Bioinformatics in Genomics

Introduction to Genomics

Genome refers to the complete DNA sequence in a set of chromosomes of an organism. In the early 1990s, the "Human Genome Project" was officially launched, opening a new journey for humans to understand their own genetic code at the molecular level. In 2001, a detailed map of the human genome and its preliminary analysis results were released, revealing the true face of the human genetic code composed of four ATCG symbols. The mining of genomic information through bioinformatics methods is extremely important for identifying genetic diseases, characterizing mutations that drive cancer progression, and tracking disease outbreaks. The rapid decline in sequencing costs and the development of bioinformatics technology have led to the emergence of a series of technologies related to human genome research, such as genetic testing, genetic diagnosis, gene therapy, targeted drugs and other new medical methods. Biomedicine has also entered the era of precision medicine based on genomic big data.

Fig 1. Genetic diagnoses associated with broad phenotype categories. (Caroline F W,et al. 2014)

Significance of Genomics Data Analysis

1. Provide high-resolution genomic base view: Genomic data can provide a comprehensive view of the entire genome, such as detecting single-nucleotide site variations, insertions/deletions, copy number changes, and large structural variations. It is ideal for exploratory applications such as the identification of pathogenic variants and assembly of new genomes.

2. Mining large or small variations that may be missed by other omics data.

3. To further study gene expression and regulation mechanisms to identify potential pathogenic variants.

4. For samples without reference genomes, new genome assembly is supported.

Genomics Data Analysis Process

Fig 2. Schematic diagram of genomics data analysis process disease research.

Applications of Genomics Data in Biomedical Research

• Cancer research: Genomics data analysis of tumor samples can provide a comprehensive understanding of unique mutations in cancer tissues and provide information for the analysis of oncogenes, tumor suppressor genes, and other risk factors.
• Pathogen research: Analyzing the genome data of pathogenic microorganisms is very important for generating accurate reference genomes, identifying microorganisms, and studying the mechanism of pathogenicity of microorganisms.
• Complex disease research: By analyzing the genomic data of normal samples and diseased samples, mining-related mutation information, finding genetic mutations related to the occurrence and development of complex diseases, and discovering disease mechanisms.
• Rare disease variant discovery: The analysis of genome data can help scientists identify pathogenic genetic variants related to rare diseases.
• Personalized diagnosis and treatment: By understanding the occurrence and development of related diseases from the genetic level, it will bring new technologies and new methods for the diagnosis and treatment of diseases, such as genetic diagnosis, gene therapy, and targeted drugs.

What We Offer

As one of the providers of genomics data analysis, CD Genomics offers established, cost-efficient and rapid turnaround analysis services for genome data analysis. The raw input genome 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. For data analysis, we will provide you with the following services:

1. Researchers 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.
2. We will select the appropriate analysis software or model based on the data and generate high-quality results and charts. 
3. 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. Caroline F W, et al. Genetic diagnosis of developmental disorders in the DDD study: a scalable analysis of genome-wide research data[J]. The Lancet, 2014, 385(9975).

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