De novo & Reference Based RNASeq
Your Industry, Our Focus
Eurofins Genomics offers a full suite of RNA sequencing services, specializing in both de novo and reference-based RNA-Seq.
Our advanced sequencing solutions enable comprehensive transcriptome analysis for a wide range of organisms, including plants, animals, bacteria, and fungi. Whether you are working with model or non-model organisms, we provide customized experimental designs and detailed bioinformatics analysis to meet your research needs.
RNA-Seq: A Powerful Tool for Transcriptome Analysis
RNA sequencing (RNA-Seq) is a cutting-edge method for studying the entire set of RNA transcripts, including mRNA, rRNA, tRNA, and other non-coding RNAs. It offers unparalleled sensitivity for measuring gene expression by sequencing cDNA libraries derived from mRNA.
Unlike static genomes, transcriptomes are dynamic, allowing RNA-Seq to uncover rare genes, profile mRNA, analyse gene expression, identify splice junctions and gene fusions, and detect novel transcripts in both coding and non-coding RNA.
Whole transcriptome analysis through RNA-Seq facilitates the discovery of differentially expressed genes across various conditions, cell types, or treatments, providing valuable insights into gene regulation and function.
RNA-Seq Services
Eurofins Genomics provides a range of RNA-Seq services, tailored to suit different research needs:
- Bacterial/Fungal/Animal/Plant De Novo RNA-Seq
- Reference-Based RNA-Seq
Workflow and Deliverables
Quality Check of Raw Reads
We filter and trim raw reads, removing primer sequences, poly(A) tails, and ribosomal DNA reads, to ensure high-quality data for analysis.
De novo Assembly
We assemble high-quality reads into transcripts using optimized parameters. The assembly is evaluated based on transcriptome length, and length distribution. Non-redundant transcripts are clustered into unigenes.
Coding Sequence (CDS) Prediction
TransDecoder predicts coding sequences from unigenes, identifying candidate coding regions within unigene sequences.
Functional Annotation
Predicted CDS are annotated against databases such as NCBI non redundant protein database (Nr), Swissprot, Kyoto Encyclopaedia of Genes and Genomes(KEGG), Cluster of Orthologous Group(COG) databases using Basic Local Alignment Search Tool
KEGG Pathway Annotation and Enrichment analysis
CDS are mapped to KEGG pathways to identify their roles in metabolism, cellular processes, genetic information processing, and environmental information processing. The output of KEGG analysis includes KEGG Orthology (KO) assignments and Corresponding Enzyme Commission (EC) numbers and metabolic pathways of predicted CDS using KEGG automated annotation server KAAS.
Pathway enrichment analysis identifies significantly enriched metabolic pathways or signal transduction pathways associated with differentially expressed genes, comparing the whole genome background. The KEGG Enrichment analysis is carried out using ClusterProfiler.
Gene Ontology and Enrichment Analysis
OmicBox reveals the most abundant GO terms related to biological processes, molecular functions, and cellular components, assigning GO terms to CDS for functional categorization.
The GO Enrichment analysis is carried out using inhouse pipeline to identify the enriched GO terms.
Transcription Factor Analysis
Transcription factors, which regulate gene expression by binding to DNA promoter regions, are identified using homology-based approach.
Simple Sequence Repeat (SSR) Identification
SSRs, also known as microsatellites, are detected from assembled unigenes. These are essential molecular markers for genetics, epidemiology, pathology, and gene mapping.
Differential Gene Expression Analysis
Differential expression analysis uses FPKM / normalized expression values to compare transcript read numbers between experimental and control conditions, identifying significantly expressed transcripts with DESeq / DESeq2 / DEGSeq.
Deliverables
De novo-Based RNASeq
- Quality filtration of reads
- De novo assembly generating transcripts/unigenes
- Summary statistics
- CDS prediction
- Functional annotation using NCBI NRdb, SwissProt, COG
- KEGG pathway annotation and enrichment
- Gene ontology and enrichment
- SSR identification
- Differential gene expression analysis (if more than one sample)
- Heat maps, volcano plots, scatter plots
- Comprehensive report with publication-standard methodology, graphs, and tables
Reference-Based RNASeq
- Quality filtration of reads
- Mapping on the reference genome
- Alignment summary statistics
- Differential gene expression analysis based on RPKM / FPKM / normalized expression value
- Functional annotation of differentially expressed genes
- List of upregulated and downregulated genes
- Statistically significant genes
- Heat maps, volcano plots, scatter plots
- KEGG pathway annotation and enrichment
- Gene ontology and enrichment
- SNP analysis
- SSR identification
- Comprehensive report with publication-standard methodology, graphs, and tables
Frequently Answered Questions ( FAQ's )
What is the difference between De novo and Reference-Based RNASeq?
De novo RNASeqassembles transcripts without a reference genome, suitable for non-model organisms.
Reference-Based RNASeq maps transcripts to an existing genome, ideal for model organisms.
Why use RNA-Seq for gene expression analysis?
RNA-Seq is highly accurate and sensitive, allowing for the quantification of gene expression levels, discovery of novel transcripts, and detection of rare genes.
How does Eurofins Genomics ensure data quality in RNASeq?
We perform rigorous quality checks, including filtering, adapter trimming, and removal of ribosomal DNA reads, ensuring high-quality data for analysis.
What types of RNA can RNA-Seq Analyze?
RNA-Seq can analyze mRNA, miRNA, and other non-coding RNA types.
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