Norway spruce (Picea abies) is an ecologically and economically important conifer species in northern Europe. Its exceptionally large and repetitive genome, together with recent improvements in genome assemblies and gene annotations, creates both new opportunities and substantial computational challenges for genome-scale functional analysis. At the same time, an increasing number of publicly available transcriptomic and genomic datasets provide an underused resource for studying root development, environmental adaptation and stress responses. The aim of this project is to perform systematic reanalysis and integration of Norway spruce genomic and transcriptomic datasets using updated genome resources. A major component will be the reanalysis of publicly available RNA-sequencing datasets related to root development and environmental stresses, particularly drought and nutrient availability. Raw sequencing reads will be processed from the FASTQ level and analysed against updated Norway spruce genome assemblies and annotations. The analyses will include quality control, splice-aware alignment, transcript and gene quantification, differential expression analysis, transcript structure analysis, comparative genomics, gene-family analysis and identification of candidate regulatory genes and pathways. An important objective is to determine how updated genome assemblies and annotations affect biological interpretation compared with previous analyses based on older reference resources. Multiple independent datasets will be integrated to identify reproducible transcriptional responses and candidate regulators associated with root development and environmental adaptation. Candidate proteins emerging from genomic and transcriptomic analyses will subsequently be investigated using AI-assisted structural approaches, including AlphaFold 3 and related methods. Structural modelling will be used to examine selected proteins, protein complexes, protein-protein interactions, selected protein-nucleic-acid interactions, and the structural effects of naturally occurring or experimentally relevant sequence variants. By integrating transcriptomics, comparative genomics and AI-assisted structural analysis, the project will provide an updated computational framework for Norway spruce functional genomics and support prioritization of candidate genes and proteins for subsequent functional studies related to forest adaptation and breeding.