Figure 1.(A) Classical gel electrophoresis experiments showing mono-, di-, tri-, tetra-, and further multinucleosome bands upon chromatin digestion. (B) The nucleosome repeat length (NRL) is defined as the genomic distance between the centres of two neighbouring nucleosomes.
Figure 2.Nucleosome mapping using MNase-seq versus ATAC-seq. (A) In MNase-seq, nucleosomes in both open and tightly packed genomic regions are accessible to digestion. MNase preferentially cleaves DNA between nucleosomes and digests DNA until it encounters a histone octamer, which provides a footprint of nucleosome-protected DNA regions. (B) Bulk MNase-seq results in averaged maps across millions of cells, effectively capturing all possible nucleosome positioning configurations. (C) Single-cell MNase-seq (scMNase-seq) results in a noisier and sparser signal. The resulting footprints still represent nucleosome-protected regions, but not all nucleosomes are represented. (D) In ATAC-seq, open regions can be accessed by the enzyme Tn5 transposase, which can insert primers in regions free from the binding of nucleosomes and transcription factors (TFs). (E) For open chromatin regions, nucleosome maps can be obtained from ATAC-seq similar to MNase-seq. (F) Closed, tightly packed chromatin regions may be less represented in ATAC-seq nucleosome maps.
Figure 5.Molecular mechanisms affecting nucleosome spacing. (A) Linker histones H1 and nonhistone chromatin proteins which compete with H1s and modulate nucleosome spacing through structural and electrostatic mechanisms. (B) Chromatin remodellers actively reposition nucleosomes following context-dependent rules. (C) Cell state-dependent chromatin boundaries formed by CTCF and other structural proteins, as well as associated recruitment of chromatin remodellers which space nucleosomes. (D) Gene activity associated with remodeller action and RNA polymerases transcribing through the nucleosomes, leading to smaller distances between nucleosomes in regulatory regions and gene bodies. (E) DNA sequence repeats of different types.
Figure 6. Examples of NRL changes in biological systems. (A) Cell differentiation leads to NRL changes between different cell types, e.g. mouse dorsal root ganglia neurons (NRL ∼165 bp) versus cortical astrocytes (NRL ∼183 bp) [175]. Schematic cell shapes are adapted from an image created in BioRender (https://BioRender.com/89trj2t). (B) Paired normal versus tumour breast tissues show NRL shortening in cancer (figure adapted from [36] under the CC BY 4.0 licence (https://creativecommons.org/licenses/by/4.0/)). (C) Nucleosome positioning derived from cfDNA of human volunteers shows NRL increase with age (figure reprinted from [79] under the CC BY 4.0 licence (https://creativecommons.org/licenses/by/4.0/)).
Nucleosome aficionados! Our new review "Nucleosome spacing across cell types, diseases, and ages" is out in NAR: academic.oup.com/nar/article/...
A huge effort to pull together what we’ve learned about nucleosome spacing in many systems. Enjoy!
@milena-bikova.bsky.social @chrsclrksn.bsky.social
05.03.2026 21:33
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Redirecting
Our most recent work on the “function and evolution” of #nuclear-speckles is now online at Cell @cp-cell.bsky.social
doi.org/10.1016/j.ce...
Read the thread👇 for the highlights of our findings.
25.02.2026 16:01
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Have you ever wondered 🤔... Does phenotypic variance respond to environmental perturbation? Does it have a genetic basis? Are mean and variance regulating loci exposed to different selection pressures? These and more questions are explored in our new preprint 🔥
www.biorxiv.org/content/10.6...
23.02.2026 15:37
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From our new paper out now in @currentbiology.bsky.social: www.cell.com/current-biol... w/ @neurofishh.bsky.social @gkafetzis.bsky.social @denilsson.bsky.social
Looking across animals, the vertebrate eye is an obvious outlier. Why is it so different that other highly visual animals?
24.02.2026 10:45
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Interested in the evo-devo of the mammalian cerebellum? This review is a must-read!
Really happy to have contributed to this work, led by @marisepp.bsky.social , together with @ioansarr.bsky.social .
12.02.2026 16:29
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Brain with puzzle overlay to show that our study provides missing pieces of the puzzle of human brain development by delivering the most comprehensive picture of hindbrain development to date. We have strived to go beyond just another multi-omics atlas to gain deep insights by:
1. Meticulously annotating cell clusters
2. Extracting regulatory programs in terms of coordinated gene sets and accessible regulatory elements
3. Using deep learning to identify regulatory syntax
4. Resolving context-specific TF activity
Excited to share our preprint on our new multi-omic atlas of human hindbrain development. Led by postdoc Piyush Joshi, in collaboration with @kaessmannlab.bsky.social and Pfister labs, our atlas represents the first comprehensive view of human hindbrain development. www.biorxiv.org/content/10.6...
10.02.2026 07:04
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HEALTH + LIFE SCIENCE ALLIANCE | Interinstitutional Postdocs
Exciting Postdoc Opportunities – Alliance Interinstitutional Program
**Deadline:** March 31, 2026 (5:00 pm CEST)
🔗 Two shared positions: www.syn-gen.de/alliance-pos...
🔗 Full call & application info:: www.health-life-sciences.de/opportunitie...
09.02.2026 05:34
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Introducing The Structural History of Eukarya (SHE): The first proteome-scale phylogeny constructed entirely from 3D structure.
We computed 300 trillion alignments across 1,542 species to map the tree of life. 🧵👇 (1/5)
07.02.2026 08:50
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The new updates for Charles McAnany’s preprint “Positional Interpretation of Cis-Regulatory Code and Nucleosome Organization with Deep Learning Models” (www.biorxiv.org/content/10.1...) are up!
We introduce PISA, a tool to visualize the cis-regulatory code. See a recap below:
05.02.2026 19:29
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A genome language model for mapping DNA replication origins
Figure 2
Figure 3
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A genome language model for mapping DNA replication origins [new]
predicts specific DNA replication origin sequences by learning rich sequence features beyond known motifs, enabling fast genome-wide mapping across vertebrates.
30.01.2026 16:15
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I've started my own lab 🎉
PhD/postdoc positions available - reach out if curious about cerebellum evo-devo and autism spectrum disorders.
We’re based at Uni Tartu, Institute of Genomics (home to Estonian Biobank), and funded by @simonsfoundation.org @embo.org, and the Estonian Research Council.
29.01.2026 20:33
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Very happy to see this work finally published!
I am truly grateful to all the collaborators who made this work possible, especially @ioansarr.bsky.social, @marisepp.bsky.social, and @kaessmannlab.bsky.social. It’s been a pleasure working with you!
30.01.2026 07:25
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Calling all OrthoFinder users!
We’ve just released GLADE, a tool to infer gene gains, losses, duplications, and ancestral genomes across a phylogeny.
GLADE runs directly on OrthoFinder results.
www.biorxiv.org/content/10.6...
github.com/lauriebelch/...
(1/10)
29.01.2026 12:07
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Remodeling of XIST regulatory landscape during primate evolution
How gene regulation strategies rapidly evolve across short evolutionary timescales is explored.
Our paper on the evolution of XIST regulatory network in primates is now published in Science Advances! Check out the paper www.science.org/doi/10.1126/... or a digest of our findings emmanuelczt.github.io/posts/2026/0... A short 🧵 of our main findings 👇
28.01.2026 15:48
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AlphaGenome is out in @nature.com today along with model weights! 🧬
📄 Paper: www.nature.com/articles/s41...
💻 Weights: github.com/google-deepm...
Getting here wasn’t a straight path. We discussed the story behind the model, paper & API in the following roundtable: youtu.be/V8lhUqKqzUc
28.01.2026 21:02
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It’s out! 🐟 We compared three regeneration superstars—axolotl, zebrafish, and Polypterus—to ask how animals regrow limbs and fins. We find shared core processes, and other programs fine-tuned by evolution in surprising, lineage-specific ways. tinyurl.com/mpftkn7y
22.01.2026 17:38
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17.01.2026 06:34
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We are thrilled to share our new pre-print: “System-wide extraction of cis-regulatory rules from sequence-to-function models in human neural development”. S2F-deeplearning models can accurately encode enhancers, yet decoding these models into human-interpretable rules remains a major challenge.
15.01.2026 11:56
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DNA sequence quantitatively encodes CTCF-binding affinity at genome scale https://www.biorxiv.org/content/10.64898/2026.01.05.696797v1
06.01.2026 07:31
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Cross-species insights into placental evolution and diseases at the single-cell resolution https://www.biorxiv.org/content/10.64898/2025.12.26.696571v1
26.12.2025 18:31
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