Bioinformatics in Transcriptomics

Introduction to Transcriptomics

With the rapid development of next-generation sequencing technology, its high-throughput, fast, and low-cost characteristics have become the first choice for more and more biological researchers when solving biological problems. Transcriptome sequencing (RNA-Seq), as a new efficient and fast transcriptome research method, is changing people’s understanding of the transcriptome. Transcriptome refers to the sum of all gene transcription products of a specific organism in a certain state, and it is an important part of functional genomics research. RNA-Seq uses high-throughput sequencing technology to sequence all RNA in tissues or cells into a cDNA library by reverse transcription, and calculate the expression of different RNAs by counting the number of relevant reads, and discover new transcripts. If there is a genome reference sequence, you can map the transcript back to the genome to determine more comprehensive genetic information such as the location of the transcript and the cutting situation. Transcriptome sequencing analysis has been widely used in biological research, medical research, clinical research, and drug development.

Fig 1. Isoform expression of cell surface genes in CD133+ and CD133− subpopulations. (Christian L B, et al. 2013)

Significance of RNA-seq Data Analysis

RNA-seq technology can detect the overall transcription activity of a specific species at the mononucleotide level, so as to obtain almost all transcript information of the species in a certain state in a comprehensive and rapid manner. Through the analysis of transcriptome data, the following information can be mined:

1. Detect new transcripts, including unknown transcripts and rare transcripts.

2. Research on the gene transcription level, such as gene expression amount and differential expression among different samples.

3. Research on the function of non-coding regions, such as microRNA, long non-coding RNA (IncRNA), RNA editing.

4. Research on the structural variation of transcripts, such as alternative splicing and gene fusion.

5. Develop SNPs and SSR, etc. 

RNA-seq Data Analysis Process

Fig 2. RNA-seq data analysis pipeline.

Applications of RNA-seq Data Analysis in the Field of Biomedicine

Transcriptome research is the basis and starting point of gene function and structure research, and has been widely used in various fields of biomedicine, such as:

1. Research on the development of cancer and other complex diseases: During the occurrence and development of cancer and other complex diseases, the gene expression patterns in cells will change significantly. RNA-seq can be analyzed by comparing genes whose expression patterns have changed significantly in normal samples and disease samples, and their functions. It can quickly grasp the changes in gene expression patterns in the occurrence of cancer or other diseases, conduct research on the mechanism of disease occurrence, identify possible biomarkers, and provide important solutions for the diagnosis and treatment of the disease.

2. Bacteria and virus research: Analyze the differentially expressed genes and their functions in normal samples and infected samples through RNA-seq data. It can quickly and comprehensively grasp the characteristics of changes in cell gene expression patterns during a certain virus or bacterial infection process, and ultimately provide an important solution strategy for effectively resisting pathogen infection.

What We Offer

As one of the providers of transcriptomics data analysis, CD Genomics offers established, cost-efficient and rapid turnaround analysis services for transcriptome analysis. The raw input transcriptome data can be produced from a range of platforms. In addition, we are able to receive various formats of data for analysis such as raw FastQ / Fasta files, or aligned BAM/SAM files and other intermediate data formats. Regarding data analysis, you only need to provide raw data and inform us of your analysis needs, and we will provide you with a one-stop data analysis service. We will select the appropriate analysis software or model based on the data and generate high-quality results and charts. It is also worth mentioning that we provide customers with personalized analysis services. If you have any questions about the data analysis content, turnaround time and price, please feel free to contact us. We look forward to working with you, and we will provide you with satisfactory services.

Our workflow

Reference

1. Christian L B, et al. Transcriptome sequencing of tumor subpopulations reveals a spectrum of therapeutic options for squamous cell lung cancer[J]. PLoS One. 2013;8(3): e58714.

* For research use only. Not for use in clinical diagnosis or treatment of humans or animals.