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 and cluster similar sequences with CD-HIT. Moreover, you are able to predict coding regions with TransDecoder or assess the coding potential of each sequence with CPAT.
Align RNA-seq data to your reference genome making use of STAR (Spliced Transcripts Alignment to a Reference) or BWA (Burrows-Wheeler Aligner) regardless of your hardware. In addition, BAM-QC provides several useful modules to evaluate RNA-seq alignment files.
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.
Use LongQC to asses the quality of long-read datasets without a reference genome.
Identify long-read-sequenced transcripts with IsoSeq3 and then perform a long-read transcriptome analysis and characterization using SQANTI3. With this implementation, you will obtain a curated transcriptome including a detailed analysis report.
Perform Single-Cell RNA-Seq clustering with Seurat to identify groups of cells. Gain insight of cell transitions with Monocle3 which allows to visualize cell lineage trajectories in pseudo-time.
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
BAM file quality control with RSeqC
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
Long-read transcript identification with IsoSeq3
Characterization and long-read transcriptome analysis with SQANTI3
Single cell RNA-Seq trajectory inference with Monocle3
Single cell RNA-Seq clustering with Seurat
Coding Potential Assessing with CPAT
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.