RNA-Seq, employing high-throughput sequencing, is pivotal for studying RNA expression in plant science, offering insights into the transcriptome’s identity, quantity, and structure, along with molecular interactions and modifications¹.

Applications in Plane Science:

1. Transcriptome Assembly:
   Facilitates assembly in plant species with complex genomes or lacking a reference genome.
   Identifies novel genes, isoforms, alternative splicing events, and annotates gene functions.
2. Expression Atlas:
   Constructs a comprehensive database of gene expression profiles across diverse plant tissues, organs, developmental stages, and environmental conditions.
   Reveals spatial and temporal dynamics, regulatory networks, and pathways involved¹².
3. Network Analysis:
   Identifies co-expressed or co-regulated gene groups.
   Unveils gene modules, clusters, hubs, and aids in inferring gene functions and interactions¹².
4. Structural Alteration:
   Detects structural changes in plant transcriptomes, such as gene fusions, translocations, inversions, and deletions.
   Identifies genetic variations, mutations, and their impacts on plant phenotypes and evolution¹².

Challenges and Limitations:

1. Data Quality and Quantity:
   Requires high-quality RNA samples and reliable sequencing platforms.
   Data quality and quantity impact downstream analysis and result interpretation¹.
2. Data Analysis and Interpretation:
   Involves complex data sets necessitating specialized bioinformatics tools.
   Outcomes depend on factors like experimental design, choice of reference genome, normalization methods, and biological context.
3. Data Integration and Comparison:
   Allows integration with other omics datasets but demands common standards and formats.
   Requires appropriate methods and tools for effective data integration and comparison.

References:

1. Design, execution, and interpretation of plant RNA-seq analyses. https://www.frontiersin.org/articles/10.3389/fpls.2023.1135455/full. 
2. Frontiers | Unleashing the power within short-read RNA-seq for plant research: Beyond differential expression analysis and toward regulomics. https://www.frontiersin.org/articles/10.3389/fpls.2022.1038109/full. 
3. IJMS | Free Full-Text | Single-Cell RNA Sequencing for Plant Research: Insights and Possible Benefits. https://www.mdpi.com/1422-0067/23/9/4497.