New from @dgpratas.bsky.social et al. for analyzing multiple sequences in multi-FASTA format using alignment-free methodologies. Scalable to millions of sequences for pandemic research and more

AltaiR: a C toolkit for alignment-free and temporal analysis of multi-FASTA data doi.org/10.1093/giga...

“Clarivate’s decision rewards journals for continuing the unhelpful practice of keeping peer review information hidden and unintentionally presenting incomplete and inadequate studies as sound science and punishes those journals that are more transparent.” 👏🙌

www.coalition-s.org/blog/how-the...

The DOI URL doesn't seem to be working for the preprint currently. You can find it here: www.biorxiv.org/content/10.1...

8/ Try it out!
LRGE is open-source and ready to integrate into your workflows as a Rust library or CLI application. Whether you’re on a high-performance cluster or a basic laptop, LRGE delivers fast and reliable genome size estimates. Get it here: github.com/mbhall88/lrge

7/ We validated LRGE on 3370 long read bacterial datasets which have associated high-quality RefSeq assemblies 🦠. We also confirmed it generalises to eukaryote organisms 🪰🌱🍞

6/ And it’s efficient! ⚡
LRGE uses significantly less CPU and memory than traditional approaches, making it ideal for both high-performance clusters and resource-limited environments.

5/ LRGE vs. the competition 🔥
LRGE delivers estimates as reliable as assembly-based methods and better than k-mer-based approaches.
Relative error (y-axis) measures the proportional difference between the estimated and true genome size.

4/ LRGE also provides a confidence interval for the estimated genome size, offering users an expected range of variation.

3/ Why choose LRGE?
* Outperforms traditional k-mer-based tools in accuracy and resource usage.
* Comparable in accuracy to quick assembly tools (like Raven) but much faster and with lower memory requirements.
* Built in Rust, with zero external dependencies. 💻

2/ How does it work?
the basic idea is that if we knew the genome size we could calculate the expected number of overlaps between each read and all other reads. We invert this relationship to estimate the genome size based on the observed number of overlaps for each read

1/ Accurate genome size estimation is crucial for genomics, yet many tools are optimised for short reads, leaving long-read datasets underserved. Enter LRGE: a lightweight, fast, and highly efficient tool specifically designed for long-read sequencing technologies.

🌟 Excited to share my latest preprint with @lachlanjmc.bsky.social on @biorxivpreprint.bsky.social: "Genome size estimation from long read overlaps”! 🚀

Check it out here: doi.org/10.1101/2024...
And find the code here: github.com/mbhall88/lrge

🧵👇

Props to eLife for sticking to their guns and essentially telling Clarivate "stuff your Journal Impact Factor, we don't want/need it and neither should anyone else".

Having recently published in eLife, I can attest to the fact that their review process is smooth and high quality.

Join the Interdisciplinary Postdoc Fellowship Program at the European Molecular Biology Laboratory (EMBL), one of the best places to do research in modern biology and develop your career.

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www.embl.org/eipod-linc

Handy tip of the day: Settings->Moderation->Muted words & tags->[enter last name of buffoon who is going to be president of USA]

🧘‍♂️

For all you Pythonistas out there, if you haven't tried `uv` yet, give it a go. It will blow you mind 🤯 it is essentially `cargo` (from the Rust world) for Python! Amazing it took so long for the ecosystem to get here, but we did. Astral.sh are doing some incredible things, with incredible devs

Microbial Genomics journal is looking for editors at senior level (functional genomics and microbe-host interactions) and handling editors (mainly eukaryotic microbial genomics but any within journal remit welcome). If you want to know more, let me know! microbiologysociety.org/who-we-are/j....