Transcriptome analysis is currently a widely used high-throughput sequencing analysis technique. CD BioSciences's plant transcriptome bioinformatics analysis platform helps clients identify differentially expressed genes, marker genes, co-changing genes, differential splicing, or new transcripts, and performs result visualization, functional annotation, and network analysis.

RNA-seq introduces transcriptome analysis into a new era, and it is an important technology for gene prediction and functional mining. CD BioSciences conducts in-depth investigations into the main steps of RNA-seq analysis, evaluates the accuracy, efficiency, and consistency of analysis tool combinations, and proposes a comprehensive plant transcriptome analysis platform. Our platforms can help researchers better understand gene function and regulatory processes, to improve breeding selection and cultivation practices.

Plant Transcriptome /RNA-Seq Bioinformatics Analysis Platform

Sequencing data quality assessment

We use fastQC software to analyze the quality of the obtained fastq sequence files and generate the result report in html format.

Filter low-quality sequences

Trimmomatic software is used to remove the adapter from the sequence file and make appropriate modifications to the bases, then the bases are pruned and the low-quality sequences are filtered.

Transcript detection

We compared reads back to the reference genome (or reference transcriptome) and then assembled transcripts based on the reads comparison results.

Comparison and binding point prediction

We spliced RNA-seq reads to the reference genome and predicted the binding sites.

Transcriptome assembly

After concatenation and comparison, we identify the set of expressed transcripts by transcriptome assembly.

Transcript quantification

We assigned reads to transcripts, providing Sailfish, Salmon, quasi-mapping, and Kallisto tools to identify the read generated for the transcript.

Differential expression analysis

Identification of differentially expressed genes and conditions is an important goal of RNA-seq analysis. We offer a variety of methods to detect differentially expressed genes, including DESeq, limma, and edgeR based on counting techniques, Cuffdif and Ballgown based on assembly techniques, and sleuth based on quantitative difference analysis without comparison.

Analysis of RNA-seq variation

In addition to expression level information, our platform can also use RNA-seq data to identify important genomic and transcriptome variants.

RNA editing detection

RNA editing is a post-transcriptional modification of RNA that can affect the function and expression level of the sequence. We offer methods for detecting RNA editing events using RNA-SEQ data, most commonly identifying RNA variants that differ from the matching genome sequence in the DNA molecule.

RNA fusion detection

RNA-seq has proven to be an important source for detecting fusion genes. Our platform provides tools to identify fusions from transcriptome data, such as JAFFA57, STAR-Fusion, TopHat-Fusion, FusionCatcher, and SOAPfuse.

RNA-Seq simulator

Our In silico read generator is an effective tool for comparing and testing the efficacy of RNA-Seq data processing algorithms. In addition, RNA-Seq protocols can be analyzed and modeled.

Application Field

• Analysis of plant genetic diversity.
• Mining of plant functional genes.
• To explore the regulation of plant secondary metabolic network.
• Development of plant molecular markers.

CD BioSciences is dedicated to helping researchers unravel the complexity of plant gene expression through cutting-edge computing tools and algorithms. Our platform provides a complete set of bioinformatics tools tailored for plant transcriptome analysis, enabling researchers to accurately explore and interpret high-throughput transcriptome data. If you have any questions, please contact us.

For research use only, not for clinical use.