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Dongwook Kim

@dongwookkim.bsky.social
202 followers 134 following 15 posts

Developing fast and easy methods for #phylogenetics and #bioinformatics | PhD in Bioinformatics | Postdoc @ Comparative Genomics Lab, UNIL/SIB🇨🇭| Formerly @ Steinegger Lab, SNU🇰🇷 | he/him

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Reposted by Dongwook Kim
Bioinformatics Advances @bioinfoadv.bsky.social · 18/08/2026
🌿 Just published in Bioinformatics Advances: "AmpliPhy improves gene trees by adding homologous sequences without affecting alignments"  Find it here: doi.org/10.1093/bioadv/vbag222 Authors include: @dongwookkim.bsky.social, @dessimoz.bsky.social
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Dongwook Kim @dongwookkim.bsky.social · 13/08/2026
This review began as an assignment for UNIL’s Reviews in Quantitative Biology course, organized by @dessimoz.bsky.social and Natasha Glover. Thanks to Nikolai Romashchenko for his excellent work and to Aurelie Fanchette, one of the course’s students, for her contributions as a coauthor.
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Dongwook Kim @dongwookkim.bsky.social · 13/08/2026
We propose model-free methods as a complementary route for assessing structural phylogenetics. Grounded in biological reasoning and empirical observations, these methods can evaluate results without relying on evolutionary models or assumptions not yet firmly established for structures. 🧵4/5
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Dongwook Kim @dongwookkim.bsky.social · 13/08/2026
Models of structural evolution are actively being developed, often using 20-state structural alphabets such as 3Di. Yet reliable models require trusted benchmarks. Without decades of community standards, identifying reliable structural phylogenetic results remains difficult. 🧵3/5
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Dongwook Kim @dongwookkim.bsky.social · 13/08/2026
Quantitative methods for assessing phylogenetic inference have developed alongside evolutionary models. Protein structures challenge this framework: sequence evolution models cannot be directly applied because many of their underlying assumptions do not translate to structures. 🧵2/5
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Dongwook Kim @dongwookkim.bsky.social · 13/08/2026
AI-driven protein structure prediction lets us revisit the question once impossible to tackle at scale: What can we learn from the evolution of protein structures? Our new preprint reviews how to properly assess the quality of structure-based alignments and trees. 🧵1/5 📄 doi.org/10.32942/X2T10D
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Dongwook Kim @dongwookkim.bsky.social · 10/08/2026
AmpliPhy is now reviewed and published in @bioinfoadv.bsky.social! AmpliPhy improves your gene trees by adding homologs with a single command, based on the observations from a quantitative benchmark design. More details in quoted 🧵 📄 doi.org/10.1093/bioadv/vbag222 💾 github.com/DessimozLab/ampliphy
doi.org
AmpliPhy improves gene trees by adding homologous sequences without affecting alignments
AbstractMotivation. In phylogenomics, gene tree reconstruction depends on multiple sequence alignment and tree inference, and ongoing work continues to imp
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Reposted by Dongwook Kim
Jaebeom Kim @jbeom.bsky.social · 09/04/2026
Metabuli & Metabuli App v1.2 improve novel species classification with higher precision and recall. New light mode is 1.8× faster and requires 50% less storage while keeping precision. New RefSeq, GTDB, HRGM, and HROM databases added. 💾 github.com/steineggerla... 📄 doi.org/10.64898/2026.03.13.711249
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Chan Yeong Kim @chanyeong-kim.bsky.social · 09/02/2026
I am pleased to share that our paper is now published in Cell! www.cell.com/cell/fulltex... I am deeply grateful to all co-authors for making this possible. This work was made possible through the guidance of Dr. Peer Bork. I share this in grateful memory and with deep respect for his mentorship.
cell.com
Planetary microbiome structure and generalist-driven gene flow across disparate habitats
A planetary-scale analysis of over 85,000 metagenomes establishes a framework for exploring the structure and drivers of global microbial habitats, revealing that generalist species bridge ecological ...
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Reposted by Dongwook Kim
Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 30/01/2026
FoldMason is out now in @science.org. It generates accurate multiple structure alignments for thousands of protein structures in seconds. Great work by Cameron L. M. Gilchrist and @milot.bsky.social. 📄 www.science.org/doi/10.1126/... 🌐 search.foldseek.com/foldmason 💾 github.com/steineggerla...
science.org
Multiple protein structure alignment at scale with FoldMason
Protein structure is conserved beyond sequence, making multiple structural alignment (MSTA) essential for analyzing distantly related proteins. Computational prediction methods have vastly extended ou...
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Dongwook Kim @dongwookkim.bsky.social · 28/01/2026
This work has been done by a collaborative effort with Manuel Gil (ZHAW), Kazutaka Katoh (UOsaka), and @dessimoz.bsky.social (UNIL/SIB). Try AmpliPhy now, we appreciate your feedback! 🌐 github.com/dessimozlab/ampliphy
github.com
GitHub - DessimozLab/ampliphy: Improve phylogenetic inference by amplifying multiple sequence alignment with homologous sequences
Improve phylogenetic inference by amplifying multiple sequence alignment with homologous sequences - DessimozLab/ampliphy
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Dongwook Kim @dongwookkim.bsky.social · 28/01/2026
This effect is maintained when MAFFT adds homologs onto an existing MSA without disrupting column structure. Based on these findings, we developed AmpliPhy, a Nextflow pipeline that automates database-driven homolog enrichment for improved gene tree inference at scale. 🧵4/n
We used a feature of the MAFFT software suite that adds sequences while keeping the column structure of the original MSA intact, implemented via the --add and --addfragments options. We refer to MSAs generated with this feature as amplified MSAs. Comparisons of normalized TCS gains between enriched and amplified MSAs showed comparable improvements in phylogenetic inference, regardless of the methodology. This suggests that we can preserve linear scalability for both sequence alignment and phylogenetic inference, while retaining the improvement in inference quality provided by the additional sequences.
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Dongwook Kim @dongwookkim.bsky.social · 28/01/2026
At lower taxonomic levels (e.g., Aminotes), this improvement was associated with more precise root placement. This provides empirical evidence that denser taxon sampling can ameliorate gene tree inference of closely related species by adding information for accurate rooting. 🧵3/n
We hypothesized that the gain in inference quality could be driven by more precise root placement, leveraging the additional information provided by the enriched taxa. To test this, we modified the processing of the enriched tree by switching the order of rooting and pruning. In our original workflow, we processed trees inferred from enriched MSAs by rooting first and then pruning leaves that were not present in the original MSA (post-pruning). By reversing the order, i.e. pruning first and rooting afterwards, we prevented the additional taxa from contributing to the rooting step (pre-pruning). We then compared the congruence gain of pre-pruned and post-pruned phylogenetic trees. To quantify the effect of pre-pruning, we computed the loss of congruence by pre-pruning the tree. The effect of pre-pruning was observed as a notable decrease in inference quality for Amniota HOGs.
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Dongwook Kim @dongwookkim.bsky.social · 28/01/2026
We devised a benchmark method to quantify the impact of homolog enrichment on phylogenetic inference, decomposing the effects on MSA quality, tree inference quality, and rooting. We show homolog enrichment improves tree inference, while effects on alignments remain marginal. 🧵2/n
For each orthologous gene family, we constructed three MSAs: original, computed by applying sequence aligners directly to the input sequences; enriched, computed by aligning the combined set of orthologs and homologs identified by database search; and impoverished, obtained by removing the added homologs from the enriched MSA. We then used TCS to quantify congruence of the resulting trees against the known taxonomy. The normalized difference in congruence between the original and enriched trees captures the joint impact of sequence addition on alignment and tree inference. The normalized difference between the original and impoverished trees reflects the effect on alignment quality alone. The effect on tree inference can then be estimated by subtraction. We observed a positive impact of homolog enrichment on phylogenetic tree inference step, regardless of the sequence aligner used to build the alignments. Notably, the impact of sequence addition on alignments was marginal.
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Dongwook Kim @dongwookkim.bsky.social · 28/01/2026
Can ever-increasing sequence databases improve phylogenetic reconstruction of a gene family? Our new preprint introduces AmpliPhy, a pipeline that automates homolog enrichment to improve gene tree inference, built on a robust phylogenomic benchmark scheme. 🧵1/n 📃 doi.org/10.64898/2026.01.26.701724
doi.org
AmpliPhy improves gene trees by adding homologs without affecting alignments
In phylogenomics, gene tree reconstruction depends on multiple sequence alignment (MSA) and tree inference, and ongoing work continues to improve inference quality. Denser taxon sampling has been associated with improved gene tree inference, suggesting that adding homologs could be a practical route to higher accuracy as sequence databases continue to expand. However, adding sequences can influence multiple steps of typical inference pipelines, and little is known on its specific effect on the multiple sequence alignment, tree reconstruction, and rooting steps. We performed a large-scale empirical benchmark to quantify how homolog enrichment affects alignment and phylogenetic inference. Using an enrichment-impoverishment design and a measure of tree accuracy based on taxonomic congruence, we found that enrichment consistently improves tree inference quality, while effects on alignment quality are marginal. We show that this improvement is associated with accurate root placement on enriched trees when sensitive homolog search is accompanied. Notably, much of the benefit can be retained with relatively compact alignments produced by sequence addition. Building on these observations, we provide a tool, AmpliPhy, which efficiently improves phylogenetic reconstruction of protein families through homolog enrichment. The AmpliPhy open-source pipeline software is available at https://github.com/DessimozLab/ampliphy. ### Competing Interest Statement The authors have declared no competing interest. Swiss National Science Foundation, https://ror.org/00yjd3n13, 216623, 10005715
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Milot Mirdita @milot.bsky.social · 20/01/2026
My time in @martinsteinegger.bsky.social's group is ending, but I’m staying in Korea to build a lab at Sungkyunkwan University School of Medicine. If you or someone you know is interested in molecular machine learning and open-source bioinformatics, please reach out. I am hiring! mirdita.org
mirdita.org
Mirdita Lab - Laboratory for Computational Biology & Molecular Machine Learning
Mirdita Lab builds scalable bioinformatics methods.
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George Bouras @gbouras13.bsky.social · 08/08/2025
Stoked to finally have a preprint out for Phold, our tool that uses protein structural information to enhance phage genome annotation #phagesky 1/n www.biorxiv.org/content/10.1...
biorxiv.org
Protein Structure Informed Bacteriophage Genome Annotation with Phold
Bacteriophage (phage) genome annotation is essential for understanding their functional potential and suitability for use as therapeutic agents. Here we introduce Phold, an annotation framework utilis...
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Chan Yeong Kim @chanyeong-kim.bsky.social · 21/07/2025
Our new preprint is out! www.biorxiv.org/content/10.1... In this study, we present the largest systematic analysis of microbiome structure and function, integrating 85K uniformly processed metagenomes from diverse habitats worldwide. @podlesny.bsky.social @jonas-bio.bsky.social @borklab.bsky.social
biorxiv.org
Planetary microbiome structure and generalist-driven gene flow across disparate habitats
Microbes are ubiquitous on Earth, forming microbiomes that sustain macroscopic life and biogeochemical cycles. Microbial dispersion, driven by natural processes and human activities, interconnects mic...
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Laurie Belcher @lauriebelch.bsky.social · 16/07/2025
OrthoFinder just dropped a major update It’s faster, more accurate, and ready for thousands of genomes Let’s break it down (1/10) github.com/OrthoFinder/... www.biorxiv.org/content/10.1...
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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 07/07/2025
Folddisco finds similar (dis)continuous 3D motifs in large protein structure databases. Its efficient index enables fast uncharacterized active site annotation, protein conformational state analysis and PPI interface comparison. 1/9🧶🧬 📄 www.biorxiv.org/content/10.1... 🌐 search.foldseek.com/folddisco
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georghochberg.bsky.social @georghochberg.bsky.social · 11/06/2025
New paper from the lab from Sriram Garg in my group. We introduce a general substitution matrix for structural phylogenetics. I think this is a big deal, so read on below if you think deep history is important. academic.oup.com/mbe/advance-...
academic.oup.com
A general substitution matrix for structural phylogenetics.
Abstract. Sequence-based maximum likelihood (ML) phylogenetics is a widely used method for inferring evolutionary relationships, which has illuminated the
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Dongwook Kim @dongwookkim.bsky.social · 03/06/2025
This work was done by talented @sukhwanpark.bsky.social and me, supervised by amazing @martinsteinegger.bsky.social ! Try Unicore now 👉 conda install -c bioconda unicore Code and tutorial: 🌐 github.com/steineggerlab/unicore Manuscript: 🌐 doi.org/10.1093/gbe/evaf109
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Dongwook Kim @dongwookkim.bsky.social · 03/06/2025
Unicore is fast, accurate, and universal. Unicore reconstructed consistent phylogeny of bacterial/fungal species, while maintaining linear time scale over the input size. Besides, Unicore works with any given taxa, presenting scalable and universal method for structure-based phylogeny. 🧵3/n
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Dongwook Kim @dongwookkim.bsky.social · 03/06/2025
With Unicore, we identified 13 structural core genes from 166 species across the Tree of Life, where 8 of them could only be defined using structures. Projected on the Tree of Life reconstructed with Unicore, you can see the universally conserved structure of one of the structural core genes. 🧵2/n
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Dongwook Kim @dongwookkim.bsky.social · 03/06/2025
Unicore is now published on GBE 🚀 Unicore rapidly identifies structural single-copy core genes from input species proteomes for phylogenetic analysis. Powered by Foldseek and ProstT5, Unicore enables linear-scale structure-based phylogeny of any given set of taxa. 🧵1/n 📃 doi.org/10.1093/gbe/evaf109
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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 27/04/2025
AFESM: a metagenomic guide through the protein structure universe! We clustered 821M structures (AFDB&ESMatlas) into 5.12M groups; revealing biome-specific groups, only 1 new fold even after AlphaFold2 re-prediction & many novel domain combos. 🧵 🌐 afesm.foldseek.com 📄 www.biorxiv.org/content/10.1...
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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 25/04/2025
Visit our posters at #RECOMB2025 for: Structural: MSAs, Virus DB, Core Genes, Motif Discovery, Multimer Clustering & Search, pLM Foldseek, Environmental analysis Metagenomics: Classification & Metabuli App GPU-based & RNA search, Proteome clustering, Novel Ribozyme discovery & get Marv stickers!
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Matthew Hahn @3rdreviewer.bsky.social · 10/04/2025
Not really my announcement to make--I am but a lesser co-author--but IQ-TREE 3 has just been released! (Most credit to Minh Bui and @roblanfear.bsky.social and their labs) ecoevorxiv.org/repository/v...
ecoevorxiv.org
IQ-TREE 3: Phylogenomic Inference Software using Complex Evolutionary Models
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EMBL-EBI @ebi.embl.org · 03/03/2025
🚀 #AlphaFold Database update AlphaFold DB now integrates The Encyclopedia of Domains (TED) – a resource designed to systematically identify & classify structural domains within AlphaFold-predicted protein structures. www.ebi.ac.uk/about/news/u... @pdbeurope.bsky.social
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Christophe Dessimoz @dessimoz.bsky.social · 26/02/2025
The PAN-GO paper is a remarkable milestone. It not only provides the most comprehensive picture of human gene function to date, but also carefully maps this knowledge across the tree of life! Congratulations @marcfeuermann.bsky.social, Pascale Gaudet & collaborators! www.sib.swiss/news/sib-hel...
sib.swiss
SIB helps create most complete, accurate resource for human gene functions
For the first time, biodata from humans have been integrated with that of other organisms to provide the most comprehensive picture of human gene function to date. The new ‘PAN-GO’ resource used e...
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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 22/02/2025
In our latest review, we explore 12 deep-learning tools for metagenomic analysis, covering their strengths, limitations, and key applications. We hope it serves as both a resource and inspiration for new ways to analyze metagenomic data. Great work by Eli Levy Karin! 📄 doi.org/10.1093/nsr/...
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Sina Majidian @sinamajidian.bsky.social · 03/01/2025
FastOMA is out now in Nature Methods 🎉: nature.com/articles/s41592-024-02552-8 A new orthology inference algorithm that scales linearly and is highly accurate. FastOMA can process all >2000 eukaryotic UniProt ref proteomes <24 hours 🚀. Try it out github.com/DessimozLab/fastoma @dessimoz.bsky.social
FastOMA retains OMA’s high precision accuracy and even improves upon it in terms of recall, positioning it on the Pareto frontier of orthology inference methods. 
FastOMA is not only fast but also accurate. a, QfO benchmar, agreement with SwissTree reference phylogeny covering manually curated gene trees. The error bars indicate 95% confidence intervals comparing FastOMA with EnsemblCompara, Domainoid, OrthoMCL, Ortholnspector, sonicparanoid, PANTHER, OrthoFinder, Hieranoid26 and the OMA family including OMA pairs, OMA groups and OMA GETHOGs (graph-based efficient technique for HOGs).

c) A computation time comparison of FastOMA and state-of-the-art alternatives.
https://www.nature.com/articles/s41592-024-02552-8
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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 23/12/2024
Unicore identifies single-copy protein structures across genomes using Foldseek, bypassing slow structure predictions by utilizing 3Di predictions from ProstT5, enabling rapid phylogenetic inference at the tree-of-life scale. 1/n 📄 www.biorxiv.org/content/10.1... 💾 github.com/steineggerla...
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bioRxiv Bioinfo @biorxiv-bioinfo.bsky.social · 23/12/2024
Unicore enables scalable and accurate phylogenetic reconstruction with structural core genes www.biorxiv.org/content/10.1101/202…
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Bluesky @bsky.app · 03/12/2024
Scientists, academics, researchers: We’re excited to share that @altmetric.com is now tracking mentions of your research on Bluesky! 🧪
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Adam Schwarz @adamjschwarz.bsky.social · 03/12/2024
South Korean citizens helped lawmakers scale the National Assembly walls so they could bypass military barricades and vote against martial law.
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Richard Sever @richardsever.bsky.social · 10/11/2024
Reminder for newcomers that bioRxiv has Bluesky accounts in every subject category - great way to keep up (please re-skeet) connect.biorxiv.org/news/2023/09...
connect.biorxiv.org
bioRxiv expands on Mastodon and Bluesky
bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution
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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 28/11/2024
Interested in bioinformatics method development for proteins, structures or metagenomic analysis? Please check out my lab’s starter pack! 🔗 go.bsky.app/VJhXcSs
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Milot Mirdita @milot.bsky.social · 27/11/2024
MMseqs2 Release 16 Highlights: GPU-accelerated search📄, ORF or new 6-frame translated search modes, contig taxonomy always keeps the longest ORF, bug fixes (reduced memory and higher sensitivity) and relicensed as MIT 📄 biorxiv.org/content/10.1... 💾 mmseqs.com and 🐍Bioconda 🖥️🧬🧶
Promotional logo for MMseqs2 16 with the MMseqs2 Rocket mascot as a smart phone like App logo
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Roli Roberts @roliroberts.bsky.social · 25/11/2024
What did the Last Eukaryotic Common Ancestor (#LECA) look like? Consensus View in #PLOSBiology; massive authorship including @AncestralState, @lauraeme.bsky.social, John Archbald, @andrewjroger.bsky.social, @dackslabecb.bsky.social, Jeremy Wideman. plos.io/4g0alq4
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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 23/11/2024
Our Big Fantastic Virus Database (BFVD) is now published NAR! It contains protein structure predictions of major viral clades, enhanced by petabase-scale homology search and it's explorable on the web. 🌐 bfvd.foldseek.com 💾 bfvd.steineggerlab.workers.dev 📄 academic.oup.com/nar/advance-...
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