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Module Transcriptomics

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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.

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image-20240430-143721.png Additional Resources