Abstract: The dataset consist of two DNA alignments in FASTA format of genotyping-by-sequencing (GBS) data for a set of Eurasian Tilia L. species (lime, linden, basswood) and a CSV file ("Tilia_sample_ident.csv") providing the sequence number information for all included individuals. In dataset 1 ("Dataset_1_Tilia_species_GBS_alignment.fas") all analyzed species were included to obtain a species phylogeny. Dataset 2 ("Dataset_2_Tilia_IRAN_GBS_alignment.fas") is derived from 85 Iranian Tilia individuals that were complemented with 6 individuals of their closest relatives. To obtain a larger taxon set, two independent GBS datasets (ENA PRJEB80134 and GenBank PRJNA811982) were combined and processed together through de-novo assembly within ipyrad v.0.9.58 after de-multipexing in the CASAVA pipeline 1.8 and trimming of barcodes and adapters with Cutadapt. Clustering of the sequence data was performed with a minimum coverage of 6x and a clustering threshold of 0.90 and using the ipyrad default settings for all other parameters. Raw data for the merged datasets are available through 1) European Nucleotide Archive (ENA) project PRJEB80134 and 2) NCBI GenBank BioProject PRJNA811982. An analysis of the data of BioProject PRJNA81198 is described in Shekhovtsov et al. (2022; Diversity 14:256; https://doi.org/10.3390/d14040256). Analyses of the merged data of dataset 1 and the Iranian samples of dataset 2 are described in Ala et al. (2025; BMC Plant Biology: "Phylogenomics of Western Eurasian Tilia: Merging GBS datasets to place the Hyrcanian Forest limes").
License: CC BY 4.0 (Creative Commons Attribution)
DOI: 10.5447/ipk/2025/10
Content: 0 Directories 3 Files (100.9 MB)
| CONTRIBUTOR: |
Ali Bagheri,
Nastaran Ala,
Dörte Harpke
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| CREATOR: |
Frank R. Blattner
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| PUBLISHER: | e!DAL - Plant Genomics and Phenomics Research Data Repository (PGP), IPK Gatersleben, Seeland OT Gatersleben, Corrensstraße 3, 06466, Germany |
| SIZE: | 6.6 KB |
| SUBJECT: | Genotyping-by-sequencing, Tilia, Phylogeny, Hyrcanian Forest, Phylogeography, Merging GBS datasets |