Tips and recommendations

This training covers one path through bacterial long-read sequencing, start to finish, but it's far from the only source worth knowing. This page is a curated set of pointers to where to go next.

Oxford Nanopore Technologies

nanoporetech.com is the first place to check for anything that's specific to ONT's own hardware, chemistry, or basecalling models — the kind of detail that changes with every new flow cell and kit release, and that this training can only describe as a snapshot in time. Useful starting points: the chemistry technical document for kit and flow cell specifics, the accuracy page for current basecalling benchmarks, and software-docs.nanoporetech.com for Dorado's own documentation (basecalling, barcoding, read trimming).

EPI2ME

EPI2ME is ONT's own free analysis platform: pre-built, production-ready workflows (basecalling, assembly, AMR screening, and more) that you can run without assembling a pipeline yourself the way this training has you do step by step. It's worth trying once you understand what each step is actually doing — the workflows are a faster path to results, and this training's own approach is a better way to first learn what's happening underneath them.

The EPI2ME blog also regularly publishes real public datasets from ONT's own sequencing runs, including the NO-MISS bacterial isolate dataset referenced in this training.

Galaxy Training Network

The Galaxy Training Network publishes free, peer-reviewed bioinformatics tutorials that walk through the same kinds of workflows covered here, but inside Galaxy's point-and-click interface rather than the command line. Several of this training's own sections are built directly on their tutorials — the pathogen detection from Nanopore sequencing data tutorial underpins the AMR module, and the large genome assembly and polishing tutorial underpins assembly and polishing.

Worth going to them when: you want a second explanation of a concept in different words and a different order, when you'd rather click through a workflow than type it, or when you want to try the same analysis on a different dataset without setting anything up locally — Galaxy hosts everything, so there's no environment to install.

Ryan Wick's tools and blog

Ryan Wick (Holt Lab, Monash University) writes and maintains several of the long-read tools this training already uses — Filtlong for read filtering, Porechop for adapter trimming, and Bandage for assembly graph visualisation — plus others you'll likely meet if you go further into bacterial assembly, such as Trycycler (consensus long-read assembly) and Unicycler (hybrid short+long-read assembly). His GitHub repos are a reliable source for actively maintained, well-documented long-read tooling generally, not just the ones named here.

His blog is worth bookmarking separately: it's where he publishes detailed, benchmark-driven write-ups comparing tool and parameter choices head-to-head on real data.

ReAct

ReAct is an independent, non-profit toolbox focused on antimicrobial resistance as a public-health and policy problem, not a bioinformatics one. Its understanding antibiotic resistance section — cited in the AMR annotation module — is a clear, visual explainer of why resistance spreads and why it matters, which is useful context to have before diving into gene-level AMR calls.

Worth going to them when: you want the public-health framing behind the genes you're finding, or a resource to point non-bioinformatics colleagues towards.