Multi-genotype analyses of long-ncRNAs in Sugarcane

This study focuses on characterizing non-coding RNAs (ncRNAs) and long non-coding RNAs (lncRNAs) in sugarcane, addressing gaps in understanding their variability, conservation, co-expression patterns, and functional roles. By leveraging publicly available RNA-Seq data and co-expression networks, this work provides a comprehensive exploration of these transcripts, offering insights into their potential biological functions.

Note

This repository serves as a resource for accessing the code, technical decisions, and results from the master's thesis "Multi-genotype analyses of long-ncRNAs in sugarcane", conducted at the Center for Nuclear Energy in Agriculture - University of São Paulo in 2024. The research was carried out in the Computational, Evolutionary, and Systems Biology Laboratory.

Approach and Methods

• How can we explore publicly available RNA-Seq data for sugarcane to identify nc/lncRNAs?

  • Predicting putative lncRNAs with CPC2
  • Predicting putative lncRNAs with PLncPRO
  • Predicting putative lncRNAs with RNAplonc
  • Aggregating the consensus of putative ncRNAs predicted by the 3 tools

• What are the main families of nc/lncRNAs present in sugarcane, and how can they be classified?

  • Annotation of ncRNAs families

• pan-RNAome inference: Are there conserved nc/lncRNAs shared among different genotypes?

  • Clustering sequences by identity
  • Inference of the pan-RNAome

• What is the probable origin of the lncRNAs in modern sugarcane hybrids, and how do they trace back to the parental species in the Saccharum complex?

  • Estimating lncRNA origin

• What are the expression and co-expression patterns of nc/lncRNAs in sugarcane across different conditions, and how can co-expression networks be utilized to annotate and infer the functional roles of these nc/lncRNAs?

  • Contrasting genotypes selection
  • Calculating the RNA expression for each sample
  • Filtering the RNAs expression matrix of the contrasting genotypes
  • Filtering the RNAs expression matrix by function
  • Analyzing RNA expression patterns after filtering
  • Principal Component Analysis of lncRNAs expression in contrasting genotypes
  • Computing gene pair relationships with Pearson correlation
  • Analyzing Network Metrics
  • Analyzing MCL clusterings (effects of Inflation value)
  • Finding conserved modules of co-expression (complete subgraphs - cliques)
  • Visualizing co-expressed gene modules in contrasting genotypes
  • Gene Ontology enrichment analysis: exploring biological function of modules

Financial Support

This research was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), process number: 130748/2022-6

Data Availability

To access the data generated in this study, please refer to the data section here.

Citation

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