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Abstract Objective Black poplar (Populus nigra L.) is a species native to Eurasia with a wide distribution area. It is an ecologically important species from riparian ecosystems, that is used as a parent of interspecific (P. deltoides x P. nigra) cultivated poplar hybrids. Variant detection from transcriptomics sequences of 241 P. nigra individuals, sampled in natural populations from 11 river catchments (in four European countries) is described here. These data provide new valuable resources for population structure analysis, population genomics and genome-wide association studies. Data description We generated transcriptomics data from a mixture of young differentiating xylem and cambium tissues of 480 Populus nigra trees sampled in a common garden experiment located at Orléans (France), corresponding to 241 genotypes (2 clonal replicates per genotype, at maximum) by using RNAseq technology. We launched on the resulting sequences an in-silico pipeline that allowed us to obtain 878,957 biallelic polymorphisms without missing data. More than 99% of these positions are annotated and 98.8% are located on the 19 chromosomes of the P. trichocarpa reference genome. The raw RNAseq sequences are available at the NCBI Sequence Read Archive SPR188754 and the variant dataset at the Recherche Data Gouv repository under https://doi.org/10.15454/8DQXK5 .

Abstract Backgroud Populus nigra is a major tree species of ecological and economic importance for which several initiatives have been set up to create genomic resources. In order to access the large number of Single Nucleotide Polymorphisms (SNPs) typically needed to carry out a genome scan, the present study aimed at evaluating RNA sequencing as a tool to discover and type SNPs in genes within natural populations of P. nigra. Results We have devised a bioinformatics pipeline to call and type SNPs from RNAseq reads and applied it to P. nigra transcriptomic data. The accuracy of the resulting RNAseq-based SNP calling and typing has been evaluated by (i) comparing their position and alleles to those previously reported in candidate genes, (ii) assessing their genotyping accuracy with respect to a previously available SNP chip and (iii) evaluating their inter-annual repeatability. We found that a combination of several callers yields a good compromise between the number of variants type and the accuracy of genotyping. We further used the resulting genotypic data to carry out basic genetic analyses whose results confirm the quality of the RNAseq-based SNP dataset. Conclusions We demonstrated the potential and accuracy of RNAseq as an efficient way to genotype SNPs in P. nigra. These results open prospects towards the use of this technology for quantitative and population genomics studies.