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Reposted by Josipa Lipovac

Roland Faure @rfaure.bsky.social · 5d

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/...

Alice: fast and haplotype-aware assembly of high-fidelity reads based on MSR sketching

We introduce Mapping-friendly Sequence Reduction (MSR) sketches, a sketching method for high-fidelity (HiFi) long reads, and Alice, an assembler that operates directly on these sketches. MSR produces ...

www.biorxiv.org

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Reposted by Josipa Lipovac

Adam Phillippy @aphillippy.bsky.social · 16d

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]

A complete diploid human genome benchmark for personalized genomics

Human genome resequencing typically involves mapping reads to a reference genome to call variants; however, this approach suffers from both technical and reference biases, leaving many duplicated and ...

www.biorxiv.org

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Reposted by Josipa Lipovac

Sina Majidian @sinamajidian.bsky.social · 17d

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)

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

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Reposted by Josipa Lipovac

Zamin Iqbal @zaminiqbal.bsky.social · 28d

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...

Efficient sequence alignment against millions of prokaryotic genomes with LexicMap - Nature Biotechnology

LexicMap uses a fixed set of probes to efficiently query gene sequences for fast and low-memory alignment.

www.nature.com

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Reposted by Josipa Lipovac

Jim Shaw @jimshaw.bsky.social · Sep 7

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

bioRxiv Bioinfo @biorxiv-bioinfo.bsky.social · Sep 7

High-resolution metagenome assembly for modern long reads with myloasm https://www.biorxiv.org/content/10.1101/2025.09.05.674543v1

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Reposted by Josipa Lipovac

Rayan Chikhi @rayanchikhi.bsky.social · Sep 3

🌎👩‍🔬 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...

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Reposted by Josipa Lipovac

Tami Lieberman @contaminatedsci.bsky.social · Aug 15

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.

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Josipa Lipovac @jlipovac.bsky.social · Jul 20

Proud to share our work on the first complete genome of an Indian individual - now on bioRxiv! 😄

Mile Sikic @msikic.bsky.social · Jul 19

Happy to present the first complete genome of an Indian individual biorxiv.org/content/10.1.... Joint effort by @astar-gis.bsky.social, FER, and partners, with support from the Singapore NPM programme!
Joint effort with JJ Liu Lab!
Prasad Sarashetti and
@jlipovac.bsky.social
as co-first authors.

A Complete Telomere-to-Telomere Diploid Reference Genome for Indian Population

Human reference genomes have been instrumental in advancing genomic and biomedical research, but South and Southeast Asian populations are underrepresented, despite accounting for a large proportion o...

biorxiv.org

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Reposted by Josipa Lipovac

bioRxiv Bioinfo @biorxiv-bioinfo.bsky.social · Jul 11

Campolina: A Deep Neural Framework for Accurate Segmentation of Nanopore Signals https://www.biorxiv.org/content/10.1101/2025.07.08.663658v1

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Reposted by Josipa Lipovac

Pierre Peterlongo @pierrepeterlongo.bsky.social · May 27

📜 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

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Josipa Lipovac @jlipovac.bsky.social · May 16

Thanks Roland! It’s good to mention here that Hairsplitter is also part of it 😄

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Reposted by Josipa Lipovac

Zamin Iqbal @zaminiqbal.bsky.social · May 16

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...

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

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Josipa Lipovac @jlipovac.bsky.social · May 16

Thanks! 😊

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Josipa Lipovac @jlipovac.bsky.social · May 16

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
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GitHub - lbcb-sci/MADRe: Strain-level metagenomic classification with Metagenome Assembly driven Database Reduction approach

Strain-level metagenomic classification with Metagenome Assembly driven Database Reduction approach - lbcb-sci/MADRe

github.com

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Josipa Lipovac @jlipovac.bsky.social · May 16

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

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Josipa Lipovac @jlipovac.bsky.social · May 16

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

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Josipa Lipovac @jlipovac.bsky.social · May 16

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

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Josipa Lipovac @jlipovac.bsky.social · May 16

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

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Josipa Lipovac @jlipovac.bsky.social · May 16

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

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Josipa Lipovac @jlipovac.bsky.social · May 16

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

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Josipa Lipovac @jlipovac.bsky.social · May 16

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

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Josipa Lipovac @jlipovac.bsky.social · May 16

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

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bioRxiv Bioinfo @biorxiv-bioinfo.bsky.social · Apr 25

High-quality metagenome assembly from nanopore reads with nanoMDBG https://www.biorxiv.org/content/10.1101/2025.04.22.649928v1

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Reposted by Josipa Lipovac

Karel Břinda @brinda.eu · Apr 11

A decade ago, we had thousands of bacterial genomes. Now, we have millions. How to scale computational methods?

Our paper in @naturemethods.bsky.social answers this: use evolutionary history to guide compression and search.

rdcu.be/eg4OA

w/ @baym.lol, @zaminiqbal.bsky.social et al. 🧵1/

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Reposted by Josipa Lipovac

Zamin Iqbal @zaminiqbal.bsky.social · Feb 28

"benchmarking multiple assembly and variant-calling pipelines....read-based methods consistently achieved high accuracy. Assembly-based approaches performed well in some cases but were highly dependent on assembly quality"
www.biorxiv.org/content/10.1...
from Ryan Wick&co

Are reads required? High-precision variant calling from bacterial genome assemblies

Accurate nucleotide variant calling is essential in microbial genomics, particularly for outbreak tracking and phylogenetics. This study evaluates variant calls derived from genome assemblies compared...

www.biorxiv.org

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