De Novo Transcriptome Sequencing

RNA sequencing is widely used to provide surprising details about an organism's transcriptional landscape. Our de novo transcriptome sequencing service can help you resolve changes in transcript levels in non-model organisms or species without reference genomes. Combining next-generation sequencing (NGS) and long read sequencing platforms, we provide advanced analysis of transcript splicing, variant detection, and functional analysis of gene expression levels to maximize research content even though no reference genome is currently available.

Overview

Reference-free (RF) de novo transcriptome analysis approaches allow direct assembly of sequenced reads into transcripts by using highly redundant and overlapping reads, without the use of a reference genome. De novo transcriptome sequencing is important for revealing gene regulatory mechanisms and uncovering genotypic and phenotypic variation for most non-model organisms that lack a complete reference genome and high-quality annotation of genetic information.

Based on the wide availability and affordability of NGS technologies for reference-free transcriptome sequencing, we reconstruct and quantify the entire transcriptome using a high-quality approved splicing and analysis platform. For identifying specific gene products or characterizing transcript isoforms, our long read sequencing platform can capture full-length transcript sequences across coding regions and structurally complex regions, reducing the difficulty of sequence assembly. Using higher read length counts and short read lengths for error correction, higher precision and accuracy can be achieved to generate high-quality de novo full-length transcriptomes. This strategy is applicable to any species and tissue samples. RF de novo transcriptome sequencing is not limited to species and genomic data, which is suitable for species that are difficult to obtain reference genomes, and can be used to interpret phenotypes and study mechanisms of action.

Features

Any Species Transcriptome-Wide Bioinformatics Analysis One-Stop Solution
This method can be applied to any species, from prokaryotes to eukaryotes. Profile all coding RNA regions and non-coding RNA, either known or unknown. Our integrated bioinformatics pipeline can be tailored to suit your project. One-stop solution from sample QC, library construction, to sequencing and data analysis.

Project Workflow

Sample Preparation

1. Sample Preparation

Quality assessment and quantification; ribosomal RNA depletion and fragmentation

Library Preparation

2. Library Preparation

cDNA library preparation

Sequencing

3. Sequencing

Illumina Nextseq/HiSeq; Circular consensus sequencing (CCS); continuous long read (CLR) sequencing.

Data Analysis

4. Data Analysis

Visualize and preprocess results, and perform custom bioinformatics analysis.

Bioinformatics Analysis Pipeline

De novo Transcriptome Sequencing
 

In-depth data analysis:

  • De novo transcripts assembly
  • CDS prediction
  • Functional annotation
  • Functional enrichment analysis
  • KEGG-pathway analysis
  • Gene ontology analysis
  • SNP Analysis
  • SSR identification
  • Differential expression analysis of transcripts
  • Alternative splicing analysis

Sample Requirements

RNA sample (concentration ≥ 200 ng/uL, quantity ≥ 1 ug)

1.8 ≤ OD260/280 ≤ 2.2, OD260/230≥2.0, RIN ≥ 6.5, 28S:18S≥1.0.

Please make sure that the RNA is not degraded.

Sample Storage: RNA can be dissolved in ethanol or RNA-free ultra-pure water and stored at -80°C. RNA should avoid repeated freezing and thawing.

Shipping Method: When shipping RNA samples, the RNA sample is stored in a 1.5 mL Eppendorf tube, sealed with a sealing film. Shipments are generally recommended to contain 5-10 pounds of dry ice per 24 hours.

Deliverable: FastQ, BAM, coverage summary, QC report, custom bioinformatics analysis.

References:

  1. Wang L, Zhu P, Mo Q, et al. Comprehensive analysis of full-length transcriptomes of Schizothorax prenanti by single-molecule long-read sequencing. Genomics, 2022, 114(1): 456-464.
  2. Lee SG, Na D, Park C. Comparability of reference-based and reference-free transcriptome analysis approaches at the gene expression level. BMC bioinformatics, 2021, 22(11): 1-9.
* For Research Use Only. Not for use in diagnostic procedures.


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