OSCR

Genome-wide and allele-resolved maps of the radial architecture of the mouse genome

Code ↔ Paper

23 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 23 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Computational methods › RNA-seq data analysis › Data processing ↔ scripts/rnaseq/RNAseq_R_processing.R, lines 29–110 · score 0.86 · GeneCounts, imported gene, DESeq2, log2FC, RNA seq, M36
  2. [2] § Methods › Computational methods › Hi-C › Hi-C data processing ↔ scripts/hic/02_Hic_convert.sh, lines 6–44 · score 0.82 · TADs insulation scores, Cooltools, Explorer, eigs, HiC, Cooler
  3. [3] § Methods › Computational methods › Chromflock › Assessment of Chr X topology in 3D genome reconstructions ↔ scripts/visualization/FigS/FS7.FGHIJ.chrX_shape.py, lines 1–59 · score 0.81 · convex hull, surface area, mesh, prolateness, sphericity, gyration
  4. [4] § Methods › Computational methods › GPSeq data analysis › Multivariate linear regression model ↔ scripts/visualization/Fig1/F1QT.R, lines 11–78 · score 0.79 · explained variance, GC content, gene density, LMG, NMAE, R2
  5. [5] § Methods › Computational methods › Hi-C › Allele-specific Hi-C analysis ↔ scripts/hic/00_Genome_build.sh, the whole file · a weak match · score 0.78 · mm39 reference, diploid B6, REL2021, bcftools, consensus, indels
  6. [6] § Methods › Computational methods › RNA-seq data analysis › Allele-specific RNA-seq analysis ↔ scripts/rnaseq/RNAseq_R_processing_allelic.R, lines 1–75 · score 0.74 · extremely low, DESeq2, phased normalized, imported, rnaseq, mNPC
  7. [7] § Methods › Computational methods › Gene Ontology analysis ↔ scripts/visualization/Fig3/F3DEFG.R, lines 18–60 · score 0.72 · clusterProfiler, enrichGO, simplify, db, cutoff, mm
  8. [8] § Methods › Computational methods › RNA-seq data analysis › Data processing ↔ scripts/rnaseq/RNAseq_nonphased_pipeline.sh, lines 56–152 · score 0.72 · quantMode, GeneCounts, GENCODE, STAR, fastp, workflow
  9. [9] § Methods › Computational methods › Gene Ontology analysis ↔ scripts/visualization/Fig4/F4.Z.R, lines 12–59 · score 0.70 · clusterProfiler, enrichGO, simplify, db, cutoff, mm
  10. [10] § Methods › Computational methods › Chromflock › Homolog-specific radial positioning analysis ↔ scripts/visualization/Fig6/F6.chromflock.py, lines 1–70 · score 0.65 · B6 CAST homologous, nuclear periphery, radial position, Chromflock, autosomes, beads
  11. [11] § Results › Interplay between radial repositioning and gene expression dynamics ↔ scripts/visualization/Fig3/F3H.R, the whole file · a weak match · score 0.64 · moving outward, fold change, gene expression changes, mESC, variation, inward
  12. [12] § Methods › Computational methods › GPSeq data analysis › Allele-specific GPSeq score calculation ↔ scripts/rnaseq/RNAseq_allelic_phased_01_filterBam.sh, lines 6–43 · score 0.61 · SNPsplit, parental genome, BAM, filtered, phased
  13. [13] § Methods › Computational methods › ATAC-seq ↔ scripts/rnaseq/RNAseq_allelic_phased_01_filterBam.sh, lines 6–43 · score 0.61 · SNPsplit, allelic phased, BAM, SAMtools, parental, mapping
  14. [14] § Methods › Computational methods › GPSeq data analysis › GPSeq data processing ↔ scripts/gpseq/gpseq_allelic_radiality.sh, lines 1–43 · score 0.60 · DpnII, Bowtie2, barcode, cut, pipeline, UMI
  15. [15] § Methods › Computational methods › GPSeq data analysis › Allele-specific GPSeq score calculation ↔ scripts/gpseq/gpseq_allelic_radiality.sh, lines 45–95 · score 0.57 · SNPsplit, quality, BAM, UMI, filtered, radiality
  16. [16] § Results › The inactive chromosome X is less peripheral than its active copy ↔ scripts/visualization/Fig5/F5.E.R, lines 14–120 · score 0.56 · genes escaping, lncRNA, Xist, XCI, escapee, protein
  17. [17] § Results › The inactive chromosome X is less peripheral than its active copy ↔ scripts/visualization/Fig6/F6.chromflock.py, lines 1–70 · score 0.55 · Chromflock configuration, nuclear periphery, radial positions, radius, Mb, Figure 6
  18. [18] § Methods › Computational methods › GPSeq data analysis › Multivariate linear regression model ↔ scripts/visualization/FigS/FS7.L.gpseq_corr.py, lines 1–43 · score 0.54 · Linear regression, GPSeq score, fitted
  19. [19] § Methods › Computational methods › GPSeq data analysis › GPSeq score, ranked GPSeq score (rGS) and |ΔrGS| calculation ↔ scripts/rnaseq/RNAseq_R_processing_allelic.R, lines 1–75 · score 0.54 · CAST alleles, confidently, extreme, transformed, log2, mNPC
  20. [20] § Methods › Experimental methods › GPSeq ↔ scripts/gpseq/gpseq_allelic_radiality.sh, lines 1–43 · score 0.52 · DpnII, barcode, adapter, UMI, SNP, sequences
  21. [21] § Results › Radial genome reorganization during mouse neurodifferentiation ↔ scripts/visualization/Fig3/F3H.R, the whole file · a weak match · score 0.52 · moves inward, moved outward, mESC, upregulated, downregulated, mNPC
  22. [22] § Methods › Computational methods › ATAC-seq ↔ scripts/atacseq/ATACseq_allelic_phased_02_getCoverage.sh, lines 9–44 · score 0.51 · allelic phased, BEDTools, SAMtools, atacseq, BAM, coverage
  23. [23] § Results › Interplay between radial repositioning and gene expression dynamics ↔ scripts/visualization/Fig3/F3C.R, lines 11–70 · score 0.50 · fold change, gene expression, outside, outward, mESC, inward

Paper

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The authors' code

Shell · 97 lines · 4.3 KB · no license · 3 matches

  1. #!/bin/bash
  3. #SBATCH --job-name=AllelicRadiality --time=24:00:00 --nodes=1 --ntasks-per-node=1 --cpus-per-task=16 --mem=64G --array=0-7
  5. ### ------------------------------------------------------------------------------
  6. ###
  7. ### Author: Wing Hin Yip ([email hidden]) or ([email hidden])
  8. ### Description: Pipeline to analyse allele-specific RNA-seq data from the output of nf-core/rnaseq pipeline
  9. ### Allelic Radiality Script adapter from our original nextflow-pipeline.
  10. ### Python and R scripts used are part of [https://github.com/BiCroLab/nextflow-gpseq]
  11. ###
  12. ### ------------------------------------------------------------------------------
  14. ### Example Code: depending on input fastq file, adjust barcode sequence according to the samplesheet.
  16. file=($(ls /path/to/*_trimmed.fq.gz))
  17. input=${file[$SLURM_ARRAY_TASK_ID]}
  19. TMP=${TMPDIR}
  21. ref="/path/to/reference/genome.fa"
  22. bt2index="/path/to/bowtie2/index/"
  23. ref_cutsite="/path/to/reference/cut/sites/bed.gz"
  24. snp_file="/path/to/snp/annotation"
  26. enzyme="DpnII"
  27. cutsite="GATC"
  28. barcode="GTCGTATC"
  30. sample=$(basename ${input} _R1_001_trimmed.fq.gz)
  32. threads=16
  34. mkdir -p ${sample} && cd ${sample}
  36. # Extracting barcode, cutsite and UMI information from reads
  37. fbarber flag extract ${input} ${sample}.hq.fastq.gz \
  38. --filter-qual-output ${sample}.lq.fastq.gz \
  39. --unmatched-output ${sample}.noprefix.fastq.gz \
  40. --log-file ${sample}.flagextracting.log \
  41. --pattern 'umi8bc8cs4' --simple-pattern \
  42. --flagstats bc cs --filter-qual-flags umi,30,.2 \
  43. --threads ${threads} --chunk-size 100000
  45. # Filtering reads checking for correct barcode and cutsite
  46. fbarber flag regex ${sample}.hq.fastq.gz ${sample}.filtered.fastq.gz \
  47. --unmatched-output ${sample}.unmathced.fastq.gz \
  48. --log-file ${sample}.filtering.log \
  49. --pattern "bc,^(?<bc>"${barcode}"){s<2}\$" "cs,^(?<cs>${cutsite}){s<2}\$" \
  50. --threads ${threads} --chunk-size 100000
  52. # Aligning filtered fastq files to reference genome using bowtie2
  53. bowtie2 -x ${bt2index} ${sample}.filtered.fastq.gz \
  54. --very-sensitive -L 20 --score-min L,-0.6,-0.2 --end-to-end --reorder -p ${threads} \
  55. -S ${sample}.sam &> ${sample}.mapping.log
  57. # Sorting sam file and converting it to bam
  58. samtools sort ${sample}.sam --threads ${threads} -o ${sample}.bam
  60. # Filtering bamfile on quality score, chromosomes etc
  61. sambamba view ${sample}.bam \
  62. -t ${threads} -f bam -F "mapping_quality>=30 and not secondary_alignment and not unmapped and not chimeric and ref_name!='chrM' and ref_name!='MT'" > ${sample}.clean.bam
  64. # Split aligned reads into separate genomes by SNPs
  65. SNPsplit --single_end --no_sort --snp_file ${snp_file} ${sample}.clean.bam
  66. mv ${sample}.clean.SNPsplit_report.txt ${sample}.clean.SNPsplit_report.log
  68. # Generate allele-specific bed file
  69. for bam in *.genome[12].bam; do
  71. name=$(basename ${bam} .bam)
  73. sambamba view -q -t ${threads} -h -f bam -F "reverse_strand" ${name}.bam -o ${name}.revs.bam
  74. sambamba view -q -t ${threads} ${name}.revs.bam | convert2bed --input=sam --keep-header - > ${name}.revs.bed
  75. cut -f 1-4 ${name}.revs.bed | sed 's/~/\t/g' | cut -f 1,3,7,16 | gzip > ${name}.revs.umi.txt.gz
  77. sambamba view -q -t ${threads} -h -f bam -F "not reverse_strand" ${name}.bam -o ${name}.plus.bam
  78. sambamba view -q -t ${threads} ${name}.plus.bam | convert2bed --input=sam --keep-header - > ${name}.plus.bed
  79. cut -f 1-4 ${name}.plus.bed | sed 's/~/\t/g' | cut -f 1,3,7,16 | gzip > ${name}.plus.umi.txt.gz
  81. group_umis.py ${name}.revs.umi.txt.gz ${name}.plus.umi.txt.gz ${name}.clean.umis.txt.gz --compress-level 6 --len 4
  82. umis2cutsite.py ${name}.clean.umis.txt.gz ${ref_cutsite} ${name}.clean.umis.atcs.txt.gz --compress --threads ${threads}
  83. umi_dedupl.R ${name}.clean.umis.atcs.txt.gz ${name}.clean.umis.dedup.txt.gz -c ${threads} -r 10000
  84. zcat ${name}.clean.umis.dedup.txt.gz | awk 'BEGIN{{FS=OFS="\t"}}{{print $1 FS $2 FS $2 FS "pos_"NR FS $4}}' | gzip > ${name}.bed.gz
  86. done
  88. mkdir fastq && mv *.fastq.gz fastq/
  89. mkdir bam && mv *.bam *.bam.bai bam/
  90. mkdir bed && mv *.bed bed/
  91. mkdir log && mv *.txt *.txt.gz *.tsv *.log *.yaml log/
  93. rm *.sam
  94. rm *.conflicting.bam *.allele_flagged.bam
  95. rm *.revs.bam *.revs.bam.bai *.revs.bed *.plus.bam *.plus.bam.bai *.plus.bed

gpseq_allelic_radiality.sh at commit a0bacf7, no license · at the source

Overview

Authors: Lorenzo Salviati1, Wing Hin Yip2,3, Giulia Peveri1, Agnese Loda4,5,6, Erik Wernersson2,3, Nicola Crosetto1,2,3, Edith Heard7,8, Britta A. M. Bouwman2,3, Magda Bienko1,2,3
ORCID iDs: Giulia Peveri, Erik Wernersson, Nicola Crosetto, Britta A. M. Bouwman, Magda Bienko
  1. Human Technopole, Viale Rita Levi-Montalcini 1, 22157, Milan, Italy
  2. Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 17177, Sweden
  3. Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden
  4. Institut Imagine, UMR 1163 24 Bd du Montparnasse, 75015, Paris, France
  5. Institut Pasteur, Department of Developmental and Stem Cell Biology, 25-28 Rue du Dr Roux, 75015, Paris (France)
  6. Université Paris Cité, 45 Rue des Saints-Pères, 75006, Paris (France)
  7. The Francis Crick Institute, London, UK, 1 Midland Road, London, NW1 1AT, UK
  8. Collège de France, Paris, France 11 Place Marcelin Berthelot, 75005, Paris, France
Institutions: Human Technopole (Italy); Karolinska Institutet (Sweden); Science for Life Laboratory (Sweden); Institut des Maladies Génétiques Imagine (France); Institut Pasteur (France); Université Paris Cité (France); The Francis Crick Institute (United Kingdom); Collège de France (France)
Journal: Research Square (preprint)
Dates: published online 19 June 2026
Type: Preprint
License: CC BY
Identifiers: DOI 10.21203/rs.3.rs-9927928/v1 · OpenAlex W7165116616
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism)
Methods: Statistics
Topic: Developmental Biology and Gene Regulation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: European Regional Development Fund (860675); Vetenskapsrådet; H2020 Marie Skłodowska-Curie Actions (860675)
Citations: not cited yet (Europe PMC); 98 references in the paper

Abstract

Despite extensive research on 3D genome architecture across species, genome organization along the periphery-center axis of the nucleus—radiality—remains a rather overlooked feature of the 3D genome, particularly in non-human cells. Here, we leveraged Genomic loci Positioning by Sequencing (GPSeq) to chart the radial arrangement of the mouse genome in mouse embryonic stem cells (mESC) and mouse neural progenitor cells (mNPC). As in human cells, the radial genome organization in these two mouse cell types was non-random, with gene density and transcriptional activity concentrating toward the center, and GC-content showing the strongest predictive power for genome radiality. During differentiation toward the neuronal lineage, many regions and genes repositioned radially, with some of the most inward-moving genomic regions being significantly enriched in genes associated with neurodevelopment and transcriptionally upregulated during the transition from mESC to mNPC. However, the relationship between the extent of radial repositioning and transcriptional changes was far from trivial, with some radially relocating genes not displaying any significant expression change at this developmental stage, and others showing opposite changes in radiality and expression (i.e., inward-moving genes being downregulated and vice versa). We also generated the first allele-resolved radiality maps in mNPC cells, which revealed that the alleles of most autosomal genes occupy highly similar radial positions in the nucleus. In contrast, genes on the X chromosome showed much more pronounced allelic differences in their radial placement when comparing the inactive and active X chromosomes, except for those genes that escape X chromosome inactivation (‘escapees’). Lastly, we integrated GPSeq and Hi-C data to generate thousands of allele-resolved, single-cell 3D genome reconstructions, demonstrating that the distribution in 3D space of allele pairs occupying highly similar radial positions is explainable by geometrical constraints. Our work provides the first high-resolution and allele-resolved radiality maps of the mouse genome, showing how radiality changes during neuronal cell lineage specification and suggesting that the design principles governing genome radiality are highly conserved between human and mouse cells.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 23 matches between paragraphs and lines of code.

BiCroLab/nextflow-gpseq

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 56c990aada17ff5709e322e9969ee2197639e292, 4 August 2025
Languages: R (6), Python (3)
Size: 25 files, 9 scripts
Software Heritage: not archived
Found in: the text, “GPSeq data processing”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (3 files), NumPy (2 files), pandas (2 files), cowplot (1 file), ggplot2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
11 files
At the source: github.com/BiCroLab/nextflow-gpseq

nf-core/rnaseq

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: a1fcdddd3b826fe46eb46f0479f2ff8a7815af05, 23 September 2026
Languages: Python (6), R (4), Shell (2), Perl (1)
Size: 851 files, 13 scripts
Software Heritage: archived
Found in: the text, “Allele-specific RNA-seq analysis”
Holds: README, license file, environment (.devcontainer/devcontainer.json, .devcontainer/setup.sh, modules/local/deseq2_qc/environment.yml, modules/local/preprocess_transcripts_fasta_gencode/environment.yml, modules/local/star_genomeparams_upgrade/environment.yml, modules/nf-core/dupradar/environment.yml, modules/nf-core/fastp/environment.yml, modules/nf-core/fastqc/environment.yml, modules/nf-core/gffread/environment.yml, modules/nf-core/gunzip/environment.yml, modules/nf-core/multiqc/environment.yml, modules/nf-core/ribodetector/environment.gpu.yml), continuous integration, documentation
Not found: CITATION.cff, tests
Tools: DESeq2 (1 file), ggplot2 (1 file), Nextflow (1 file), pheatmap (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
15 files
At the source: github.com/nf-core/rnaseq

nf-core/atacseq

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: e805fffab3d2113d41768d08a71bfdda129fe396, 25 July 2026
Languages: Python (6), R (4)
Size: 228 files, 10 scripts
Software Heritage: archived
Found in: the text, “ATAC-seq”
Holds: README, license file, environment (pyproject.toml, .devcontainer/devcontainer.json), continuous integration, documentation
Not found: CITATION.cff, tests
Tools: ggplot2 (3 files), reshape2 (2 files), DESeq2 (1 file), pheatmap (1 file), pysam (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
12 files
At the source: github.com/nf-core/atacseq

elgw/chromflock

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 379c8b47ecca20be7c9312ef47dec1d86d34166f, 19 December 2024
Languages: C (69), C/C++ (52), Shell (7), Python (4), MATLAB (1)
Size: 193 files, 133 scripts
Software Heritage: archived
Found in: the text, “Generation of single-cell 3D genome reconstructi”
Holds: README, documentation
Not found: license file, CITATION.cff, environment file, tests, continuous integration
Tools: NumPy (4 files), pandas (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
134 files
At the source: github.com/elgw/chromflock/

BiCroLab/AllelicRadiality

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: a0bacf72a760104a16559d391baac82adf8c480c, 27 May 2026
Languages: R (48), Shell (20), Python (6), C (1)
Size: 84 files, 75 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (48 files), ggplot2 (48 files), cowplot (16 files), BEDTools (9 files), SAMtools (7 files), ggpubr (6 files), NumPy (6 files), tidyverse (6 files), Matplotlib (5 files), pandas (5 files), SciPy (4 files), clusterProfiler (3 files), DESeq2 (3 files), reshape2 (3 files), FastQC (2 files), patchwork (2 files), BCFtools (1 file), STAR (1 file), Subread (featureCounts) (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
76 files
At the source: github.com/BiCroLab/AllelicRadiality

Code Availability

All the scripts used for data processing, analysis and plotting from Source Data are available on GitHub at https://github.com/BiCroLab/AllelicRadiality.

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

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:

  • 5 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 240 scripts, each with its path and the digest of its content;
  • 23 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

Datasets cited

Data Availability

A list of all the datasets generated in this study and of the corresponding source samples is provided in Supplementary Table 1. The GPSeq and ATAC-seq data (FASTQ and BAM files) generated and analyzed in this study have been deposited on the European Nucleotide Archive (ENA) under accession number PRJEB112822. The mESC RNA-seq and ATAC-seq data analyzed in this study were generated previously98 and are available on the NCBI Sequence Read Archive (SRA) under accession number PRJNA478782 (RNA-seq) and PRJNA1186175 (ATAC-seq). Source Data and code to regenerate the plots displayed in this manuscript are available at https://github.com/BiCroLab/AllelicRadiality. Source data is also available at the Zenodo repository, at 10.5281/zenodo.20374207 (http://dx.doi.org/10.5281/zenodo.20374207).

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 2, 28 September 2026

  • Funding: added European Commission: 860675; Vetenskapsrådet; H2020 Marie Skłodowska-Curie Actions: 860675

Version 1, 27 September 2026: the first record

Recorded: type, journal, dates, 9 authors, 97 references.

Cite

This paper

Salviati, L., Yip, W. H., Peveri, G., Loda, A., Wernersson, E., Crosetto, N., Heard, E., Bouwman, B. A. M., & Bienko, M. (2026). Genome-wide and allele-resolved maps of the radial architecture of the mouse genome. Research Square (preprint). https://doi.org/10.21203/rs.3.rs-9927928/v1

BibTeX

@article{salviati2026genome,
author = {Salviati, Lorenzo and Yip, Wing Hin and Peveri, Giulia and Loda, Agnese and Wernersson, Erik and Crosetto, Nicola and Heard, Edith and Bouwman, Britta A. M. and Bienko, Magda},
title = {{Genome-wide and allele-resolved maps of the radial architecture of the mouse genome}},
journal = {Research Square (preprint)},
year = {2026},
month = jun,
publisher = {Research Square},
issn = {2693-5015},
doi = {10.21203/rs.3.rs-9927928/v1},
url = {https://doi.org/10.21203/rs.3.rs-9927928/v1}
}

RIS

TY - JOUR
AU - Salviati, Lorenzo
AU - Yip, Wing Hin
AU - Peveri, Giulia
AU - Loda, Agnese
AU - Wernersson, Erik
AU - Crosetto, Nicola
AU - Heard, Edith
AU - Bouwman, Britta A. M.
AU - Bienko, Magda
TI - Genome-wide and allele-resolved maps of the radial architecture of the mouse genome
T2 - Research Square (preprint)
J2 - Res Sq
PY - 2026
DA - 2026/06/19
SN - 2693-5015
PB - Research Square
DO - 10.21203/rs.3.rs-9927928/v1
UR - https://doi.org/10.21203/rs.3.rs-9927928/v1
ER -

CSL-JSON

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"title": "Genome-wide and allele-resolved maps of the radial architecture of the mouse genome",
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"author": [
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"family": "Salviati",
"given": "Lorenzo"
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{
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"given": "Britta A. M."
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"container-title-short": "Res Sq",
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"issued": {
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19
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}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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Journal: Nature communications
In common: STAR, pysam, BEDTools, 14 other tools, genetics / omics, 4 references
[4] doi:10.1016/j.celrep.2026.117073 [code]
Single-cell epigenomics uncovers heterochromatin instability and transcription factor dysfunction during mouse brain aging.
Journal: Cell reports
In common: Subread (featureCounts), pysam, BEDTools, 15 other tools, genetics / omics, mouse, 2 references
[5] doi:10.1126/sciadv.aed2952 [code]
Activation of transposable elements is linked to a region- and cell type-specific interferon response in Parkinson's disease.
Journal: Science advances
In common: Subread (featureCounts), STAR, pysam, 15 other tools, 2 references
[6] doi:10.7554/elife.107393 [code]
Chromosome-scale genome assembly of the European common cuttlefish &lt;i&gt;Sepia officinalis&lt;/i&gt;.
Journal: eLife
In common: Subread (featureCounts), STAR, pysam, 13 other tools, 4 references
[7] doi:10.1038/s41467-026-69944-6 [code]
Multi-modal dissection of cell-type specific TDP-43 pathology in the motor cortex.
Journal: Nature communications
In common: pysam, BEDTools, SAMtools, 13 other tools, genetics / omics, 4 references
[8] doi:10.1038/s41531-026-01287-x [code]
Faecalibacterium prausnitzii, depleted in the Parkinson's disease microbiome, improves motor deficits in α-synuclein overexpressing mice.
Journal: NPJ Parkinson's disease
In common: FastQC, Subread (featureCounts), STAR, 11 other tools, mouse, 3 references
[9] doi:10.1038/s41467-026-71432-w [code]
MeCP2 gene dosage-dependent neurodevelopmentally restricted defects arise by aberrant activation of cell fate-determining bivalent genes.
Journal: Nature communications
In common: FastQC, Subread (featureCounts), STAR, 11 other tools, mouse, 1 reference
[10] doi:10.1038/s41380-026-03578-4 [code]
Assessing molecular gene by treatment interactions using a population of neural progenitors exposed to valproic acid and lithium.
Journal: Molecular psychiatry
In common: FastQC, Subread (featureCounts), STAR, 9 other tools, genetics / omics, 2 references

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