Ryan Wick

Exploring the IS-capades of Klebsiella pneumoniae: insertion sequences drive metabolic loss in obscure sub-lineages Introduction. Klebsiella pneumoniae is an opportunistic pathogen that causes a wide spectrum of infections within healthcare settings and the community. Four K. pneumoniae sub-lineages, defined using ...

Our new paper on Insertion Sequences (IS) in #Klebsiella

- Lineages have vastly different IS loads and profiles
- An inverse relationship between IS load and metabolic capacity, in particular phosphorus use, consistent with early reductive evolution.

www.microbiologyresearch.org/content/jour...

Phold's manuscript is now available @narjournal.bsky.social thanks to @susiegriggo.bsky.social @npbhavya.bsky.social @vijinim.bsky.social @linsalrob.bsky.social @martinsteinegger.bsky.social @milot.bsky.social @eunbelivable.bsky.social & others not on bsky #phagesky academic.oup.com/nar/article/...

New blog post with some thoughts on @nanoporetech.com and their recent announcement that the P2 Solo will be discontinued:
rrwick.github.io/2026/01/21/p...

Just updated this old blog post:
rrwick.github.io/2023/04/19/v...

Now uses qsv instead of xsv, plus some other miscellaneous tweaks/fixes.

In the 2+ years since I wrote this post, I use my tv function a LOT.

And since both metaMDBG and Myloasm had new versions after the paper was accepted, here's a blog post with an updated benchmark:
rrwick.github.io/2025/09/23/a...
(3/3)

Here's the ultra-short version:
If you want the best possible long-read bacterial genome assemblies, Autocycler is the tool for you! It is computationally intensive (due to the need to generate many alternative input assemblies) but consistently more accurate than other methods.
(2/3)

Autocycler: long-read consensus assembly for bacterial genomes AbstractMotivation. Long-read sequencing enables complete bacterial genome assemblies, but individual assemblers are imperfect and often produce sequence-l

Happy to share that the paper describing Autocycler is now 100% up:
doi.org/10.1093/bioi...
(1/3)

One caveat though: my experience is with isolates that (usually) have a single 'true' sequence to aim for. In a metagenome with natural variation (i.e. a mixture of multiple 'true' sequences), I'm not sure how Dorado/Medaka would behave...

I have limited experience with long-read metagenome assembly, so I don't have any special insight here. But I like the examples shown @floriantrigodet.bsky.social's preprint - it shows how sometimes one strange read (e.g. a chimera) can throw off the assembly.

No, the assemblies are not Dorado/Medaka polished. I predict doing so would significantly reduce error rates, especially for the higher-error assemblies - Dorado/Medaka is quite good at fixing small-to-medium scale errors. But for lower-error assemblies (e.g. Autocycler), it may not change much.

I agree, most real-world MAGs probably have more errors than isolates, especially if low depth. Also, metagenomes may have within-species variation, and then the ideal MAG is (arguably) some sort of consensus. Especially if there is structural variation, this can be a BIG challenge for assemblers.

Boxplots showing the benchmarking results from my blog post: old-vs-new versions of metaMDBG and Myloasm

New blog post!

metaMDBG (@gaetanbenoit.bsky.social) and Myloasm (@jimshaw.bsky.social) have had recent releases, so I updated the benchmarks from the Autocycler paper:
rrwick.github.io/2025/09/23/a...

Both tools improved considerably! Time to update your conda environments 😄

agtools: a software framework to manipulate assembly graphs Assembly graphs are a fundamental data structure used by genome and metagenome assemblers to represent sequences and their overlap information, facilitating the assembler to construct longer genomic f...

Excited to share our latest preprint on agtools, an open-source Python framework for analysing and manipulating assembly graphs. (1/n)

www.biorxiv.org/content/10.1...

#Bioinformatics #genomics #assembly #assemblygraphs #software

Preprint out for myloasm, our new nanopore / HiFi metagenome assembler!

Nanopore's getting accurate, but

1. Can this lead to better metagenome assemblies?
2. How, algorithmically, to leverage them?

with co-author Max Marin @mgmarin.bsky.social, supervised by Heng Li @lh3lh3.bsky.social

1 / N

Amazing, thanks! Will keep an eye out for this preprint.

I haven't yet, but that is absolutely something I should try. I should also more generally educate myself on best practices in telomere assembly, e.g. with that paper Adam linked. This is new to me!

Thanks for this - looks interesting!
In the paper you said: "...Illumina sequencing can generate spurious indels within HTs, especially for HT lengths longer than 14 bp." Do you have a sense of how bad this gets for really long homopolymers, e.g. 20+ bp?

I'm sure there are more robust ways to go about telomere assembly - I'm not very experienced with T2T eukaryote genome assemblies 😬

And my manual telomere fixing was makeshift. I pieced together a few assemblies (mostly from Flye) that extended all the way to the telomeres. And then I manually repaired the telomeres to be exact 6-mer repeats - i.e. I assumed any deviation from the 6-mer was ONT error not real biology.

Since all Illumina sequencing involves some PCR (bridge amplification), I also wonder at what length Illumina reads start to fail with homopolymers. Can they reliably sequence 20-mers? 40-mers? 60-mers? It's a hard question to answer if every sequencing tech struggles with these...

I too am wary. I suppose the hope is that by limiting changes to long homopolymers, polishing will fix more errors than it introduces. I.e. I'm guessing that ONT's long-homopolymer error rate is greater than cross-sample homopolymer differences. But this is very much unproven!

New blog post!

I added a new feature to @gbouras13.bsky.social's Pypolca: homopolymer-only polishing. Potentially useful for cross-sample polishing - early test on Cryptosporidium looks promising.

Check it out here:
rrwick.github.io/2025/09/04/h...

Pleased to say that our preprint benchmarking Nanopore data for MLST, cgMLST, cgSNP & AMR typing from bacterial isolates is out! TL;DR you can get almost perfect results from 50x depth using live SUP basecalling with a GPU in under 20 hours #microsky#IDsky 🦠🧬🖥️ /1
www.medrxiv.org/content/10.1...

However, I hear rumours that ONT might be working on a new move-table-aware bacterial polishing model. See my blog post from Feb for details: rrwick.github.io/2025/02/07/d.... If true, I'll be eager to test it out when released.

Minor new release of @nanoporetech.com's Dorado:
github.com/nanoporetech...

It supports using --bacteria when polishing an assembly with v5.2.0 data, which is nice! But if I understand correctly, it's the same bacterial polishing model from Sep 2024, not a new model.

Good to know - thanks for clarifying. Makes sense for a tool that's designed to work with big metagenomic datasets. I'm using it a bit out of its domain on a bacterial isolate.

The read set was only 240 MB (gzipped), so it is memory hungry. The myloasm docs do acknowledge that it uses more memory than other assemblers.

Also, I ran my tests on an ARM Mac, but the docs suggest that myloasm (specifically the polishing step) will be even faster on x86-64 CPUs with AVX2.

Just ran a few more tests through GNU time:
1 thread: 435 seconds, 10.1 GB RAM
2 threads: 238 seconds, 10.0 GB RAM
4 threads: 133 seconds, 10.1 GB RAM
8 threads: 73 seconds, 10.1 GB RAM
16 threads: 49 seconds, 13.3 GB RAM

I tested myloasm on a 50x Klebsiella isolate, and it was very fast - only took about 1 minute to complete (on my Macbook).