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