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, and predict coding regions with TransDecoder.
RNA-Seq Alignment
Align RNA-seq data to your reference genome making use of STAR, an ultrafast universal RNA-seq 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
- Predict Coding Regions with TransDecoder
- RNA-Seq alignment with STAR
- 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
Statistics
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.
Workflows
De-Novo Transcriptome Analysis
Generate your own reference transcriptome by assembling RNA-seq reads, assess the completeness of the assembly and find coding regions, estimate the expression value of each transcript sequence and perform differential expression analysis.
Reference-Based Transcriptome Analysis
Align RNA-Seq reads against the reference genome, estimate the expression value of each gene and perform differential expression analysis.
