Evidence map›Paper›PMID 42231007›Full record

ArticleNature ecology & evolution2026

Alignment-free integration of single-nucleus ATAC-seq across species with sPYce.

Leo Zeitler, Camille Berthelot

Abstract read
In one paragraph

Article in Nature ecology & evolution, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Leo ZeitlerInstitut Pasteur, Université Paris Cité, CNRS UMR 3525, INSERM U1351, Comparative Functional Genomics group, Paris, France. lzeitler@turing.ac.uk.ORCID http://orcid.org/0000-0001-6667-4663
Camille BerthelotInstitut Pasteur, Université Paris Cité, CNRS UMR 3525, INSERM U1351, Comparative Functional Genomics group, Paris, France. camille.berthelot@pasteur.fr.ORCID http://orcid.org/0000-0001-5054-2690

Funding

Centre National de la Recherche Scientifique (National Center for Scientific Research) CNRS UMR 3525EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) 851360
6 · The paper itself

Abstract

Changes in gene regulation largely contribute to differences in cellular identities and phenotypes between species. Single-nucleus assays for transposase-accessible chromatin with sequencing (snATAC-seq) are an efficient strategy to identify putative gene regulatory elements and provide new insight into evolutionary divergence of regulatory programmes. However, no dedicated framework exists to integrate and compare snATAC-seq data across species, while methods designed for single-cell gene expression data have serious limitations. Here we present sPYce, a cross-species snATAC-seq integration method that relies on sequence composition similarities through k-mer histograms of regulatory regions, removing the need for genome alignments to anchor data from different species. sPYce can embed datasets from multiple species into the same mathematical space and permits further downstream analysis steps. We benchmarked sPYce against existing approaches on two publicly available datasets spanning more than 160 myr of evolution, showing that it successfully uncovers conserved cellular programmes while preserving biologically relevant species-specific differences. By comparing cerebellar development in mice and opossums, sPYce identifies regulatory divergence in granule cell differentiation programmes, particularly driven by nuclear factor 1. As an easy-to-use, alignment-free cross-species snATAC-seq integration approach, sPYce opens new perspectives to compare gene regulatory evolution across species.

Indexed as

Chromatin Immunoprecipitation SequencingAnimalsCell NucleusMiceOpossumsSingle-Cell Gene Expression AnalysisSpecies Specificity

Identifiers

PMID42231007
PMCPMC13541617

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.