The GTDB website now has an ANI calculator based on skani that supports uploading of user genomes. Try it at gtdb.ecogenomic.org/tools/skani.
Find more information about @jimshaw.bsky.social fantastic tool at www.nature.com/articles/s41....
I’m recruiting a postdoc to work on algorithms for cancer genome reconstruction. We have access to a rich set of tumour samples sequenced across multiple technologies. If interested, feel free to DM. Please share.
Preprint out! Check out our new long-read metagenomic SNP-caller, SNooPy 😀. Work with Chris Quince. Thread 🧵
👉 www.biorxiv.org/content/10.6...
Optimized k-mer search across millions of bacterial genomes on laptops https://www.biorxiv.org/content/10.1101/2025.11.23.690050v1
Congrats Martin! 🥳🥳
As new human assemblies become available on beta.ensembl.org - which human reference genome will you choose? This article explores the question with insights from Ensembl’s own Fergal Martin - www.nature.com/articles/s41...
#HumanGenomics #Pangenomes #ReferenceGenomes
🚀 Looking for talented PhD students!
Join us in 🇸🇬 Singapore for 1-2 years to push the frontiers of AI for Genomics.
Work on:
🧬 Cancer genome reconstruction
🧫 Cancer genome & cell foundation models
💊 RNA drug & mRNA therapeutic design
#AI #Genomics #PhD
1/5
Our preprint on our new metagenomic HiFi assembler Alice is out 🥳 Based on a *new sketching method* (🧵1/6)
👉 Preprint www.biorxiv.org/content/10.1...
👉 Github github.com/rolandfaure/...
Delighted to finally announce a preprint describing the Q100 project! “A complete diploid human genome benchmark for personalized genomics” For which we finished HG002 to near-perfect accuracy: www.biorxiv.org/content/10.1... 🧵[1/14]
Figure 1(A) ANI quantifies the similarity between two genomes. ANI can be defined as the number of aligned positions where the two aligned bases are identical, divided by the total number of aligned bases. Historically, ANI was calculated using a single gene family for multiple sequence alignment. Another approach finds orthologous genes between two genomes and reports the average similarity between their CDSs. This method was later extended to whole-genome alignment by identifying local alignments and excluding supplementary alignments with lower similarity. (B) Different ANI tools employ various approaches in calculating ANI values. ANIm, OrthoANI, and FastANI use aligners to identify homologous regions, whereas Mash uses k-mer hashing to estimate similarities. Only alignments with higher similarity represented by green arrows are included in ANI calculations, while red arrows, corresponding to paralogs, are excluded. (C) The proposed benchmarking method evaluates the performance of different tools using both real and simulated data. It assumes that more distantly related species on the phylogenetic tree should have lower ANI similarities. This is measured by calculating the statistics of Spearman rank correlation. We expect a negative correlation between ANI and the tree distance (scatter plot on the right). https://academic.oup.com/bib/article/doi/10.1093/bib/bbaf267/8160681
Excited to share our EvANI benchmarking workflow, published in Briefings in Bioinformatics doi.org/10.1093/bib/...
Computing average nucleotide identity (ANI) is neither conceptually nor computationally trivial. Its definition has evolved over years, with different meanings and assumptions (1/5)
Sometimes you meet absolutely incredible bioinfo-magicians.
It was a huge privilege when @shenwei356.bsky.social
joined our group for a year on an @embl.org sabbatical.
While here, he developed a new way of aligning to
millions of bacteria, called LexicMap 1/n
www.nature.com/articles/s41...
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
🌎👩🔬 For 15+ years biology has accumulated petabytes (million gigabytes) of🧬DNA sequencing data🧬 from the far reaches of our planet.🦠🍄🌵
Logan now democratizes efficient access to the world’s most comprehensive genetics dataset. Free and open.
doi.org/10.1101/2024...
Our high-precision metagenomic strain caller, PHLAME, is now published in Cell Reports!! www.cell.com/cell-reports...
PHLAME works on tough sample types -- including those with coexisting strains of a species and low depth.
Proud to share our work on the first complete genome of an Indian individual - now on bioRxiv! 😄
Campolina: A Deep Neural Framework for Accurate Segmentation of Nanopore Signals https://www.biorxiv.org/content/10.1101/2025.07.08.663658v1
📜 Excited to share insights from our recent paper: "Kaminari: a resource-frugal index for approximate colored k-mer queries". The study aims to efficiently identify documents containing a query string, focusing on DNA strings. www.biorxiv.org/content/10.1... 🧬 🖥️ 1/8
Thanks Roland! It’s good to mention here that Hairsplitter is also part of it 😄
Overview of pipeline. Map reads to NCBI database of references, use EM to choose a minimal set, then use mapping info to do read classification
This looks cool from @jlipovac.bsky.social ! Strain-level metag assignment; first use EM +mapping to shrink your ref db, then do read classification
www.biorxiv.org/content/10.1...
Thanks! 😊
Work with @msikic.bsky.social, @rvicedomini.bsky.social, Kresimir Krizanovic
MADRe is open-source, modular, and ready to use.
Check it out:
🔗 github.com/lbcb-sci/MADRe
9/9
A key feature of MADRe is its focus on organisms with sufficient abundance to be assembled.
While low-abundance strains may be underrepresented, this trade-off significantly reduces false-positive identifications, a common issue in strain-level metagenomics.
8/9
We evaluated MADRe on both real and simulated datasets and observed:
✅ Comparable or improved accuracy over existing tools
✅ Clearer and more realistic abundance profiles
✅ Substantial reductions in runtime and memory usage
7/9
While assembly is often considered computationally expensive, we demonstrate that MADRe, by combining assembly with contig-level mapping, is more efficient than directly mapping large volumes of reads to a full reference database. 6/9
To complete the pipeline, MADRe maps reads to the reduced reference database and applies a second round of probabilistic reassignment.
This enhances classification sensitivity and filters false-positive identifications, enabling precise strain-level profiling. 5/9
This reduction identifies candidate genomes present in the sample.
However, this step alone does not eliminate all false positives and does not provide accurate abundance estimates. 4/9
MADRe begins by assembling the metagenomic sample and mapping the resulting contigs - often representing collapsed strains - to a (large) reference database.
Using EM-based read reassignment and info about strain collapses, we construct reduced database. 3/9
Strain-level classification requires large reference databases, especially when there is no prior knowledge about sample composition.
However, mapping reads to such large databases is computationally expensive and often impractical at scale. 2/9
I am happy to share our new preprint introducing MADRe - a pipeline for Metagenomic Assembly-Driven Database Reduction, enabling accurate and computationally efficient strain-level metagenomic classification.
🔗https://www.biorxiv.org/content/10.1101/2025.05.12.653324v1
1/9
High-quality metagenome assembly from nanopore reads with nanoMDBG https://www.biorxiv.org/content/10.1101/2025.04.22.649928v1
