The coding transcriptome represents mRNAs that are ultimately translated into proteins and drive cellular function. While bulk RNA-seq is used widely to profile gene expression across reproductive tissues and developmental stage, single cell (sc) and spatial RNA-seq (22) provides cell-level resolution and allows to dissect heterogeneity of reproductive tissues (21).

Beyond coding mRNAs, non-coding RNAs add critical regulatory layers in reproductive processes. Among these, microRNAs (miRNAs), long non-coding (lnc) RNAs, and resulting competing endogenous (ce) RNAs are particularly relevant for network-based analyses.

RNA-seq experiments often produce extensive lists of differentially expressed genes (DEGs), which are difficult to interpret through manual literature review alone. Functional enrichment analysis provides a systematic approach by identifying statistically overrepresented biological pathways and function (66, 67). However, enrichment outputs can themselves be overwhelming, often comprising long, redundant lists of terms. Network visualization mitigates this by clustering related gene sets into coherent themes, reducing redundancy and helping to grasp the overall picture of enrichment results.

EnrichmentMap (68), available as a Cytoscape app (69) and also as a web tool (70), is a tool of choice for this. It organizes enrichment results into networks where nodes are enriched terms and edges represent gene overlap between the terms, see example in Figure 1A. Additional Cytoscape apps such as clusterMaker (71), WordCloud (72), and AutoAnnotate (73) further enhance interpretability by grouping and labeling clusters. This protocol (74) provides a great example of enrichment analysis pipeline with EnrichmentMap visualization.

The choice of enrichment tool depends on species coverage and the statistical approach, i.e., predefined gene set vs. ranked gene list from differential expression analysis. For instance, DAVID (75) performs gene set enrichment and supports annotations for over 50 species, including domestic cat and ferret, making it a good choice for non-model species analysis. Meanwhile, GSEA (66) operates on ranked gene list but is limited to human and mouse annotations, relying on the MSigDB database; therefore, analysis in other species require orthology mapping. Finally, g: Profiler (76) supports over 800 species via Ensembl and allows custom annotations, making it another great choice for non-model species analysis. Outputs from all three of the above enrichment tools can be then visualized with EnrichmentMap. Ideally, species-specific databases should be used to avoid missing lineage-specific genes. On the other hand, human and mouse annotation databases provide more functional information and can be used to expand the results and generate additional hypotheses. However, this should be done carefully and only in addition to the species-specific annotation to not over-interpret your results: some species-specific genes, particularly those involved in immune and reproductive functions, may lack direct counterparts in human or mouse, limiting transferability. For a broader overview of gene set enrichment analysis see (77).

Beyond standard enrichment analysis, topology-aware methods consider the actual structure and direction of pathways. For example SPIA (78) combines classical enrichment with the analysis of how a pathway is perturbed under specific conditions, and it is available for numerous species annotated in KEGG database, including domestic cat and ferret. Other approaches, such as PROGENy (79), estimate pathway activity based on downstream gene responses, while CARNIVAL (80) reconstructs causal networks by linking interactions that have both direction and sign. These methods are particularly useful for studying signaling pathways, but are often limited to human and mouse, requiring orthology mapping for other species.

Unlike traditional approaches that focus on individual DEGs, network-based strategies capture gene interactions, which have been shown to be more predictive of phenotype than single-gene markers (81). PPI networks position DEGs within interaction maps, revealing functional clusters and signaling cascades, and including such analysis on top of the initial pathway enrichment can add confidence in the observed data trends. STRING database integrates experimental and predictive evidence for both functional and physical interactions of proteins for many species (82, 83), and StringApp in Cytoscape (84) allows to map additional information to the network, see example in Figure 1B. Mice studies can utilize GeneMANIA (85) and it’s Cytoscape app (86) which collects numerous interactions from various databases, and expand it by addition of transcription factor binding information from manually curated TRRUST v2 database (87). For cattle, domestic cat and ferret, AnimalTFDB 4.0 database (88) can be used for predicted transcription factors.

When PPI networks are too large they resemble so called “hairballs” and require the use of clustering methods to improve network readability (89). Cytoscape apps such as clusterMaker (71) and MCODE (90) can extract PPI clusters based on their interaction score, which can then be analyzed for functional enrichment (e.g., integrated function in StringApp) to improve biological interpretability. Utilizing human orthologs, CORUM database (91) can also be used to extract known mammalian protein complexes.

GRNs can model relationships between regulators (transcription factors, miRNA) and targets (mRNA) and are a powerful abstraction of biological systems (62, 63); see example in Figure 1C. Zhao et al. (92) provide an in-depth overview of tools for inferring GRNs from various types of expression data, dividing them into model-based, information-based and machine learning-based methods. Model-based methods, including differential equation, Boolean and Bayesian methods, are suitable for inferring small real networks, while information theory-based methods, including Pearson correlation coefficient and (conditional/part) mutual information, are suitable for steady-state data (92). Machine learning-based methods, such as widely used tool GENIE3 that utilizes random forests to infer GRN (93) and its adaptation for time-series expression data dynGENIE3 (94), provide best results when reconstructing large-scale networks. In addition, reverse engineering approaches have been developed to trace back the initial relationships between genes that resulted in the observed gene expression (62). Mercatelli et al. (95) go through a variety of tools available for GRN inference, and highlight that the optimal tool selection ultimately relies on the biological context studied and data availability on the species, transcription factors, cellular context or specific perturbation.

Integrating strategies can extend GRNs to multi-layer networks by incorporating miRNA-mRNA interactions, ceRNA relationships, genomic variants and other omics data layers. Morabito et al. (96) provide a useful overview of recent algorithms and tools applied to integrate genomics, transcriptomics, proteomics, and metabolomics, while a more ML oriented approach can be found in the review of Picard et al. (97).

Machine learning uses computation to recognize patterns in the data by fitting predictive models to it or identifying informative clustering within data (98). This approach allows scientists to make predictions where experimental data is lacking to guide future research and to improve the understanding of biological systems. Greener et al. (98) provide an essential guide to ML for biologists that is a good starting point, which can further be expanded with practical advices from Chicco (99) and more in-depth review of use for biological networks from Camacho et al. (100).

ML methods are divided into supervised, where a model is fitted to labeled data, and unsupervised, where a model identifies patterns in unlabeled data (98). Apart from traditional ML, an area of deep learning that relies on neural networks has been rapidly developing for genomics analyses (101, 102), see example in Figure 1D. While deep learning is a powerful tool, it is limited to specific applications where a large amount of highly structured data is available, i.e., each data point has many features with clear relationship (103). Generally, traditional ML should be the starting point to find the most appropriate method for a given analysis and in some cases outperforms deep representation learning in phenotype prediction from transcriptomics data (81). At the same time, interpretable ML for omics data is becoming more prevalent in systems biology (104).

Because ML typically requires large amount of data points, from hundreds and thousands for traditional ML to millions for deep learning, as well as training and validation sets for supervised methods, this approach may not always be applicable to bulk transcriptomic data in non-model species. When sample size is limited, applying unsupervised ML methods, such as t-SNE (105) and UMAP (106), to scRNA-seq data can enable detailed profiling of reproductive tissues and their cellular environment, providing foundation for in vitro system development and generation of further hypotheses. Here single-nuclei RNA-seq approaches are particularly useful in field conditions, as tissues can be snap-frozen in liquid nitrogen without immediate dissociation (107). In addition, genomics data is growing fast for wild species and opens possibilities to study evolutionary trends in reproduction with the use of ML in the future, including miRNA emergence in placental animals (108) and effect of deleterious mutations on species fitness (109, 110).

The principle of “garbage in, garbage out” is borrowed from computer science and can be applied directly to omics analysis: flawed input data inevitably produce misleading predictions and interpretations. For transcriptomics-based phenotype prediction, proper normalization and robust regression methods are essential, but the factors that have the biggest impact still consist of adequate biological replications and sequencing depth, ensuring accurate sample metadata, incorporating complementary data types such as proteomics, and improved prior knowledge. Paton et al. demonstrated that preprocessing choices significantly affect downstream enrichment results (111). In order to trust the results of network inference and be able to apply ML methods in a study, researchers must produce/utilize high-quality raw data and provide transparent reporting of quality control and processing.

Termed as the “curse of dimensionality,” a common problem in omics datasets is that the number of features is much higher than the number of samples. This leads to overfitting and poor generalization in ML models (104). Dimensionality reduction through feature extraction, selection or engineering can reduce the number of variables (data sparsity) and improve prediction reliability (112). Lasso regularized models and nearest shrunken centroids introduce sparsity by removing contributions of unimportant features (113), as used in the example of combining transcriptomics and genetic variants to predict phenotype from genotype (114).

While omics data and its integration into network-based or ML approaches are powerful methods to infer observed changes in biological systems, they can also lead to overinterpretation when patterns are mistaken for causal relationships. Hub nodes, for example, often reflect annotation density rather than true biological centrality. Similarly, inferred regulatory relationships of nodes via edges should be treated as hypotheses rather than definitive interactions, particularly in non-model species where prior knowledge is sparse. It is always best practice to report data quality and confidence scores, be careful when discussing results and, when possible, validate predictions experimentally. Ultimately, insight gained from computational analysis and network visualization should rather guide hypothesis formulation and future functional studies but never replace them.