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.
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RNA-Seq de novo assembly with Trinity
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Completeness Assessment with BUSCO
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Clustering with CD-HIT
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Predict Coding Regions with TransDecoder
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RNA-Seq alignment with STAR
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RNA-Seq alignment with BWA
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BAM file quality control with RSeqC
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Gene-level expression quantification with HTSeq
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Transcript-level expression quantification with RSEM
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Pairwise differential expression analysis with edgeR
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Pairwise differential expression analysis without replicates with NOISeq
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Time course expression analysis with maSigPro
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Long-read transcript identification with IsoSeq3
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Characterization and long-read transcriptome analysis with SQANTI3
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Single cell RNA-Seq trajectory inference with Monocle3
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Single cell RNA-Seq clustering with Seurat
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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.
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.
