Quality Control, Assembly, Quantification and Differential Expression

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 perform the quality control of your samples, to filter reads and to remove low quality bases.

De-Novo Assembly

Assemble short reads with Trinity to obtain a de-novo transcriptome without a reference genome. Assess the completeness of the transcriptome with BUSCO, cluster similar sequences with CD-HIT, and predict coding regions with TransDecoder

RNA-Seq Alignment

Align RNA-seq data to your reference genome making use of STAR (Spliced Transcripts Alignment to a Reference) or BWA (Burrows-Wheeler Aligner). Regardless or your hardware in the OmicsBox Cloud.

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.

Enrichment Analysis

By combining differential expression results with functional annotations, enrichment analysis allows to identify over- and underrepresented biological functions.

  • RNA-Seq de novo assembly with Trinity
  • Completeness Assessment with BUSCO
  • Clustering with CD-HIT
  • Predict Coding Regions with TransDecoder
  • RNA-Seq alignment with STAR
  • RNA-Seq alignment with BWA
  • Gene-level expression quantification with HTSeq
  • Transcript-level expression quantification with RSEM
  • Pairwise differential expression analysis with edgeR
  • Pairwise differential expression analysis without replicates with NOISeq
  • Time course expression analysis with maSigPro


Different statistical charts provide additional information about the assembly and quantification processes as well as a quality assessment of the results.

Rich Visualizations

Interactive heatmaps help to intuitively check the differences and similarities between the expression values of the different genes and samples.

Spreadsheet Alike

Sort and filter the differential expression results and adjust the statistical criteria to review significant genes and combine them with functional information to gain biological insights.


Assembly of A. galli

1 M
Input Reads
1 %
high Quality transcripts (BUSCO)
1 h


De-Novo Transcriptome Characterization

Generate your own reference transcriptome by de novo assembling RNA-seq reads. Assess the completeness of the assembly, cluster similar sequences to reduce redundancy. Finally, predict coding regions and find homologous sequences to characterize transcript sequences. 

Gene-level Analysis

When reference is available, perform a gene-level analysis by aligning RNA-Seq reads against the reference genome. Then estimate the expression value of each gene and perform differential expression analysis.

Transcript-level Analysis

Estimate expression at the transcript-level by mapping reads to the transcriptome (such as a de novo assembled transcriptome), and perform differential expression analysis to identify significant transcripts.