and annotate findings. Invite collaborators with role-based access, assign tasks, and capture discussion in line with code, notebooks, and figures. Methods sections write themselves when every step is tracked: export a run summary that lists tools, versions, inputs, and settings, then paste it into your manuscript or preprint. If you need to branch an analysis, fork the project and keep both paths visible.

Scaling up is straightforward. Submit hundreds of structure predictions or virtual screens as a batch, schedule hyperparameter sweeps for a model, or shard large metagenomics jobs across compute backends. Tools run in containers for consistent results, and you can bring your own image if you need a custom stack. Build a new pipeline by chaining existing steps, or wrap a script with a small manifest that defines inputs, outputs, and resources. Automate nightly runs through the API, integrate with CI for regression checks, and set spending limits to avoid surprises. Every run records provenance, so debugging and auditing are just a click away.

When it’s time to share, turn your workflow into a reusable module with docs, tests, and example data. Publish it to the community catalog so others can discover, cite, and extend it. Produce a report that bundles figures, tables, and narratives into a single, portable file—ideal for submissions or team updates. If you teach, clone a template course space with locked datasets and prepared notebooks. If you’re exploring a new idea, join a community sprint, request feedback on your pipeline, and find collaborators who can contribute methods, compute, or domain review. The end result: faster experiments, fewer handoffs, and analyses that anyone can reproduce.