Figure 1: Transcriptomics menu
Detecting genes that are differentially expressed between conditions is a fundamental part of understanding the molecular basis of phenotypic variation. To take advantage of the possibilities and address the challenges posed by this relatively new type of data, OmicsBox offers several tools to analyze RNA-Seq data and obtain functional insights.
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 sequencing samples, to filter reads and remove low-quality bases.
De novo Assembly: Assemble short reads with Trinity to create a de novo transcriptome without a reference genome.
Assembly Post-processing: Assess the completeness of the transcriptome with BUSCO, cluster similar sequences with CD-HIT, and predict coding regions with TransDecoder.
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).
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.
Transcriptomic Analysis use case: https://www.biobam.com/drug-response-transcriptomics/ .
Transcriptomic Example Dataset: Download.