---
title: "Module Transcriptomics"
canonical: "https://help.biobam.com/space/OUM3/3675855529/Module%20Transcriptomics"
format: markdown
---
The OmicsBox Transcriptomics module allows you to process RNA-seq data from raw reads down to their functional analysis in a flexible and intuitive way. Quality Control:  Use FastQC and Trimmomatic to conduct quality control on your samples, filter reads, and eliminate low-quality bases. De-Novo Assembly:  Assemble brief reads with Trinity to obtain a de novo transcriptome without a reference genome. Evaluate the completeness of the transcriptome using BUSCO and cluster similar sequences with CD-HIT. Additionally, you can predict coding regions with TransDecoder or assess the coding potential of each sequence using CPAT. RNA-Seq Alignment:  Align your RNA-seq data to the reference genome using STAR (Spliced Transcripts Alignment to a Reference) or BWA (Burrows-Wheeler Aligner), irrespective of your hardware. Furthermore, BAM-QC offers several useful modules for assessing RNA-seq alignment files. Quantify Expression:  Quantify expression at gene or transcript level through HTSeq or RSEM and with or without a reference genome. Differential Expression Analysis:  Detect differentially expressed genes between experimental conditions or over time with well-known and versatile statistical packages like NOISeq, edgeR, or maSigPro. Rich visualizations help to interpret results. Long-Read Analysis:  Use LongQC to evaluate the quality of long-read datasets in the absence of a reference genome. First, identify transcripts sequenced with long reads using IsoSeq3, FLAIR, or IsoQuant. Subsequently, conduct an in-depth analysis and characterization of the long-read transcriptome using SQANTI3. This process will result in a refined transcriptome along with a comprehensive analysis report. Single-Cell RNA-Seq:  Obtain scRNA-Seq counts seamlessly with STARsolo for different library-prep technologies. Perform Single-Cell RNA-Seq clustering with Seurat to identify groups of cells and examine marker genes’ expression. Gain insight into cell transitions with Monocle3 and visualize cell lineage trajectories in pseudo-time. Enrichment Analysis: By integrating the findings of differential expression with functional annotations, enrichment analysis enables the identification of both overrepresented and underrepresented biological functions. Transcriptomics Module Menu  Additional Resources Transcriptomic Analysis use case:   . Transcriptomic Example Dataset:  Download .