PlantNetX: Integrated bulk and single-cell RNA-seq web resource for plant functional genomics

Overview of PlantNetX and its purpose

PlantNetX is presented as a web-based transcriptomic resource that integrates bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) data to facilitate plant functional genomics. The platform was developed to bridge tissue-level co-expression analyses derived from bulk data with cell-type-specific expression profiles from scRNA-seq, addressing a gap where most existing platforms provide access to only one data modality.

The developers report that PlantNetX currently focuses on rice (Oryza sativa) and that the system was designed with extensibility in mind to incorporate additional plant species, new datasets, and further analytical tools in later releases.

Datasets included: bulk RNA-seq and single-cell RNA-seq coverage

In its initial release, PlantNetX aggregates two main classes of data:

• Seventy quality-controlled bulk RNA-seq datasets encompassing a total of 1,198 sequencing libraries.
• Nine single-cell RNA-seq datasets representing more than 580,000 cells.

The source emphasizes that some recently released single-cell datasets, which are not consistently represented across other existing platforms, are included in PlantNetX. The exact identities of individual datasets, their accession identifiers, or the criteria used for quality control are not detailed in the source abstract.

Analytical features: co-expression and gene association networks

PlantNetX implements Mutual Rank–based co-expression analysis to infer gene associations. The platform produces both global and tissue-specific gene association networks to identify co-expressed genes that may participate in shared biological pathways.

Mutual Rank is highlighted as the basis for co-expression scoring, but the preprint abstract does not provide specifics on parameter settings, thresholds, or network construction algorithms beyond this method name.

Visualization and cell-type expression summaries

The platform provides interactive visualization tools and summaries that map gene expression to specific cell types using the integrated scRNA-seq data. These interfaces are intended to help users explore how genes that are co-expressed at the tissue level are distributed across cell types, supporting more precise hypothesis generation about gene function and cellular context.

Details about the exact visualization components (for example, types of plots, interactivity features, or export options) are not enumerated in the abstract.

Validation with published plant examples

The authors validated PlantNetX using published examples from two biological contexts: plant cell-wall biosynthesis and root-hair growth. According to the source, PlantNetX retrieved expected gene association patterns and cell-type expression profiles consistent with these published examples, demonstrating that the integrated data and analytical workflows can reproduce known relationships.

The abstract does not provide the specific genes, metrics, or figures used in these validation cases; full methodological and result details are expected in the complete preprint but are not reported in the provided abstract.

Performance comparison and response-time testing

Under standardized testing conditions described by the authors, PlantNetX exhibited a shorter mean response time than the other databases that were assessed. The abstract does not list the comparator databases, the testing protocol, or quantitative response-time values.

Because the source text omits these details, users should consult the full preprint or the platform documentation for complete benchmarking procedures and results.

Planned expansion and use cases

PlantNetX was designed to be extensible: the authors state the platform will accommodate additional plant species, incoming datasets, and extra analytical tools. Intended use cases highlighted in the abstract include research into plant cell-wall biosynthesis, pathway discovery, broader functional genomics investigations, and applications in crop improvement.

The abstract does not state a release schedule for new species or feature timelines, nor does it provide user access details such as licensing, data download options, or programmatic access endpoints.

Preprint status and competing interests

This work is published as a preprint on bioRxiv and has not undergone peer review. The authors declared no competing interests in the preprint record.

Readers and potential users should consider the preprint status when interpreting the platform descriptions and reported validations, and consult the full manuscript or platform site for technical documentation, dataset provenance, and any subsequent peer-reviewed publications or updates.