OSCR

Ribo-ITP enables identification of translons from limited input samples.

Code ↔ Paper

7 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 7 matches
  1. [1] § Methods › Correlation of ribosome occupancy of translons vs. CDS ↔ figures/fig4.R, lines 245–330 · score 0.75 · Wilcoxon signed rank, partial Spearman, RNA seq, coefficients, correlation, CPM
  2. [2] § Methods › Identification of translated ORFs ↔ pipeline/ORFPipeline.py, lines 403–434 · score 0.67 · nucleotide periodicity, chi squared, threshold, start codons, candidate, APPRIS
  3. [3] § Methods › Correlation of ribosome occupancy of translons vs. CDS ↔ figures/fig4.R, lines 245–330 · score 0.59 · Gini coefficient, partial Spearman, correlation, CPM, cell, CDS
  4. [4] § Results › Expression patterns of non-canonical translons ↔ figures/fig4.R, lines 1–67 · score 0.58 · neural stem cells, heart, neurons, brain, tissues, embryonic
  5. [5] § Methods › Cell culture ↔ figures/fig4.R, lines 1–67 · score 0.54 · mouse embryonic stem, fetal, cells
  6. [6] § Methods › Amino acid composition comparison between translons and CDS regions ↔ figures/fig3.R, lines 133–180 · score 0.52 · CDS frequencies, amino acid, composition, genes
  7. [7] § Results › Ribo-ITP enables identification of non-canonical translation events from microdissected mouse hippocampus ↔ pipeline/ribobaseFTFilter.py, lines 204–214 · score 0.51 · Fourier transform, RiboBase, database, filtered, pipeline, transcriptome

Paper

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The authors' code

R · 342 lines · 15 KB · no license · 4 matches

  1. library(tidyverse)
  2. library(ppcor)
  3. library(ggplot2)
  4. library(scales)
  5. library(readr)
  6. library(dplyr)
  7. library(cowplot)
  8. library(viridis)
  9. library(DescTools)
  10. library("ggExtra")
  12. # Load files
  13. orfE = read_csv("outputFTembryoORF_comp.csv", col_names = TRUE)
  14. orfN = read_csv("outputFTneuralORF_comp.csv", col_names = TRUE)
  15. dataE = read_csv("outputRNASeqORFembryo.csv", col_names = TRUE)
  16. dataN = read_csv("outputRNASeqORFneural.csv", col_names = TRUE)
  18. # Add Type
  19. embryo_data <- left_join(dataE, dplyr::select(orfE, Gene, Start, Stop, Type),
  20. by = c("Gene", "Start", "Stop"))
  21. neural_data <- left_join(dataN, dplyr::select(orfN, Gene, Start, Stop, Type),
  22. by = c("Gene", "Start", "Stop"))
  24. # Label and combine datasets
  25. neural_data <- neural_data %>% mutate(Context = "Neural")
  26. embryo_data <- embryo_data %>% mutate(Context = "Embryo")
  27. combined_data <- bind_rows(neural_data, embryo_data)
  28. reads_CPM <- data.frame(reads_CPM = ((combined_data$`ORF Reads` / combined_data$`Experiment Reads`) * 1000000))
  29. combined_data <- cbind(combined_data, reads_CPM)
  30. gene_CPM <- data.frame(gene_CPM = ((combined_data$`CDS Reads` / combined_data$`Experiment Reads`) * 1000000))
  31. combined_data <- cbind(combined_data, gene_CPM)
  32. combined_data <- combined_data %>% filter(!is.na(reads_CPM) & !is.na(gene_CPM))
  34. # Combine and simplify cell lines
  35. combined_data$Cell_Line[combined_data$Cell_Line %in% c("Liver cells", "liver tissue", "liver")] <- "Liver"
  36. combined_data$Cell_Line[combined_data$Cell_Line %in% c("ES cell line A3-1", "RF8 mouse embryonic stem cell line", "Embryonic stem cells", "embryonic stem cells", "A3-1", "v6.5", "R1 mouse embryonic stem cells", "ESC line CGR8", "E14", "E14Tg2a")] <- "ESC"
  37. combined_data$Cell_Line[combined_data$Cell_Line %in% c("brain neural stem cells", "neural tube")] <- "NSC"
  38. combined_data$Cell_Line[combined_data$Cell_Line %in% c("inguinal white fat", "subcutaneous white fat", "epididymal white fat", "interscapular brown fat")] <- "Fat"
  40. combined_data$Cell_Line[combined_data$Cell_Line %in% c("Bone marrow derived primary dendritic cells", "Bone marrow derived dendritic cells", "Bone marrow derived regulatory dendritic cells", "Flt3L-DC")] <- "Bone marrow dendritic"
  41. combined_data$Cell_Line[combined_data$Cell_Line %in% c("3T3", "CD4 T-cells", "resting state T cells")] <- "T cells"
  42. combined_data$Cell_Line[combined_data$Cell_Line %in% c("Forebrain tissue (WT E14.5 mouse embryos)", "Forebrain tissue (Elp3cKO E14.5 mouse embryos)", "hippocampal", "cortical tissue", "brain cortex", "hippocampal cortex", "Cerebellum", "Fetal cortex", "dentate gyrus", "hemisphere")] <- "brain"
  43. combined_data$Cell_Line[combined_data$Cell_Line %in% c("Bone marrow derived macrophage", "RAW264", "peritoneal macrophage cells", "RAW264.7")] <- "Macrophage"
  44. combined_data[combined_data == "RAW264.7"] <- "RAW264"
  46. combined_data$Cell_Line[combined_data$Cell_Line %in% c("Mouse back skins", "primary epidermis", "Epidermal basal cells")] <- "Skin"
  47. combined_data$Cell_Line[combined_data$Cell_Line %in% c("Quadriceps muscle", "tibialis anterior tissue", "gastrocnemius tissue", "forelimbs")] <- "Skeletal muscle"
  48. combined_data$Cell_Line[combined_data$Cell_Line %in% c("ES cell derived neurons", "Primary cortical neurons", "Neurons (DIV 8) derived from CGR8 ES cells", "Cortical neuron", "Striatal cells")] <- "Neuron"
  49. combined_data$Cell_Line[combined_data$Cell_Line %in% c("EL4", "T-ALL")] <- "Leukemia"
  51. combined_data$Cell_Line[combined_data$Cell_Line %in% c("Splenic B cells", "follicular B cells", "primary splenic B cells", "resting state B cells")] <- "B cells"
  52. combined_data$Cell_Line[combined_data$Cell_Line %in% c("1 cell", "2 cell", "4 cell", "morula", "blastocyst")] <- "Preimplantation embryo"
  53. combined_data$Cell_Line[combined_data$Cell_Line %in% c("duodenum", "Ileum")] <- "Small intestine"
  54. combined_data$Cell_Line[combined_data$Cell_Line %in% c("KH2", "P19")] <- "Tetracarcinoma"
  55. combined_data$Cell_Line[combined_data$Cell_Line %in% c("Spontaneously Immortalized Mouse Keratinocyte Cult", "Primary Keratinocytes")] <- "Keratinocyte"
  57. combined_data$Cell_Line[combined_data$Cell_Line %in% c("Cardiac (left ventricle)", "heart")] <- "Heart"
  58. combined_data$Cell_Line[combined_data$Cell_Line %in% c("testis", "testes")] <- "Testes"
  59. combined_data$Cell_Line[combined_data$Cell_Line == "mouse eye"] <- "Eye"
  60. combined_data$Cell_Line[combined_data$Cell_Line == "fat"] <- "Fat"
  61. combined_data$Cell_Line[combined_data$Cell_Line == "brain"] <- "Brain"
  62. combined_data$Cell_Line[combined_data$Cell_Line == "embryo"] <- "Embryo"
  63. combined_data$Cell_Line[combined_data$Cell_Line == "lung"] <- "Lung"
  64. combined_data$Cell_Line[combined_data$Cell_Line == "kidney"] <- "Kidney"
  65. combined_data$Cell_Line[combined_data$Cell_Line == "liver"] <- "Liver"
  66. combined_data$Cell_Line[combined_data$Cell_Line == "pancreas"] <- "Pancreas"
  67. combined_data$Cell_Line[combined_data$Cell_Line == "skeletal muscle"] <- "Skeletal muscle"
  69. #simplify names
  70. combined_data[combined_data == "Dorsal section of lumbar spinal cord"] <- "Spinal cord"
  71. combined_data[combined_data == "KRPC-A cells (from gen. eng. murine tumors)"] <- "KRPC-A"
  72. combined_data[combined_data == "skin squamous tumours (skin papilloma)"] <- "Skin papilloma"
  73. combined_data[combined_data == "embryonic fibroblast"] <- "MEF"
  74. combined_data[combined_data == "lymphoid ba/f3 cells"] <- "Lymphoid"
  76. # Capitalize
  77. combined_data[combined_data == "neutrophils"] <- "Neutrophils"
  78. combined_data[combined_data == "spleen"] <- "Spleen"
  80. # Define ORF type
  81. combined_data <- combined_data %>%
  82. mutate(ORF_Class = ifelse(str_detect(Type, "dORF"), "dORF", "uORF")) %>%
  83. mutate(Group = paste(Context, ORF_Class))
  85. ### Themes ###
  86. theme_custom <- theme_minimal() +
  87. theme(
  88. panel.grid = element_blank(),
  89. panel.border = element_blank(),
  90. axis.line.x = element_line(color = "black"),
  91. axis.line.y = element_line(color = "black"),
  92. axis.ticks = element_blank(),
  93. axis.text = element_text(color = "black", size = 7),
  94. axis.title = element_text(size = 8),
  95. legend.title=element_text(size=7),
  96. legend.text=element_text(size=7),
  97. legend.key.size = unit(0.5, "lines")
  98. )
  100. theme_custom_x0 <- theme_minimal() +
  101. theme(
  102. panel.grid = element_blank(),
  103. panel.border = element_blank(),
  104. axis.line.x = element_blank(),
  105. axis.line.y = element_line(color = "black"),
  106. axis.ticks = element_blank(),
  107. axis.text = element_text(color = "black", size = 7),
  108. axis.title = element_text(size = 7),
  109. legend.title=element_text(size=8),
  110. legend.text=element_text(size=7),
  111. legend.key.size = unit(0.2, "lines"),
  112. legend.key.height = unit(0.2, "lines"),
  113. legend.key.width = unit(0.2, "lines"),
  114. legend.spacing.y = unit(0.1, "lines")
  115. )
  117. ### Dot Plot of Translation Level ###
  118. # Collapse to one value per Gene/Start/Stop/Cell_Line
  119. per_orf <- combined_data %>%
  120. group_by(Cell_Line, Group, Gene, Start, Stop) %>%
  121. summarise(reads = mean(reads_CPM, na.rm = TRUE), .groups = "drop")
  123. plot_data <- per_orf %>%
  124. group_by(Cell_Line, Group) %>%
  125. summarise(
  126. Med_Reads = median(reads, na.rm = TRUE),
  127. Pct_Translated = mean(reads > 0, na.rm = TRUE) * 100, # percent non-zero
  128. .groups = "drop"
  129. )
  132. # Create a data frame for shading
  133. background_rects <- data.frame(
  134. Group = factor(c("Neural dORF", "Neural uORF", "Embryo dORF", "Embryo uORF"),
  135. levels = levels(factor(plot_data$Group))),
  136. fill_color = c("steelblue", "turquoise", "brown", "tomato")
  137. )
  139. plot_data <- plot_data %>%
  140. mutate(
  141. Dataset = case_when(
  142. str_detect(Group, "Neural") ~ "Neural",
  143. str_detect(Group, "Embryo") ~ "Embryo"
  144. ),
  145. ORF_Class = case_when(
  146. str_detect(Group, "uORF") ~ "uORF",
  147. str_detect(Group, "dORF") ~ "dORF"
  148. )
  149. )
  151. neural_order <- plot_data %>%
  152. filter(Dataset == "Neural") %>%
  153. group_by(Cell_Line) %>%
  154. summarise(med = median(Med_Reads),
  155. pct_med = median(Pct_Translated)) %>%
  156. arrange(desc(med), desc(pct_med)) %>%
  157. pull(Cell_Line)
  159. plot_data$Cell_Line <- factor(plot_data$Cell_Line, levels = neural_order)
  161. # Plot
  162. p_dotplot <- ggplot(plot_data, aes(x = Cell_Line, y = Group)) +
  163. geom_rect(data = background_rects,
  164. aes(ymin = as.numeric(Group) - 0.5,
  165. ymax = as.numeric(Group) + 0.5),
  166. xmin = -Inf, xmax = Inf,
  167. fill = background_rects$fill_color,
  168. alpha = 0.05,
  169. inherit.aes = FALSE) +
  170. geom_point(aes(size = Pct_Translated, fill = Med_Reads+0.001), shape = 21,
  171. stroke = 0.2,
  172. color = "black") +
  173. scale_fill_viridis(
  174. option = "plasma",
  175. name = "Median reads\n(CPM)",
  176. trans = "log10",
  177. labels = scales::label_number(accuracy = 0.001)
  178. ) +
  179. scale_size(range = c(1, 3), name = "Percent\ntranslated") +
  180. labs(x = NULL, y = NULL) +
  181. theme_custom +
  182. theme(
  183. axis.text.x = element_text(angle = 90, hjust = 1),
  184. legend.box.margin = margin(t = 50)
  185. )
  190. ### Embryo violin plot ###
  191. plot_partial_correlation <- function(data, fillColor, orftype, xPos, yOff) {
  193. data <- data %>% filter(!is.na(RNASeq_Exp))
  194. data$Name = paste0(data$Gene,"_",data$Start)
  195. data$RNA_CPM = data$RNASeq_Gene/data$RNASeq_Exp * 10^6
  197. # Run Spearman partial correlation for each ORF
  198. unique_orfs = unique(data$Name)
  199. outcorr <- data.frame(Name = unique_orfs, R = NA, p = NA)
  201. for (orf in unique_orfs) {
  202. orf_data <- data %>% filter(Name == orf)
  204. if (sum(orf_data$gene_CPM) == 0) {
  205. outcorr[outcorr$Name == orf, "R"] <- NA
  206. outcorr[outcorr$Name == orf, "p"] <- NA
  207. next
  208. }
  210. test <- pcor.test(orf_data$reads_CPM, orf_data$gene_CPM, orf_data$RNA_CPM, method = "spearman")
  211. outcorr[outcorr$Name == orf, "R"] <- test$estimate
  212. outcorr[outcorr$Name == orf, "p"] <- test$p.value
  214. }
  215. outcorr$padj <- p.adjust(outcorr$p, method = "fdr")
  216. mean_R <- mean(outcorr$R, na.rm = TRUE)
  218. # Run Wilcoxon signed-rank test
  219. rhos <- outcorr$R[!is.na(outcorr$R)]
  220. wilcox_p <- wilcox.test(rhos, mu = 0, alternative = "two.sided")$p.value
  221. print(wilcox_p)
  223. # Generate violin plot
  224. plot_data = outcorr %>% filter(!is.na(R))
  225. p = ggplot(plot_data, aes(x = "", y = R)) +
  226. geom_violin(trim = TRUE, fill = fillColor) +
  227. geom_hline(yintercept = mean_R, linetype = "dashed", color = "black") +
  228. annotate("text", x = xPos, y = mean_R + yOff, label = bquote(bar(rho) == .(round(mean_R, 2))), color = "black", size = 2)+
  229. labs(x = "", y = expression("Spearman " * rho)) +
  230. geom_hline(yintercept = 0, color = "black", linewidth = 0.5) +
  231. coord_cartesian(xlim = c(1, 1.3)) +
  232. theme_custom_x0
  233. return(p)
  234. }
  236. dataE = combined_data[combined_data$Context == "Embryo", ]
  237. uorfE <- dataE %>% filter(!str_detect(Type, "dORF"))
  238. dorfE <- dataE %>% filter(str_detect(Type, "dORF"))
  239. p_uCorr = plot_partial_correlation(uorfE, "turquoise", "uORF", 1.69, 0.037)
  240. p_dCorr = plot_partial_correlation(dorfE, "steelblue", "dORF", 1.61, 0.025)
  245. ### Neural specificity analysis + plot ###
  246. specificity_correlation <- function(data, orftype, fillColor) {
  247. # cell line average
  248. avg <- data %>%
  249. group_by(Cell_Line, Gene, Start, Stop) %>%
  250. summarise(`ORF Reads` = mean(reads_CPM), .groups = "drop") %>%
  251. mutate(Name = paste0(Gene, "_", Start))
  253. avg_wide <- avg %>%
  254. dplyr::select(Name, Cell_Line, `ORF Reads`) %>%
  255. pivot_wider(names_from = Cell_Line, values_from = `ORF Reads`, values_fill = 0)
  257. # Compute Gini coefficient for each ORF across cell lines
  258. expr_cols <- setdiff(names(avg_wide), "Name")
  259. avg_wide$Gini <- apply(avg_wide[, expr_cols], 1, function(x) {
  260. if (sum(x) == 0) return(NA)
  261. Gini(x, na.rm = TRUE)
  262. })
  264. data <- data %>% filter(!is.na(RNASeq_Exp))
  265. data$Name = paste0(data$Gene,"_",data$Start)
  266. data$RNA_CPM = data$RNASeq_Gene/data$RNASeq_Exp * 10^6
  268. # Run Spearman partial correlation for each ORF
  269. unique_orfs = unique(data$Name)
  270. outcorr <- data.frame(Name = unique_orfs, R = NA, p = NA)
  272. for (orf in unique_orfs) {
  273. orf_data <- data %>% filter(Name == orf)
  275. if (sum(orf_data$gene_CPM) == 0) {
  276. outcorr[outcorr$Name == orf, "R"] <- NA
  277. outcorr[outcorr$Name == orf, "p"] <- NA
  278. next
  279. }
  281. test <- pcor.test(orf_data$reads_CPM, orf_data$gene_CPM, orf_data$RNA_CPM, method = "spearman")
  282. outcorr[outcorr$Name == orf, "R"] <- test$estimate
  283. outcorr[outcorr$Name == orf, "p"] <- test$p.value
  284. }
  285. outcorr$padj <- p.adjust(outcorr$p, method = "fdr")
  287. # Run Wilcoxon signed-rank test
  288. rhos <- outcorr$R[!is.na(outcorr$R)]
  289. wilcox_p <- wilcox.test(rhos, mu = 0, alternative = "two.sided")$p.value
  291. # Spearman for Gini vs. ORF-CDS rho
  292. merged <- left_join(avg_wide[, c("Name", "Gini")], outcorr, by = "Name") %>%
  293. filter(!is.na(Gini) & !is.na(R))
  295. included_names <- merged$Name
  296. all_names <- intersect(avg_wide$Name, outcorr$Name)
  297. excluded_names <- setdiff(all_names, included_names)
  299. # Print excluded ORFs - usually due to no CDS reads in all datasets
  300. if (length(excluded_names) > 0) {
  301. message(length(excluded_names), " ORFs were excluded from the final correlation due to missing Gini or R.")
  302. print(excluded_names)
  303. }
  305. cor_result <- cor.test(merged$Gini, merged$R, method = "spearman")
  306. print(cor_result)
  308. # Plot Gini vs Spearman rho
  309. merged$Group <- orftype
  310. p <- ggplot(merged, aes(x = Gini, y = R, color = Group)) +
  311. geom_point(alpha = 0.9, size = 1) +
  312. scale_color_manual(values = setNames(fillColor, orftype)) +
  313. labs(
  314. x = "Gini Coefficient",
  315. y = expression("Partial Spearman " * rho)
  316. ) +
  317. annotate(
  318. "text",
  319. x = 0.05, y = max(merged$R),
  320. label = bquote(rho == .(round(cor_result$estimate, 2))),
  321. hjust = 0, size = 2.5
  322. ) +
  323. theme_custom_x0 +
  324. theme(
  325. legend.position = "none"
  326. ) +
  327. geom_hline(yintercept = 0, color = "black", linewidth = 0.5)
  328. p = ggMarginal(p, type = "density", groupFill = TRUE, margins = "y")
  329. return(list(plot = p, corr_df = merged, corr_res = cor_result))
  330. }
  332. dataN = combined_data[combined_data$Context == "Neural", ]
  333. uorfN <- dataN %>% filter(!str_detect(Type, "dORF"))
  334. dorfN <- dataN %>% filter(str_detect(Type, "dORF"))
  335. outputU = specificity_correlation(uorfN, "uORF", "tomato")
  336. outputD = specificity_correlation(dorfN, "dORF", "brown")
  338. ### Arrangement ###
  339. row2 = plot_grid(p_uCorr, p_dCorr, outputU$plot, outputD$plot, "", labels = c("B", "C", "D", "E", ""), label_size = 10, ncol = 5, rel_widths = c(1,1,1,1,0.05))
  340. fig4 = plot_grid(p_dotplot, row2, ncol = 1, labels = c("A", ""), label_size = 10, rel_heights = c(1.5, 1))
  341. ggsave("fig4.pdf", fig4, width = 6.3, height = 4.3, units = "in")

fig4.R at commit c46cd1f, no license · at the source

Overview

Authors: Vighnesh Ghatpande1, Uma Paul1, Logan Persyn1, Yifan Tian1, MacKenzie A. Howard2, Can Cenik1
ORCID iDs: Vighnesh Ghatpande, Logan Persyn, Yifan Tian, MacKenzie A. Howard, Can Cenik
  1. Department of Molecular Biosciences, University of Texas at Austin,Austin, TX USA
  2. Departments of Neurology and Neuroscience, Center for Learning and Memory, Dell Medical School, University of Texas at Austin,Austin, TX USA
Institutions: The University of Texas at Austin (United States)
Journal: Nature communications, volume 17, issue 1, article 9264
Dates: received 21 July 2025; accepted 4 July 2026; published online 31 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-75571-y · PMID 42669660 · PMCID PMC13526858 · OpenAlex W7171871019
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism)
Methods: Statistics, Spectral & time-frequency, Evoked potentials, Connectivity, fMRI & imaging
Keywords: Translation, Computational biology and bioinformatics
MeSH: Protein Biosynthesis*, Ribosomes*, Animals, Blastocyst, Green Fluorescent Proteins, Hippocampus, Machine Learning, Mice, Mouse Embryonic Stem Cells, Proteomics, Ribosome Profiling, RNA, Messenger (* major topic)
Topic: RNA and protein synthesis mechanisms (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Welch Foundation (F-2027-20230405, F-2027-20260402); NIGMS (R35GM150667); U.S. Department of Health & Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (HD110096)
Citations: not cited yet (Europe PMC); 112 references in the paper
Research resources: C57BL6/J mice RRID:IMSR_JAX:000664

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up114/translons

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Commit: c46cd1fb214b3e79542e087e764b4ab0af6c2406, 13 February 2026
Languages: Python (5), R (3)
Size: 12 files, 8 scripts
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Found in: “Code availability”
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Tools: pandas (5 files), NumPy (4 files), SciPy (3 files), tidyverse (3 files), cowplot (2 files), ggplot2 (2 files), statsmodels (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
9 files
At the source: github.com/up114/translons

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Ghatpande, V., Paul, U., Persyn, L., Tian, Y., Howard, M. A., & Cenik, C. (2026). Ribo-ITP enables identification of translons from limited input samples. Nature communications, 17(1), 9264. https://doi.org/10.1038/s41467-026-75571-y

BibTeX

@article{ghatpande2026ribo,
author = {Ghatpande, Vighnesh and Paul, Uma and Persyn, Logan and Tian, Yifan and Howard, MacKenzie A. and Cenik, Can},
title = {{Ribo-ITP enables identification of translons from limited input samples}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {9264},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-75571-y},
url = {https://doi.org/10.1038/s41467-026-75571-y},
pmid = {42669660},
pmcid = {PMC13526858}
}

RIS

TY - JOUR
AU - Ghatpande, Vighnesh
AU - Paul, Uma
AU - Persyn, Logan
AU - Tian, Yifan
AU - Howard, MacKenzie A.
AU - Cenik, Can
TI - Ribo-ITP enables identification of translons from limited input samples
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/31
VL - 17
IS - 1
SP - 9264
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-75571-y
UR - https://doi.org/10.1038/s41467-026-75571-y
LA - en
ER -

CSL-JSON

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{
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}
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"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "9264",
"DOI": "10.1038/s41467-026-75571-y",
"PMID": "42669660",
"PMCID": "PMC13526858",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-75571-y",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
31
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]
}
}

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