OmicsBox Transcriptomics Module

The OmicsBox Transcriptomics Module, developed by BioBam, is a comprehensive RNA-seq analysis platform that enables researchers to process data from raw reads through to functional analysis in a flexible and intuitive environment. Designed for life scientists working with both short- and long-read sequencing technologies, the module integrates a wide range of established bioinformatics tools into a single, accessible interface that runs on standard Windows, Linux, and Mac hardware.

The module supports end-to-end transcriptomics workflows, covering quality control, de novo assembly, genome alignment, expression quantification, differential expression analysis, long-read transcriptomics, single-cell RNA-seq, and enrichment analysis — all with rich visualisations and spreadsheet-style result exploration to support biological interpretation.

Quality Control

• Perform read quality control using FastQC to assess sample quality metrics
• Filter reads and remove low-quality bases with Trimmomatic
• Evaluate RNA-seq alignment files using BAM-QC and RSeQC modules
• Assess the quality of long-read datasets without a reference genome using LongQC

De Novo Assembly

• Assemble short reads into a de novo transcriptome without a reference genome using Trinity
• Assess transcriptome completeness with BUSCO
• Cluster similar sequences to reduce redundancy with CD-HIT
• Predict coding regions with TransDecoder
• Assess the coding potential of each sequence with CPAT
• Combine transcriptomes from multiple sources using TAMA Merge

RNA-Seq Alignment and Quantification

• Align RNA-seq reads to a reference genome using STAR (Spliced Transcripts Alignment to a Reference) regardless of hardware
• Perform alignment with BWA (Burrows-Wheeler Aligner) as an alternative short-read aligner
• Use Minimap2 as a long-read aligner
• Quantify expression at the gene level using HTSeq
• Quantify expression at the transcript level using RSEM, with or without a reference genome
• Batch rename feature IDs for count tables and differential expression results
• Rename and delete samples within count tables for streamlined data management

Differential Expression Analysis

• Perform pairwise differential expression analysis with edgeR (including edgeR v.4 for single-cell data)
• Conduct pairwise differential expression analysis without replicates using NOISeq
• Analyse time-course expression data with maSigPro
• Sort, filter, and adjust statistical criteria in a spreadsheet-like interface to review significant genes
• Combine differential expression results with functional annotations for enrichment analysis
• Export experimental design options for differential expression results

Long-Read Transcriptomics

• Identify long-read-sequenced transcripts using IsoSeq3, FLAIR v.2 (including quantification), or IsoQuant
• Perform long-read transcriptome analysis and characterisation with SQANTI3 v.5 to obtain a curated transcriptome and detailed analysis report
• Define and quantify isoforms with IsoQuant
• Build a reference-free long-read transcriptome using the isON pipeline
• Access a redesigned PacBio IsoSeq pipeline for improved long-read processing

Single-Cell RNA-Seq Analysis

• Obtain scRNA-seq counts for different library preparation technologies using STARsolo
• Perform single-cell clustering with Seurat v.5 to identify cell groups and examine marker gene expression
• Infer cell lineage trajectories and visualise them in pseudo-time with Monocle3
• Perform autocorrelation analysis via Monocle3
• Identify cell types automatically with SingleR
• Visualise results with gene trend charts and expression UMAP plots, with improved UMAP performance for large datasets

Supported Workflows

1. De Novo Transcriptome Characterisation: Assemble RNA-seq reads without a reference, assess completeness, cluster sequences, predict coding regions, and find homologous sequences to characterise transcripts
2. Gene-Level Analysis: Align RNA-seq reads to a reference genome, estimate per-gene expression values, and perform differential expression analysis
3. Transcript-Level Analysis: Map reads to a transcriptome (including de novo assembled transcriptomes), estimate transcript-level expression, and identify significant transcripts through differential expression analysis

Visualisations and Result Exploration

• Interactive heatmaps for intuitive comparison of expression values across genes and samples
• Statistical charts providing additional information about assembly, quantification, and quality assessment
• Detailed reports, charts, and tables for in-depth understanding of data
• Spreadsheet-style interface for sorting, filtering, and combining differential expression results with functional information

OmicsBox works out of the box on any standard PC or laptop running Windows, Linux, or macOS, requiring no specialist hardware. The Transcriptomics Module integrates seamlessly with other OmicsBox modules, enabling functional annotation and enrichment analysis to be combined directly with expression results for comprehensive biological insights.