Transcriptome resources can facilitate to increase yield and quality of walnuts. Finding the best transcriptome assembly has not been the subject of walnuts research as yet. This research generated 240,179,782 reads from 11 walnut leaves according to cDNA libraries. The reads provided a complete de novo transcriptome assembly. Fifteen different transcriptome assemblies were constructed from five different well-known assemblers used in scientific literature with different k-mer lengths (Bridger, BinPacker, SOAPdenovo-Trans, Trinity and SPAdes) as well as two merging approaches (EvidentialGene and Transfuse). Based on the four quality metrics of assembly, the results indicated an efficiency in the process of merging the assemblies after being generated by de novo assemblers. Finally, EvidentialGene was recognized as the best assembler for the de novo assembly of the leaf transcriptome in walnut. Among a total number of 183,191 transcripts which were generated by EvidentialGene, there were 109,413 transcripts capable of protein potential (59.72%) and 104,926 were recognized as ORFs (57.27%). In addition, 79,185 transcripts were predicted to exist with at least one hit to the Pfam database. A number of 3,931 transcription factors were identified by BLAST searching against PlnTFDB. Furthermore, 6,591 of the predicted peptide sequences contained signaling peptides, while 92,704 contained transmembrane domains. Comparison of the assembled transcripts with transcripts of the walnut and published genome assembly for the ‘Chandler’ cultivar using the BLAST algorithm led to identify a total number of 27,304 and 19,178 homologue transcripts, respectively. De novo transcriptomes in walnut leaves can be developed for the future studies in functional genomics and genetic studies of walnuts.

Background: Optimizing plant tissue culture media is a complicated process, which is easily influenced by genotype, mineral nutrients, plant growth regulators (PGRs), vitamins and other factors, leading to undesirable and inefficient medium composition. Facing incidence of different physiological disorders such as callusing, shoot tip necrosis (STN) and vitrification (Vit) in walnut proliferation, it is necessary to develop prediction models for identifying the impact of different factors involving in this process. In the present study, three machine learning (ML) approaches including multi-layer perceptron neural network (MLPNN),k-nearest neighbors (KNN) and gene expression programming (GEP) were implemented and compared to multiple linear regression (MLR) to develop models for prediction of in vitro proliferation of Persian walnut (Juglans regiaL.). The accuracy of developed models was evaluated using coefficient of determination (R²), root mean square error (RMSE) and mean absolute error (MAE). With the aim of optimizing the selected prediction models, multi-objective evolutionary optimization algorithm using particle swarm optimization (PSO) technique was applied.Results: Our results indicated that all three ML techniques had higher accuracy of prediction than MLR, for example, calculated R²of MLPNN, KNN and GEP vs. MLR was 0.695, 0.672 and 0.802 vs. 0.412 in Chandler and 0.358, 0.377 and 0.428 vs. 0.178 in Rayen, respectively. The GEP models were further selected to be optimized using PSO. The comparison of modeling procedures provides a new insight into in vitro culture medium composition prediction models. Based on the results, hybrid GEP-PSO technique displays good performance for modeling walnut tissue culture media, while MLPNN and KNN have also shown strong estimation capability.Conclusion: Here, besides MLPNN and GEP, KNN also is introduced, for the first time, as a simple technique with high accuracy to be used for developing prediction models in optimizing plant tissue culture media composition studies. Therefore, selection of the modeling technique to study depends on the researcher’s desire regarding the simplicity of the procedure, obtaining clear results as entire formula and/or less time to analyze.

Somatic embryos (SEs) can play important roles in genetic manipulation and breeding. They can be used as targets for induced mutagenesis, as material for cryopreservation and germplasm conservation, and for transformation or gene editing in support of plant improvement and proof of gene function. However, germination rates of walnut ( Juglans regia ) SEs are low, and the genetic stability of plantlets regenerated from them has not been explored. Here, we studied first the effects of gibberellic acid (GA ₃ ) and low temperature storage (LTS) on germination of walnut somatic embryos. Second, we assessed the genetic fidelity of plantlets regenerated from these SEs by comparing them to each other and to their cultivar of origin. Results showed that GA ₃ and LTS increased the walnut SE germination rate. The best rate was observed when SEs were subjected to LTS for 60 d followed by culture on a medium with either 1 or 3 mg·L ⁻¹ GA ₃ (56.6% and 46.6% germination respectively). Genetic stability was evaluated, using flow cytometry and 15 sets of ISSR primers. Flow cytometry indicated that all samples (i.e., regenerated and parental counterpart) showed the same peak. Amplified fragments of inter simple sequence repeats (ISSR) primers ranged in size from ≈200 to 1800 bp. All ISSR profiles of regenerants were monomorphic. Results did not show any genetic differences among plantlets regenerated from SEs or from their parental counterpart. Due to this apparent genetic stability, walnut SEs can be useful for genetic transformation and germplasm conservation.