OSCR

DUET-seq: An Open-Source Droplet Platform for High-Fidelity Joint Chromatin and Transcriptome Profiling Reveals Temporal Regulatory Decoupling in Single Cells.

Code ↔ Paper

34 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 34 matches
  1. [1] § Methods › Single‐cell Data of Mouse Testis Processing and Quality Control ↔ 04_testis/base/01_rna_processing.R, lines 92–133 · score 0.98 · blacklist ratio, ribosomal content, nFeature_ATAC, nCount_RNA, nucleosome signal, nCount_ATAC
  2. [2] § Methods › Mouse Brain Data Analysis ↔ 04_testis/base/01_rna_processing.R, lines 92–133 · score 0.94 · nucleosome signal, percent.mt, nCount_ATAC, nFeature_RNA, mitochondrial content, TSS enrichment
  3. [3] § Methods › Single‐cell Data of Mouse Testis Processing and Quality Control ↔ 03_brain/01_Platform_Comparison_Plots.R, lines 41–82 · score 0.90 · nFeature_ATAC, nCount_RNA, nCount_ATAC, nFeature_RNA, TSS enrichment, quality control
  4. [4] § Methods › Mouse Brain Data Analysis ↔ 03_brain/01_Platform_Comparison_Plots.R, lines 41–82 · score 0.86 · percent.mt, nCount_ATAC, nFeature_RNA, TSS enrichment, QC metrics, FRiP
  5. [5] § Methods › Single‐cell Data of Mouse Testis Processing and Quality Control ↔ 04_testis/base/01_rna_processing.R, lines 135–215 · score 0.85 · FindClusters, FindNeighbors, SCTransform, Dimensionality reduction, PCA, regressing
  6. [6] § Methods › Identification of Robust Peak‐Gene Links ↔ 04_testis/base/03_link_peaks.R, lines 45–129 · score 0.82 · pct.1, pct.2, LinkPeaks, Rik, Gm, kb
  7. [7] § Results › Benchmarking DUET‐seq Against Existing Multi‐omic Platforms ↔ 02_cellline/technical_qc_comparisons.ipynb, lines 902–987 · score 0.80 · SNARE seq2, sci CAR, SUM seq, SHARE seq, S4, HEK293T
  8. [8] § Methods › Identification of Robust Peak‐Gene Links ↔ 04_testis/base/03_link_peaks.R, lines 45–129 · score 0.76 · min.pct, logfc.threshold, FindAllMarkers, expressed genes, linkage, SCT
  9. [9] § Results › Joint Multi‐omic Profiling of Mouse Testis Across Six Postnatal Stages ↔ 04_testis/base/05_coverage_plot.R, lines 41–114 · score 0.75 · Leydig cells, Sertoli cells, elongating spermatids, Sycp2, Uchl1, Clu
  10. [10] § Results › Chromatin Inertia and Regulatory Logic Governing the Spermatid‐to‐sperm Transition ↔ 04_testis/re/04_construct_stage_specific_grn.R, lines 217–263 · score 0.74 · ES_Late, ES_Early, RS_Early, elongating spermatids, global, round
  11. [11] § Results › Joint Multi‐omic Profiling of Mouse Testis Across Six Postnatal Stages ↔ 04_testis/base/04_link_peaks_visualization.R, lines 136–202 · score 0.69 · Leydig cells, Sertoli cells, elongating spermatids, chromatin accessibility, macrophages, SPC
  12. [12] § Results › Chromatin Inertia and Regulatory Logic Governing the Spermatid‐to‐sperm Transition ↔ 04_testis/re/04_construct_stage_specific_grn.R, lines 217–263 · score 0.69 · ES_Early, RS_Early, round spermatids, elongating spermatids, TFs
  13. [13] § Results › Design and Workflow of DUET‐seq ↔ 02_cellline/technical_qc_comparisons.ipynb, lines 902–987 · score 0.68 · asked, Mg2, S3, arms, mis, HEK293T
  14. [14] § Methods › Trajectory Inference and Dynamic Regulatory Analysis ↔ 04_testis/spg/03_rna_velocity_analysis_scvelo.py, lines 25–46 · score 0.67 · AnnData, RNA velocities, unspliced, scVelo, embedding, matrices
  15. [15] § Results › DUET‐seq Resolves Cellular Heterogeneity in Adult Mouse Brain and Enables Peak‐to‐gene Linking ↔ 03_brain/02_Peak_Gene_Regulation.R, lines 208–260 · score 0.66 · Slc17a7, gene activity, Gad2, Pdgfra, Plp1, chromatin accessibility
  16. [16] § Results › Decoding Transcriptional and Epigenetic Programs During Spermatogonial Lineage Progression ↔ 04_testis/spg/04_celloracle_grn_perturbation_analysis.py, lines 131–178 · score 0.65 · core TFs, regulating genes, FigR, module, perturbation, SSCs
  17. [17] § Results › Decoding Transcriptional and Epigenetic Programs During Spermatogonial Lineage Progression ↔ 03_brain/04_FigR_Core_Pipeline.R, lines 89–145 · score 0.65 · TF DORC, DORC genes, figR, regulatory networks, peak gene, score
  18. [18] § Methods › Trajectory Inference and Dynamic Regulatory Analysis ↔ 03_brain/02_Peak_Gene_Regulation.R, lines 208–260 · score 0.64 · LinkPeaks, high confidence, peak gene, intersected, regulated, clustering
  19. [19] § Methods › Identification of Robust Peak‐Gene Links ↔ 04_testis/base/04_link_peaks_visualization.R, lines 53–117 · score 0.64 · min.pct, logfc.threshold, FindAllMarkers, SCT, peak, gene
  20. [20] § Results › Joint Multi‐omic Profiling of Mouse Testis Across Six Postnatal Stages ↔ 04_testis/base/04_link_peaks_visualization.R, lines 136–202 · score 0.64 · peritubular myoid cells, Sertoli cells, linked peaks, chromatin accessibility, Sox9, gene expression
  21. [21] § Results › Joint Multi‐omic Profiling of Mouse Testis Across Six Postnatal Stages ↔ 04_testis/base/06_correlation_plot.R, lines 1–85 · score 0.63 · peritubular myoid cells, Sertoli cells, linked peaks, chromatin accessibility, inferred, gene expression
  22. [22] § Results › Decoding Transcriptional and Epigenetic Programs During Spermatogonial Lineage Progression ↔ 04_testis/spg/02_spg_visualization.R, lines 134–215 · score 0.62 · Pseudotime trajectory, Gfra1, Ret, Zbtb16, Foxo1, Kit
  23. [23] § Methods › Single‐cell Data of Mouse Testis Processing and Quality Control ↔ 04_testis/base/02_atac_processing.R, lines 94–150 · score 0.62 · FindClusters, FindNeighbors, graph, Dimensionality, clustering, testis
  24. [24] § Results › Decoding Transcriptional and Epigenetic Programs During Spermatogonial Lineage Progression ↔ 04_testis/re/04_construct_stage_specific_grn.R, lines 1–41 · score 0.60 · gene regulatory networks, regulation score, FigR, GRNs, TF, spermatogonial
  25. [25] § Results › Joint Multi‐omic Profiling of Mouse Testis Across Six Postnatal Stages ↔ 04_testis/base/04_link_peaks_visualization.R, lines 53–117 · score 0.59 · Positively correlated, elongating spermatids, stromal, log2, max, Sertoli
  26. [26] § Results › Chromatin Inertia and Regulatory Logic Governing the Spermatid‐to‐sperm Transition ↔ 04_testis/base/03_link_peaks.R, lines 1–43 · score 0.58 · peak gene linkage, cis regulatory, regulatory elements, gene expression, clustering, ATAC
  27. [27] § Results › Decoding Transcriptional and Epigenetic Programs During Spermatogonial Lineage Progression ↔ 04_testis/base/03_link_peaks.R, lines 1–43 · score 0.57 · peak gene linkage, cis regulatory, regulatory elements, gene expression, clustering, ATAC
  28. [28] § Methods › Species Mixing Experiment ↔ 02_cellline/human_mouse_mix_split.py, lines 103–133 · score 0.57 · species mixing, doublet rate, barnyard, RNA
  29. [29] § Results › Benchmarking DUET‐seq Against Existing Multi‐omic Platforms ↔ 02_cellline/cellline_trackplot.R, lines 87–149 · score 0.57 · 65600000–66000000, 68900000–69000000, chr11, tracks, DUET seq, ISSAAC
  30. [30] § Methods › Identification of DORCs and Regulatory Network Construction ↔ 04_testis/re/03_figr_dorc_identification.R, lines 1–39 · score 0.57 · Regulatory Chromatin, FigR, Domains, DORCs, workflow, gene expression
  31. [31] § Results › Decoding Transcriptional and Epigenetic Programs During Spermatogonial Lineage Progression ↔ 04_testis/spg/02_spg_visualization.R, lines 134–215 · score 0.52 · annotation bars, pseudotime trajectory, Ridge, density, ratio, SPG
  32. [32] § Results › Design and Workflow of DUET‐seq ↔ 02_cellline/human_mouse_mix_split.py, lines 103–133 · score 0.52 · species mixing, doublet rate, human, mouse, RNA, seq
  33. [33] § Results › Decoding Transcriptional and Epigenetic Programs During Spermatogonial Lineage Progression ↔ 04_testis/re/04_construct_stage_specific_grn.R, lines 43–103 · score 0.51 · core TFs, FigR, refine, Crem, network, spermatogenic
  34. [34] § Results › DUET‐seq Resolves Cellular Heterogeneity in Adult Mouse Brain and Enables Peak‐to‐gene Linking ↔ 04_testis/re/03_figr_dorc_identification.R, lines 1–39 · score 0.51 · Regulatory Chromatin, FigR, Domain, DORC, DUET seq, gene expression

Paper

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The authors' code

R · 129 lines · 4.5 KB · no license · 4 matches

  1. # ==============================================================================
  2. # DUET-SEQ Analysis Pipeline: Peak-Gene Linkage Analysis
  3. # ==============================================================================
  4. # Description:
  5. # This script identifies cis-regulatory elements by linking ATAC-seq peaks
  6. # to gene expression using the Signac LinkPeaks function. It focuses on
  7. # cell-type-specific marker genes identified in previous steps.
  8. #
  9. # Steps:
  10. # 1. Load the fully processed and annotated Seurat object.
  11. # 2. Identify cell-type specific marker genes.
  12. # 3. Filter marker genes (remove noise/low specificity).
  13. # 4. Calculate GC content and other region stats for peaks.
  14. # 5. Link peaks to gene expression.
  15. # ==============================================================================
  17. # 1. Library Loading
  18. # ==============================================================================
  19. suppressPackageStartupMessages({
  20. library(Seurat)
  21. library(Signac)
  22. library(qs)
  23. library(glue)
  24. library(dplyr)
  25. library(BSgenome.Mmusculus.UCSC.mm10)
  26. library(EnsDb.Mmusculus.v79)
  27. library(GenomicRanges)
  28. })
  30. # 2. Directory Setup & Data Loading
  31. # ==============================================================================
  32. # Set working directory relative to project root
  33. # setwd("./src/rna/seurat_signac_pipeline2")
  35. data_dir <- "./data_overall"
  36. outs_dir <- "./outs_overall"
  38. if (!dir.exists(data_dir)) dir.create(data_dir, recursive = TRUE)
  39. if (!dir.exists(outs_dir)) dir.create(outs_dir, recursive = TRUE)
  41. message("Loading annotated Seurat object...")
  42. # Load the object from the previous step (e.g., after leiden clustering and annotation)
  43. seu_obj <- qread(file = glue("{data_dir}/seu_processed_annotated.qs"))
  45. # 3. Identify Cell-Type Specific Marker Genes
  46. # ==============================================================================
  47. message("Identifying cell-type specific marker genes...")
  49. DefaultAssay(seu_obj) <- "SCT"
  50. Idents(seu_obj) <- "celltype"
  52. # Prepare SCT assay for differential expression (optional but recommended)
  53. seu_obj <- PrepSCTFindMarkers(seu_obj)
  55. # Find markers for all clusters
  56. # Criteria: positive markers only, min 10% cells expressing, logFC threshold 0.25
  57. de_genes <- FindAllMarkers(
  58. seu_obj,
  59. only.pos = TRUE,
  60. min.pct = 0.1,
  61. logfc.threshold = 0.25,
  62. verbose = FALSE
  63. )
  65. # 4. Filter Marker Genes
  66. # ==============================================================================
  67. message("Filtering marker genes...")
  69. # Initial filter: Adjusted P-value < 0.05 and Log2FC > 0.1
  70. sig_markers <- de_genes %>%
  71. filter(p_val_adj < 0.05, avg_log2FC > 0.1)
  73. # Remove noise genes (Ensembl IDs, Rik genes, Gm genes, etc.)
  74. noise_pattern <- "^ENSMUS|^[0-9]|^Gm[0-9]|^Rik$|Rik[0-9]|Rik$|^LOC|^BC[0-9]"
  75. clean_markers <- sig_markers[!grepl(noise_pattern, sig_markers$gene), ]
  77. # Specificity filter: Difference in percentage expression (pct.1 - pct.2) > 0.1
  78. clean_markers$specificity <- clean_markers$pct.1 - clean_markers$pct.2
  79. final_markers <- clean_markers %>% filter(specificity > 0.1)
  81. # Extract unique gene list for linking
  82. genes_to_link <- unique(final_markers$gene)
  84. message(glue("Identified {length(genes_to_link)} specific marker genes for linkage analysis."))
  86. # Save marker results
  87. qsave(final_markers, file = glue("{data_dir}/final_celltype_markers.qs"))
  89. # 5. Link Peaks to Genes
  90. # ==============================================================================
  91. message("Calculating peak-gene links (this may take time)...")
  93. DefaultAssay(seu_obj) <- "ATAC"
  95. # 5.1 Calculate Region Statistics (GC content)
  96. # Required for background peak selection in LinkPeaks
  97. seu_obj <- RegionStats(
  98. seu_obj,
  99. assay = "ATAC",
  100. genome = BSgenome.Mmusculus.UCSC.mm10
  101. )
  103. # 5.2 Run LinkPeaks
  104. # Links peaks within 500kb of the TSS to gene expression
  105. seu_obj <- LinkPeaks(
  106. object = seu_obj,
  107. peak.assay = "ATAC",
  108. expression.assay = "SCT",
  109. genes.use = genes_to_link,
  110. distance = 500000, # 500 kb window
  111. min.cells = 10, # Minimum cells expressing gene/peak
  112. n_sample = 200, # Downsample for p-value calculation (speeds up process)
  113. pvalue_cutoff = 0.05,
  114. score_cutoff = 0.05,
  115. verbose = TRUE
  116. )
  118. # 6. Save Results
  119. # ==============================================================================
  120. message("Saving results...")
  122. # Save the updated Seurat object with links
  123. qsave(seu_obj, file = glue("{data_dir}/seu_with_links.qs"))
  125. # Extract and save the Links GRanges object separately for easier access
  126. links_granges <- Links(seu_obj[["ATAC"]])
  127. qsave(links_granges, file = glue("{data_dir}/peak_gene_links.qs"))
  129. message("Linkage analysis completed successfully.")

03_link_peaks.R at commit bd130dc, no license · at the source

Overview

Authors: Dong Cheng1,2, Zijun Meng3, Lan Wei4, Pingjing Yang1, Fanfan Zhang1, Zhiyi Luo1, Ting Lai1, Chunli Li5, Mengyao Zhao1, Mengqin Xu1, Jiaqi Wang1, Linjun Li6, Huarong Chen7, Jin Li8, Ailong Huang1,9, Youquan Bu2,9, Liuyang Zhao1,10,11
ORCID iDs: Huarong Chen, Jin Li, Ailong Huang, Liuyang Zhao
  1. Department of Infectious Diseases Key Laboratory of Molecular Biology For Infectious Diseases (Ministry of Education) Institute For Viral Hepatitis The Second Affiliated Hospital Chongqing Medical University Chongqing China
  2. Department of Thoracic Surgery Department of Biochemistry and Molecular Biology College of Basic Medical Sciences The First Affiliated Hospital of Chongqing Medical University Chongqing Medical University Chongqing China
  3. Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders National Clinical Research Center for Child Health and Disorders China International Science and Technology Cooperation Base of Child Development and Critical Disorders Ministry of Education Key Laboratory of Child Development and Disorders Children's Hospital of Chongqing Medical University Chongqing China
  4. Chongqing Blood Center Chongqing China
  5. Department of Hepatobiliary Surgery The Second Affiliated Hospital of Chongqing Medical University Chongqing China
  6. Department of Cardiothoracic Surgery The First Affiliated Hospital of Chongqing Medical University Chongqing China
  7. Department of Anaesthesia and Intensive Care and Peter Hung Pain Research Institute The Chinese University of Hong Kong Hong Kong China
  8. Department of Laboratory Medicine The Affiliated Dazu's Hospital of Chongqing Medical University Chongqing China
  9. Molecular Medicine and Cancer Research Center Chongqing Medical University Chongqing China
  10. Reproductive Medicine Center the First Affiliated Hospital of Chongqing Medical University Chongqing China
  11. Key Laboratory of Laboratory Medical Diagnostics Chinese Ministry of Education Chongqing Medical University Chongqing China
Institutions: Dalian Medical University (China); Second Affiliated Hospital of Chongqing Medical University (China); The Affiliated Yongchuan Hospital of Chongqing Medical University (China); Chongqing Medical University (China); Children's Hospital of Chongqing Medical University (China); Chinese University of Hong Kong (Hong Kong SAR China); Chongqing Dazu District People's Hospital (China)
Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany), article e77986
Dates: received 15 April 2026; accepted 18 September 2026; published online 27 September 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/advs.77986 · PMID 42801560 · PMCID PMC13616282 · OpenAlex W7214524852
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: biology, chromatin, computational biology, gene expression, rna, transcriptome
Topic: Genomics and Chromatin Dynamics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Prevention and Control of Emerging and Major Infectious Diseases‐National Science and Technology Major Project (2025ZD01905600); National Natural Science Foundation of China (National Science Foundation of China) (82372356, 82372630); Chongqing Talents Program‐Youth Top‐notch Talents (CQYC20220512141, 20880); CQMU Program for Youth Innovation in Future Medicine (W0161, W0143)
Citations: not cited yet (Europe PMC); 94 references in the paper

Abstract

Joint profiling of chromatin accessibility and gene expression in the same cell enables direct linkage of regulatory elements to transcriptional output, but existing approaches remain limited by low co‐capture sensitivity, proprietary platforms, and high costs. Here we present DUET‐seq, an open‐source droplet microfluidic platform for joint profiling of chromatin accessibility and gene expression from the same nucleus. DUET‐seq combines programmable dissolvable dual‐linker hydrogel beads with one‐step intra‐droplet RT‐PCR to physically co‐index RNA and transposed chromatin fragments, completing library preparation within 12 h. Optimization of joint reaction conditions, including suppression of residual Tn5 activity, achieves ∼3,200 genes per HEK293T cell while maintaining high‐quality chromatin accessibility profiles. By providing a fully disclosed reagent‐and‐device ecosystem, DUET‐seq enables researchers to optimize lysis and reaction chemistry for non‐standard tissues without proprietary constraints. We demonstrate the platform's versatility by mapping over 14,000 cis‐regulatory element‐to‐gene linkages in adult mouse brain and by profiling ∼29,000 nuclei across six stages of postnatal spermatogenesis, where paired measurements reveal two modes of temporal regulatory decoupling—epigenetic priming and chromatin inertia—that are inaccessible to unimodal assays. DUET‐seq thus provides an accessible, cost‐effective framework for joint single‐nucleus multi‐omic profiling, with broad applicability across developmental biology, disease epigenomics, and functional genomics.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 34 matches between paragraphs and lines of code.

biochengd/DUET-seq

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: bd130dc6669158a5c0734db73fd829187b4ba5e2, 24 August 2026
Languages: R (21), Python (4), Jupyter (2)
Size: 60 files, 27 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (19 files), Seurat (19 files), tidyverse (17 files), patchwork (12 files), Matplotlib (3 files), NumPy (3 files), pandas (3 files), Scanpy (3 files), ComplexHeatmap (2 files), Harmony (2 files), pheatmap (2 files), seaborn (2 files), circlize (1 file), clusterProfiler (1 file), cowplot (1 file), h5py (1 file), igraph (1 file), SciPy (1 file), scVelo (1 file), SingleCellExperiment (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
28 files
At the source: github.com/biochengd/DUET-seq

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 27 scripts, each with its path and the digest of its content;
  • 34 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability Statement

The raw sequencing data and processed datasets generated in this study have been deposited in the Gene Expression Omnibus (GEO) under accession number GSE345817 and are publicly available. The following publicly available datasets were utilized for benchmarking and comparative analysis: 10x Multiome and ISSAAC‐seq data from ArrayExpress (E‐MTAB‐11264); sci‐CAR‐seq (GSE117089), SHARE‐seq (GSE140203), SNARE‐seq2 (GSE157660), Paired‐seq (GSE130399), and SUM‐seq (GSE253165) from the Gene Expression Omnibus (GEO); and HT‐scCAT‐seq from GSA (CRA025996). The 10x Multiome mouse brain dataset was downloaded from the 10x Genomics repository. All original code and analysis scripts (including the barcode whitelist and complete oligonucleotide sequence list) used to support the findings of this study have been deposited in a GitHub repository (https://github.com/biochengd/DUET‐seq (https://github.com/biochengd/DUET-seq)).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, pages, dates, 17 authors, 6 keywords, 4 funders, 64 references.

Cite

This paper

Cheng, D., Meng, Z., Wei, L., Yang, P., Zhang, F., Luo, Z., Lai, T., Li, C., Zhao, M., Xu, M., Wang, J., Li, L., Chen, H., Li, J., Huang, A., Bu, Y., & Zhao, L. (2026). DUET-seq: An Open-Source Droplet Platform for High-Fidelity Joint Chromatin and Transcriptome Profiling Reveals Temporal Regulatory Decoupling in Single Cells. Advanced science (Weinheim, Baden-Wurttemberg, Germany), e77986. https://doi.org/10.1002/advs.77986

BibTeX

@article{cheng2026duet,
author = {Cheng, Dong and Meng, Zijun and Wei, Lan and Yang, Pingjing and Zhang, Fanfan and Luo, Zhiyi and Lai, Ting and Li, Chunli and Zhao, Mengyao and Xu, Mengqin and Wang, Jiaqi and Li, Linjun and Chen, Huarong and Li, Jin and Huang, Ailong and Bu, Youquan and Zhao, Liuyang},
title = {{DUET-seq: An Open-Source Droplet Platform for High-Fidelity Joint Chromatin and Transcriptome Profiling Reveals Temporal Regulatory Decoupling in Single Cells}},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
year = {2026},
month = sep,
pages = {e77986},
publisher = {Wiley},
issn = {2198-3844},
doi = {10.1002/advs.77986},
url = {https://doi.org/10.1002/advs.77986},
pmid = {42801560},
pmcid = {PMC13616282}
}

RIS

TY - JOUR
AU - Cheng, Dong
AU - Meng, Zijun
AU - Wei, Lan
AU - Yang, Pingjing
AU - Zhang, Fanfan
AU - Luo, Zhiyi
AU - Lai, Ting
AU - Li, Chunli
AU - Zhao, Mengyao
AU - Xu, Mengqin
AU - Wang, Jiaqi
AU - Li, Linjun
AU - Chen, Huarong
AU - Li, Jin
AU - Huang, Ailong
AU - Bu, Youquan
AU - Zhao, Liuyang
TI - DUET-seq: An Open-Source Droplet Platform for High-Fidelity Joint Chromatin and Transcriptome Profiling Reveals Temporal Regulatory Decoupling in Single Cells
T2 - Advanced science (Weinheim, Baden-Wurttemberg, Germany)
J2 - Adv Sci (Weinh)
PY - 2026
DA - 2026/09/27
SP - e77986
SN - 2198-3844
PB - Wiley
DO - 10.1002/advs.77986
UR - https://doi.org/10.1002/advs.77986
LA - en
ER -

CSL-JSON

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"container-title": "Advanced science (Weinheim, Baden-Wurttemberg, Germany)",
"author": [
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{
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{
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],
"container-title-short": "Adv Sci (Weinh)",
"page": "e77986",
"DOI": "10.1002/advs.77986",
"PMID": "42801560",
"PMCID": "PMC13616282",
"ISSN": "2198-3844",
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"issued": {
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The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1016/j.xcrm.2026.102766 [code]
A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.
Journal: Cell reports. Medicine
In common: scVelo, Harmony, SingleCellExperiment, 17 other tools, genetics / omics, 5 references
[2] doi:10.1002/imt2.70163 [code]
Spatial multi-omics unveils sphingolipid metabolic reprogramming within the retinal pathological niche.
Journal: iMeta
In common: Harmony, SingleCellExperiment, igraph, 15 other tools, genetics / omics, 1 reference
[3] doi:10.1038/s41586-026-10214-2 [code]
Multidimensional profiling of heterogeneity in supratentorial ependymomas.
Journal: Nature
In common: Harmony, SingleCellExperiment, igraph, 15 other tools, genetics / omics
[4] doi:10.1093/bioinformatics/btag652 [code]
mmVelo: a deep generative model for estimating cell state-dependent dynamics across multiple modalities.
Journal: Bioinformatics (Oxford, England)
In common: scVelo, Scanpy, seaborn, 4 other tools, genetics / omics, 10 references
[5] doi:10.1038/s41467-026-76341-6 [code]
Neonatal inflammation disrupts a temporally restricted postnatal Numb-enriched microglial state in mice.
Journal: Nature communications
In common: Harmony, SingleCellExperiment, igraph, 13 other tools, 2 references
[6] doi:10.1038/s41586-026-10629-x [code]
Whole-genome duplication shaped cell-type evolution in the vertebrate brain.
Journal: Nature
In common: scVelo, Harmony, igraph, 14 other tools, genetics / omics
[7] doi:10.1038/s41467-026-71595-6 [code]
A single-cell and spatial atlas of early human olfactory development.
Journal: Nature communications
In common: Harmony, SingleCellExperiment, igraph, 12 other tools, genetics / omics, 2 references
[8] doi:10.1073/pnas.2523130123 [code]
FABP7 controls radial glial scaffold stability during human cortical development.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: scVelo, Harmony, igraph, 13 other tools
[9] doi:10.1038/s44320-026-00208-7 [code]
Interpretable deep generative ensemble learning for single-cell omics with Hydra.
Journal: Molecular systems biology
In common: SingleCellExperiment, igraph, Scanpy, 11 other tools, 3 references
[10] doi:10.1016/j.cpblue.2026.100007 [code]
An integrated single-cell and spatial proteotranscriptomics atlas of fibroblast-driven immunoregulation within the human adult oral cavity.
Journal: Cell press blue
In common: SingleCellExperiment, igraph, circlize, 13 other tools

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