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Transcriptomics
Quality Control, Assembly, Quantification and Differential Expression

Transcriptomics Module

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 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.

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 of your hardware. In addition, BAM-QC provides several useful modules to evaluate 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 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.

Single Cell RNA-Seq

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.

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

  • 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

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.

Detailed Information

Different reports, charts, and tables allow to obtain an in-depth understanding of what is happening with your data.

Benchmark

Assembly of A. galli

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

Workflows

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.