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Nonapeptide molecular evolution during the adaptive radiation of Tanganyika cichlids.

Code ↔ Paper

20 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 20 matches · 16 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § METHODS › Lake Tanganyika cichlids de novo assemblies ↔ Scripts/01.Map_assemblies.sh, the whole file · a weak match · score 0.96 · Bwa mem, STAR aligner, BioProject, Oreochromis niloticus, GCF_001858045.2, mapped
  2. [2] § METHODS › Correlated evolution between discrete traits ↔ Scripts/14.2.BayesTraits_run.sh, the whole file · a weak match · score 0.86 · Log Bayes Factor, correlated evolution, BayesTraits, independent model, stone, v4
  3. [3] § METHODS › Tissue expression and correlation with behavioral phenotypes ↔ Scripts/15.PGLMM.R, lines 68–189 · score 0.86 · tailed Bayesian, posterior samples, pMCMC, ESS, interval, probability
  4. [4] § METHODS › Multiple sequence alignments ↔ Scripts/08.Alignments.sh, the whole file · a weak match · score 0.85 · pal2nal, v7.526, consensus sequences, reference gene, amino acids, V14
  5. [5] § METHODS › Correlated evolution between discrete traits ↔ Scripts/14.5.BT_reverse-jump.sh, the whole file · a weak match · score 0.83 · reversible jump, BayesTraits, independent model, v4, discrete, MCMC
  6. [6] § RESULTS › Positive selection on nonapeptide system genes ↔ Scripts/15.PGLMM.R, lines 1–31 · score 0.75 · VTR2Ba, VTR1Ab, VTR1Aa, VTR2Ab, VTR2Aa, OTRa
  7. [7] § RESULTS › Positive selection on nonapeptide system genes ↔ Scripts/09.2.NucDiv_by_domain.R, the whole file · a weak match · score 0.74 · VTR2Ab.tr2, VTR2Ba, VTR1Ab, VTR2Aa, EL3, OTRa
  8. [8] § RESULTS › Correlation between behavioral phenotypes and SNPs ↔ Scripts/14.5.BT_reverse-jump.sh, the whole file · a weak match · score 0.73 · reversible jump, BayesTraits, independent model, pair bonding, evolution, phenotypes
  9. [9] § RESULTS › Association between the expression of nonapeptide genes and behavioral phenotypes ↔ Scripts/15.PGLMM.R, lines 68–189 · score 0.72 · HPD interval, posterior samples, pMCMC, ESS, Geweke, diagnostics
  10. [10] § RESULTS › Association between the expression of nonapeptide genes and behavioral phenotypes ↔ Scripts/15.PGLMM.R, lines 1–31 · score 0.71 · VTR2Bb, pMCMC, VTR1Aa, VTR2Aa, pair bonding, caregivers
  11. [11] § METHODS › Lake Tanganyika cichlids de novo assemblies ↔ Scripts/07.2.Extract_consensus_transcriptomes.sh, the whole file · a weak match · score 0.70 · STAR aligner, consensus sequences, GCF_001858045.2, accession, genome, Transcriptomic
  12. [12] § RESULTS › Structure of the nonapeptides and their receptors ↔ Scripts/09.2.NucDiv_by_domain.R, the whole file · a weak match · score 0.68 · signal peptide, canonical GPCR, copeptin, neurophysin, tr1, IL3
  13. [13] § METHODS › Positive selection ↔ Scripts/12.FEL_pos_selection.sh, the whole file · a weak match · score 0.67 · gene wide, positive selection, gene trees, FEL, HyPhy, alignments
  14. [14] § METHODS › Nonapeptide system genes repertoire ↔ Scripts/07.1.Extract_consensus_genomes.sh, the whole file · a weak match · score 0.64 · species consensus, consensus sequence, BCFtools, Seqtk, BAM, Samtools
  15. [15] § METHODS › Nonapeptide system genes repertoire ↔ Scripts/03.Local_Database_Blast.sh, the whole file · a weak match · score 0.63 · PacBio, databases, makeblastdb, BLASTn, assemblies, species
  16. [16] § RESULTS › Correlation between behavioral phenotypes and SNPs ↔ Scripts/14.2.BayesTraits_run.sh, the whole file · a weak match · score 0.61 · correlated evolution, BayesTraits, independent model, amino acid, likelihood, log
  17. [17] § METHODS › Multiple sequence alignments ↔ Scripts/07.1.Extract_consensus_genomes.sh, the whole file · a weak match · score 0.58 · v7.526, consensus sequences, MAFFT, frame, genomic, genome
  18. [18] § RESULTS › Nonapeptide system genes repertoire ↔ Scripts/01.Map_assemblies.sh, the whole file · a weak match · score 0.57 · Oreochromis niloticus, GCF_001858045.2, reference genome, NCBI, accession, assembly
  19. [19] § METHODS › Positive selection ↔ Scripts/10.Gene_Trees.sh, the whole file · a weak match · score 0.55 · amino acid sequences, gene trees, IQTREE, nucleotide, alignments, transcript
  20. [20] § METHODS › Nonapeptide system genes repertoire ↔ Scripts/07.2.Extract_consensus_transcriptomes.sh, the whole file · a weak match · score 0.51 · consensus sequence, BCFtools, Seqtk, BAM, Samtools, transcripts

Paper

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The authors' code

R · 264 lines · 6.8 KB · MIT · 4 matches

  1. # PGLMM analysis for gene expression data. This script runs Phylogenetic Generalized Linear Mixed Models (PGLMM) using MCMCglmm for each gene and phenotype, extracts pMCMC values, and saves results to a file.
  3. library(tidyverse) # version 2.0.0
  4. library(ape) # version 5.8
  5. library(MCMCglmm) # version 2.36
  6. library(coda) # version 0.19-4.1
  8. # Read input data
  9. input_file <- "Data/16.PGLMM/inp_exp_scaled.txt"
  10. df <- read.table(input_file, header = TRUE, sep = "\t")
  12. # Read Phylogeny in Newick format
  13. tree_tan <- read.tree("Data/16.PGLMM/tree_tan.nwk")
  15. # Convert factors in initial data frame
  16. df$spp <- factor(df$spp)
  17. df$sex <- factor(df$sex)
  18. df$Sex_caregiver <- factor(df$Sex_caregiver)
  19. df$Pair_bonding <- factor(df$Pair_bonding)
  21. # Convert to long format
  22. gene_cols <- c("OT","VT","OTRa","OTRb",
  23. "VTR1Aa","VTR1Ab",
  24. "VTR2Aa","VTR2Ab",
  25. "VTR2Ba","VTR2Bb")
  26. df_long <- df %>%
  27. pivot_longer(
  28. cols = all_of(gene_cols),
  29. names_to = "GeneID",
  30. values_to = "log_expression"
  31. )
  33. # Function to run PGLMM for a given gene and phenotype
  34. run_pglmm <- function(data, gene_name, phenotype, inv.phylo) {
  36. # Subset data for the specific gene
  37. df_gene <- subset(data, GeneID == gene_name)
  39. # Ensure species factor levels match the phylogeny
  40. df_gene$spp <- factor(df_gene$spp, levels = rownames(inv.phylo$Ainv))
  42. # Define the formula dynamically based on the phenotype
  43. formula_txt <- paste0(
  44. "log_expression ~ ",
  45. phenotype,
  46. " * sex + d15N + d13C"
  47. )
  49. # Convert to formula object
  50. formula_obj <- as.formula(formula_txt)
  52. # Run the MCMCglmm model
  53. model <- MCMCglmm(
  54. formula_obj,
  55. random = ~ spp,
  56. family = "gaussian",
  57. ginverse = list(spp = inv.phylo$Ainv),
  58. data = as.data.frame(df_gene),
  59. nitt = 1000000,
  60. burnin = 100000,
  61. thin = 100,
  62. verbose = FALSE
  63. )
  65. return(model)
  66. }
  68. # Function to extract results
  69. extract_results <- function(model, gene, phenotype,
  70. pheno_term, sex_term, inter_term) {
  72. sol <- model$Sol
  74. # -----------------------------
  75. # Convergence diagnostics (ONLY pheno, sex, interaction)
  76. # -----------------------------
  77. get_geweke <- function(term) {
  79. z_val <- geweke.diag(as.mcmc(sol[, term]))$z
  80. conv_ok <- abs(z_val) < 1.95
  82. return(c(z_val, conv_ok))
  83. }
  85. pheno_conv <- get_geweke(pheno_term)
  86. sex_conv <- get_geweke(sex_term)
  87. int_conv <- get_geweke(inter_term)
  88. d15N_conv <- get_geweke("d15N")
  89. d13C_conv <- get_geweke("d13C")
  91. # -----------------------------
  92. # Effective sample size
  93. # -----------------------------
  94. get_ess <- function(term) {
  96. ess_val <- effectiveSize(as.mcmc(sol[, term]))
  98. return(ess_val)
  99. }
  101. ess_pheno <- get_ess(pheno_term)
  102. ess_sex <- get_ess(sex_term)
  103. ess_int <- get_ess(inter_term)
  104. ess_d15N <- get_ess("d15N")
  105. ess_d13C <- get_ess("d13C")
  107. # -----------------------------
  108. # Extract posterior stats + HPD
  109. # -----------------------------
  110. extract_stats <- function(term) {
  112. if (!(term %in% colnames(sol))) return(c(NA, NA, NA, NA))
  114. # Extract posterior samples for the term
  115. post <- sol[, term]
  116. mean_post <- mean(post)
  118. # Two-tailed Bayesian pMCMC
  119. pMCMC <- 2 * min(mean(post > 0), mean(post < 0))
  121. # 95% HPD interval
  122. hpd <- HPDinterval(as.mcmc(post), prob = 0.95)
  123. CI_low <- hpd[1]
  124. CI_high <- hpd[2]
  126. return(c(mean_post, pMCMC, CI_low, CI_high))
  127. }
  129. # Extract stats for each term
  130. pheno_stats <- extract_stats(pheno_term)
  131. sex_stats <- extract_stats(sex_term)
  132. int_stats <- extract_stats(inter_term)
  133. d15N_stats <- extract_stats("d15N")
  134. d13C_stats <- extract_stats("d13C")
  136. # Compile results into a data frame
  137. return(data.frame(
  138. gene = gene,
  140. # Convergence diagnostics
  141. geweke_pheno = pheno_conv[1],
  142. conv_pheno = pheno_conv[2],
  143. geweke_sex = sex_conv[1],
  144. conv_sex = sex_conv[2],
  145. geweke_interaction = int_conv[1],
  146. conv_interaction = int_conv[2],
  147. geweke_d15N = d15N_conv[1],
  148. conv_d15N = d15N_conv[2],
  149. geweke_d13C = d13C_conv[1],
  150. conv_d13C = d13C_conv[2],
  152. # ESS
  153. ESS_pheno = ess_pheno,
  154. ESS_sex = ess_sex,
  155. ESS_interaction = ess_int,
  156. ESS_d15N = ess_d15N,
  157. ESS_d13C = ess_d13C,
  159. # Phenotype effect
  160. mean_pheno = pheno_stats[1],
  161. p_pheno = pheno_stats[2],
  162. HPD_low_pheno = pheno_stats[3],
  163. HPD_high_pheno = pheno_stats[4],
  165. # Sex effect
  166. mean_sex = sex_stats[1],
  167. p_sex = sex_stats[2],
  168. HPD_low_sex = sex_stats[3],
  169. HPD_high_sex = sex_stats[4],
  171. # Interaction
  172. mean_interaction = int_stats[1],
  173. p_interaction = int_stats[2],
  174. HPD_low_interaction = int_stats[3],
  175. HPD_high_interaction = int_stats[4],
  177. # d15N
  178. mean_d15N = d15N_stats[1],
  179. p_d15N = d15N_stats[2],
  180. HPD_low_d15N = d15N_stats[3],
  181. HPD_high_d15N = d15N_stats[4],
  183. # d13C
  184. mean_d13C = d13C_stats[1],
  185. p_d13C = d13C_stats[2],
  186. HPD_low_d13C = d13C_stats[3],
  187. HPD_high_d13C = d13C_stats[4]
  188. ))
  189. }
  191. # ----------------------------------
  192. # Run models for both phenotypes
  193. # ----------------------------------
  194. phenotypes <- c("Sex_caregiver", "Pair_bonding")
  196. all_genes <- unique(df_long$GeneID)
  198. for (phenotype in phenotypes) {
  200. cat("\nRunning phenotype:", phenotype, "\n")
  202. # Define output file path
  203. output_file <- paste0("Data/16.PGLMM/out/", phenotype, "_results.txt")
  205. # Remove NAs for this phenotype
  206. df_pheno <- df_long[!is.na(df_long[[phenotype]]), ]
  208. # Prune tree to only species with the phenotype
  209. tree <- keep.tip(tree_tan, as.vector(unique(df_pheno$spp)))
  210. inv.phylo <- inverseA(tree, nodes = "TIPS", scale = FALSE)
  212. # Ensure factor levels are set
  213. df_pheno[[phenotype]] <- factor(df_pheno[[phenotype]])
  215. # Define term names dynamically
  216. pheno_levels <- levels(df_pheno[[phenotype]])
  217. sex_levels <- levels(df_pheno$sex)
  219. pheno_term <- paste0(phenotype, pheno_levels[2])
  220. sex_term <- paste0("sex", sex_levels[2])
  221. inter_term <- paste0(pheno_term, ":", sex_term)
  223. results_list <- list()
  225. # -------------------------------
  226. # Loop over genes
  227. # -------------------------------
  229. for (g in all_genes) {
  231. cat(" Gene:", g, "\n")
  233. # Run model with error handling
  234. model <- try(run_pglmm(df_pheno, g, phenotype, inv.phylo))
  236. if (inherits(model, "try-error")) {
  237. cat(" Failed:", g, "\n")
  238. next
  239. }
  241. # Extract results
  242. res <- extract_results(model, g, phenotype,
  243. pheno_term, sex_term, inter_term)
  244. results_list[[g]] <- res
  245. }
  247. # Combine gene results
  248. results_df <- bind_rows(results_list)
  250. # ----------------------------------
  251. # Save phenotype-specific results
  252. # ----------------------------------
  254. write.table(
  255. results_df,
  256. file = output_file,
  257. sep = "\t",
  258. row.names = FALSE,
  259. col.names = TRUE,
  260. quote = FALSE
  261. )
  263. cat("Saved:", output_file, "\n")
  264. }

15.PGLMM.R at commit d2b5a65, under MIT · at the source

Overview

Authors: Pol Sorigue1,2, Walter Salzburger3, Rui F Oliveira1,2
ORCID iDs: Pol Sorigue, Walter Salzburger, Rui F Oliveira
  1. GIMM—Gulbenkian Institute for Molecular Medicine, Oeiras, Portugal
  2. ISPA—University Institute for Psychological, Social and Life Sciences, Lisbon, Portugal
  3. Department of Environmental Sciences, Zoological Institute, University of Basel, Basel, Switzerland
Institutions: Gulbenkian Institute for Molecular Medicine (Portugal); Instituto Gulbenkian de Ciência (Portugal); University of Basel (Switzerland); ISPA - Instituto Universitário (Portugal)
Journal: Journal of neuroendocrinology, volume 38, issue 6, article e70203
Dates: received 18 June 2025; accepted 20 May 2026; published online 8 June 2026; in print June 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1111/jne.70203 · PMID 42259525 · PMCID PMC13246276 · OpenAlex W4411601965
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.06.18.660417
Status: code verified
Categories: other (organism), cellular / molecular (subfield)
Methods: Statistics
Keywords: cichlids, nonapeptides, oxytocin, social behavior, vasotocin
MeSH: Cichlids*, Evolution, Molecular*, Oxytocin*, Vasotocin*, Animals, Phylogeny, Receptors, Oxytocin, Receptors, Vasopressin, Selection, Genetic, Social Behavior (* major topic)
Topic: Animal Behavior and Reproduction (Ecology, Evolution, Behavior and Systematics, Agricultural and Biological Sciences), according to OpenAlex
Funding: Fundação para a Ciência e a Tecnologia (PTDC/BIA‐COM/3068/2020, PTDC/BIA-COM/3068/2020); Swiss National Science Foundation (208002)
Citations: not cited yet (Europe PMC); 70 references in the paper

Abstract

Oxytocin (OT) and vasotocin (VT) are evolutionarily conserved nonapeptides that regulate a wide range of physiological and behavioral processes in vertebrates. Their receptor families have undergone gene duplications that facilitated functional diversification throughout vertebrate evolution. Using the diverse cichlid species in Lake Tanganyika, which have undergone repeated evolutionary transitions between social phenotypes, we investigated the molecular evolution of the nonapeptide system and its potential involvement in social behavior. We performed a positive selection analysis based on the dN/dS ratio and examined the correlation between amino acid variants and two social phenotypes. We also analysed gene expression data to explore associations between brain receptor expression and social phenotype variation. Our findings reveal that, while most sites in nonapeptide receptors are under strong purifying selection, a few sites– primarily in the extended intracellular loop 3 (IL3) of VTR2A receptors– show signatures of positive selection. Additionally, a specific amino acid in VTR2Aa correlates with pair‐bonding, suggesting its potential role in social attachment. Gene expression analyses further revealed that components of the nonapeptide system, including VTR2Bb and OT, are differentially expressed across social phenotypes, supporting a role for regulatory variation alongside coding changes. Together, these findings provide new insights into how conserved neuroendocrine systems contribute to social diversity in cichlids.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 20 matches between paragraphs and lines of code.

psorigue/Molecular-evolution-OTVT-in-LT-cichlids

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d2b5a65a58c30e02039738ee8413853ff49f851c, 9 June 2026
Languages: Shell (17), R (7), Python (1)
Size: 650 files, 25 scripts
Software Heritage: not archived
Found in: “DATA AVAILABILITY STATEMENT”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SAMtools (2 files), tidyverse (2 files), BCFtools (1 file), Biopython (1 file), pandas (1 file), STAR (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
27 files
At the source: github.com/psorigue/Molecular-evolution-OTVT-in-LT-cichlids

Zenodo 19077530

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “DATA AVAILABILITY STATEMENT”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.5281/zenodo.19077530

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 25 scripts, each with its path and the digest of its content;
  • 20 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

All scripts used in this study are publicly available on GitHub (https://github.com/psorigue/Molecular-evolution-OTVT-in-LT-cichlids), and a permanent archived version is available in Zenodo (https://doi.org/10.5281/zenodo.19077530).

Reproduced under the paper's license (CC BY-NC), 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

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 10 MeSH terms, 2 funders, 70 references.

Cite

This paper

Sorigue, P., Salzburger, W., & Oliveira, R. F. (2026). Nonapeptide molecular evolution during the adaptive radiation of Tanganyika cichlids. Journal of neuroendocrinology, 38(6), e70203. https://doi.org/10.1111/jne.70203

BibTeX

@article{sorigue2026nonapeptide,
author = {Sorigue, Pol and Salzburger, Walter and Oliveira, Rui F},
title = {{Nonapeptide molecular evolution during the adaptive radiation of Tanganyika cichlids}},
journal = {Journal of neuroendocrinology},
year = {2026},
month = jun,
volume = {38},
number = {6},
pages = {e70203},
publisher = {Wiley},
issn = {0953-8194},
doi = {10.1111/jne.70203},
url = {https://doi.org/10.1111/jne.70203},
pmid = {42259525},
pmcid = {PMC13246276}
}

RIS

TY - JOUR
AU - Sorigue, Pol
AU - Salzburger, Walter
AU - Oliveira, Rui F
TI - Nonapeptide molecular evolution during the adaptive radiation of Tanganyika cichlids
T2 - Journal of neuroendocrinology
J2 - J Neuroendocrinol
PY - 2026
DA - 2026/06/01
VL - 38
IS - 6
SP - e70203
SN - 0953-8194
PB - Wiley
DO - 10.1111/jne.70203
UR - https://doi.org/10.1111/jne.70203
LA - en
ER -

CSL-JSON

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"PMID": "42259525",
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