Dataset DOI: 10.5061/dryad.t4b8gtjff

Description of the data and file structure

Superb starlings were captured across three sites in central Kenya with a gradient of rainfall, and kept in aviaries in the moderate rainfall savanna site. Baseline and stress-induced corticosterone levels were measured by ELISA assay at initial capture, and at 3 and 6 month timepoints in captivity (Table S1 of associated manuscript RSTB-2025-0027.R1). Reduced representation bisulfite sequencing (RRBS) libraries were sequenced for each individual. This data includes sequence processing, methylation analysis, variant calling and relatedness matrix generation.

Files and variables

File: Rubenstein_Solomon_dryad_v2.zip

Zip file with subfolders for each analysis.

1) alignments_metilene_lme4qtl_run_code_SNP_calling 

includes raw sequence file trimming trimming_raw_sequencer_fq_files.txt and Bismark bisulfite read alignment bismark_bisulfite_alignments.txt to the Lamprotornis superbus reference genome (sequence files are hosted on National Center for Biotechnology Sequence Read Archive under BioProject accession PRJNA1252789, and genome reference under GenBank Accession GCA_015883425.2).

The folder also includes sorting_alignments_and_SNV_calling.txt for CGmapTools v0.1.3 SNP calling, indexing_and_merging_SNV_vcfs_and_matrix_gen.txt for GEMMA v0.98.5 relatedness matrix generation, bismark_cov_processing_and_metilene_runs_intersects.txt for Metilene v0.2-8 DMR analysis and bedtools v2.31.1 intersects, and lme4qtl_R_code.txt (based on Lindner et al. 2021) for lme4qtl v0.2.2/lme4 v1.1.37 CpG site analysis.

fdrtool_R_lme4qtl_pvalues_to_qvalues.txt is fdrtool v1.2.18 of lme4qtl p-values.

header_for_lme4qtl_input_files.txt is the header for the lme4qtl files. cpg_cluster_test.txt is a check for promoter clustering of significant CpG sites.

GCA_015883425.2_CU_Lasu_v2_genomic_genes_and_pseudogenes_sorted.bed is genome bed track for genes. GCA_015883425.2_CU_Lasu_v2_genomic_just_10kb_promoters_sorted_intersect_withinfonlyfrom_v2_genes_and_pseudogenes_10kbpromoter.bedgraph is a bed track for 10kb promoters.

2) metilene_runs 

includes the above bismark_cov_processing_and_metilene_runs_intersects.txt and also metilene input and output files, as well as the promoter and gene bodies tracks above

3) randomized_metilene_runs 

metilene_runs_randomized_header_wild_3_and_3_6.txt, code for 10 Metilene runs with randomized sample headers, using the same input file, just with changed headers

randomized_header_wild_3.csv, and randomized_header_wild_6.csv, randomized_header_3_6.csv are the 10 randomized headers for each group comparison

3_vs_6_metilene_genebodies.txt, wild_vs_6_metilene_genebodies.txt, wild_vs_3_metilene_genebodies.txt, wild_vs_6_metilene_promoter.txt, wild_vs_3_metilene_promoter.txt, 3_vs_6_metilene_promoter.txt are bedtools intersect tracks with significant DMRs from original metilene run with real non-randomized data

4) R_lme4qtl_input_files

all input files for lme4qtl analysis above

5) lme4qtl_metilene_intersects_code

includes 61_sampling_site_intersects.txt, for further CpG site bedtools intersects

wild_vs_3_metilene_genebodies.txt, 3_vs_6_metilene_promoter.txt, 3_vs_6_metilene_genebodies.txt, wild_vs_6_metilene_promoter.txt, wild_vs_6_metilene_genebodies.txt, wild_vs_3_metilene_promoter.txt are metilene DMR region bed tracks. promoters_for_promoters_DMRs.txt, is bed track for promoters associated with metilene DMRs

References:

Guo, W., Zhu, P., Pellegrini, M., Zhang, M. Q., Wang, X., Ni, Z. 2018. CGmapTools improves the precision of heterozygous SNV calls and supports allele-specific methylation detection and visualization in bisulfite-sequencing data. Bioinformatics 34, 381-387. (doi:10.1093/bioinformatics/btx595)

Jühling, F., Kretzmer, H., Bernhart, S.H., Otto, C., Stadler, P.F. & Hoffmann, S. 2016. Metilene: fast and sensitive calling of differentially methylated regions from bisulfite sequencing data. Genome Res. 26, 256–262. (doi:10.1101/gr.196394.115)

Krueger, F., Andrews, S.R. 2011. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27, 1571–1572. (doi:10.1093/bioinformatics/btr167)

Lindner, M., Laine, V. N., Verhagen, I., Viitaniemi, H. M., Visser, M. E., van Oers, K., Husby, A. 2021. Rapid changes in DNA methylation associated with the initiation of reproduction in a small songbird. Mol. Ecol. 30, 3645-3659. (doi: 10.1111/mec.15803)

Quinlan, A.R., Hall, I.M. 2010 BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842. (doi:10.1093/bioinformatics/btq033)

Strimmer, K. 2008. fdrtool: a versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics 24, 1461-1462. (doi:10.1093/bioinformatics/btn209)

Zhou, X., & Stephens, M. 2012. Genome-wide efficient mixed-model analysis for association studies. Nat. Gen. 44, 821-824. (doi:10.1038/ng.2310)

Ziyatdinov, A., Vázquez-Santiago, M., Brunel, H., Martinez-Perez, A., Aschard, H., Soria, J. M. 2018. lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals. BMC Bioinformatics 19, 68. (doi:10.1186/s12859-018-2057-x)