# Functional annotation of novel heat stress-responsive genes in rice utilizing public transcriptomes and structurome

## TL;DR

This paper uses public data to find and annotate rice genes that respond to heat stress, revealing new insights into their functions.

## Contribution

The study introduces an integrative workflow combining transcriptome and structural data to annotate understudied genes in rice.

## Key findings

- Meta-analysis identified heat stress-responsive gene groups in rice.
- Structural alignment revealed rice-human protein similarities despite low sequence similarity.
- Candidates linked to metal homeostasis, like iron and copper metabolism, were highlighted.

## Abstract

Life science databases include large collections of public transcriptome and large-scale structural data. The reuse and integration of these datasets may facilitate the identification of understudied genes and enable functional annotation across distantly related species, including plants and humans.

In this study, we used heat stress-responsive genes in rice as a model to functionally annotate previously understudied genes by integrating publicly available transcriptome data with structural information from the AlphaFold Protein Structure Database. Initially, we conducted a meta-analysis of public heat stress-related transcriptome datasets, identified gene groups, and verified stress-related terms through enrichment analysis. Subsequently, we performed structural alignment and sequence alignment between rice and human proteins, focusing on candidates exhibiting low sequence similarity but high structural similarity. We further incorporated supplemental data from public databases, including shared domain information between rice and human. This approach yielded a unique set of these candidates, notably those associated with metal homeostasis, such as iron and copper metabolism. Overall, our integrative method provided insights into these genes by leveraging diverse, publicly available datasets.

The “plant2human workflow” for this analysis is available at https://doi.org/10.48546/WORKFLOWHUB.WORKFLOW.1206.10.