Pipeline for RNA-Seq Analysis of Axillary Bud Outgrowth in Energy Cane and Sugarcane
Creators: name: Silva, Nicholas Vinicius
name: de Carvalho, Lucas Miguel
name: Ferreira, Pedro
Publisher: name: Zenodo
Year: 2024

description: Pipeline for RNA-Seq Analysis of Axillary Bud Outgrowth in Energy Cane and Sugarcane

This dataset and pipeline serve as a valuable resource for researchers investigating the transcriptome of energy cane (Saccharum spp. hybrid) and sugarcane (Saccharum officinarum). The study focuses on gene expression profiling during the initial stages of axillary bud outgrowth, examining three distinct time points: 0, 24, and 48 hours. The files encompass all major steps of the analysis, including raw data preprocessing, transcriptome assembly, gene expression quantification, and downstream analyses.

These files are an integral part of the study:Transcriptomic Profiling During the Initial Hours of Axillary Bud Outgrowth Reveals Genotype-Specific Responses in Energy Cane and SugarcaneAuthors: Luís Guilherme F. de Abreu¹²#, Nicholas Vinicius Silva¹#, Francisco Martin Rivera¹, Adrielle Ayumi Vasconcelos¹², Pedro Henrique N. Ferreira¹, Piotr A. Mieczkowski², Maria Carolina B. Grassi¹³, Marcelo F. Carazzolle¹⁴, Gonçalo Amarante G. Pereira¹*, Lucas Miguel de Carvalho¹⁴.

Included Materials

1 - Pipeline RNASeq Analysis - Gene Expression Profiling in the Axillary Bud Outgrowth of Enery Cane and Sugarcane: A Comparative Transcriptome Analysis

This document details the bioinformatics workflow applied to the RNA-Seq data. The pipeline includes:







Data summary

Quality control and filtering: Raw sequencing reads were analyzed using FastQC and MultiQC to ensure data quality. Reads were trimmed and filtered with Trimmomatic, and ribosomal RNA contamination was removed using SortMeRNA.

De novo transcriptome assembly: High-quality reads were assembled using Trinity to create transcriptomes for energy cane and sugarcane.

Preliminary gene annotation and classification: Basic steps for processing the assembled transcriptome are outlined for further analysis.




2 - Analysis Workflow (gene expression, differential expression, gene ontology, co-expression)

This document provides a step-by-step description of the downstream analyses, including:







Gene expression quantification: Using the assembled transcriptomes as references, kallisto was used for pseudo-alignment and quantification of gene expression for all samples.

Differential expression analysis: Temporal differences in gene expression were assessed with Sleuth, focusing on biologically significant genes (q-value ≤ 0.05).

Gene Ontology annotation and enrichment: Functional annotation of genes was performed using PANNZER2, followed by enrichment analysis with topGO.

Co-expression clustering: Genes with similar temporal expression profiles were grouped using Clust.




3 - sleuth_EC.RScript for temporal differential expression analysis in energy cane using Sleuth. This script processes gene expression data quantified by kallisto, fits statistical models to compare conditions across time points (0h, 24h, 48h), and identifies differentially expressed genes. It includes the generation of visualizations, such as PCA and QQ plots, and exports significant results.

4 - sleuth_SC.RSimilar to sleuth_EC.R, but focused on sugarcane. It analyzes temporal differential expression data, fits models to identify differentially expressed genes across experimental time points, and exports results along with relevant visualizations.

5 - topGO_EC.RScript for gene ontology (GO) enrichment analysis in energy cane. It uses functional annotation outputs to identify significantly enriched biological processes, molecular functions, or cellular components among the differentially expressed genes.

6 - topGO_SC.RSimilar to topGO_EC.R, but applied to sugarcane. It identifies enriched GO categories among differentially expressed genes, providing insights into significant biological changes observed at different experimental time points.

Usage and Accessibility

The resources provided here are aimed at facilitating research on plant transcriptomics, particularly in grasses and bioenergy crops. The pipeline can be adapted for studies on other species or experimental designs. All steps and software commands are detailed, ensuring reproducibility and transparency.

Acknowledgments

We encourage users to cite this dataset and acknowledge the authors when utilizing these materials in publications or presentations.