Whole Genome Sequencing (WGS) Bioinformatics Analysis
High-throughput next-generation sequencing (NGS) technologies have reshaped the scope of genomics research in many diseases, especially in cancers. Based on our high-throughput SuPrecision™ platform, Creative Biolabs has accumulated extensive experience in whole genome sequencing (WGS) (WGS) and we have explored a powerful pipeline for large-scale WGS data analysis. We are pleased to tailor the most appropriate strategies to meet every unique need of our global clients.
High-Accuracy Bioinformatics Tools is Needed for WGS Data Analysis
Patients with a family history of cancer are being evaluated with single-gene or gene panel tests. Recent explosive advances in NGS technology and computational approaches to massive data enable us to analyze a number of cancer genome profiles by WGS. The decreasing cost and potential to provide comprehensive genetic risk assessment make WGS an attractive tool for understanding the genetic risk for cancer. Technical improvements have decreased sequencing costs while the size and number of genomic datasets have increased rapidly. The growing clinical use of WGS will require large-scale efforts to consolidate WGS results with clinical data to improve the accuracy of interpretation of rare variants and to determine optimum management strategies. The expanding volume of data has enabled the rapid adoption of WGS to enhance drug research and development. This leads to a significant increase in the need for computational methods and bioinformatics tools.
Fig.1 WGS by NGS can detect non-coding mutations, structural variants (SVs) including somatic copy number alterations (SCNAs) and translocations, and pathogen detection, as well as protein-coding mutations (Nakagawa et al. 2015).
WGS Bioinformatic Analysis at Creative Biolabs
Scientists from Creative Biolabs have developed a powerful pipeline for cancer WGS bioinformatic analysis with high accuracy. Our in-house pipeline has the capacity to process genomic data from large-scale subjects. Our strategy focuses on identifying genetic variations from WGS data. The genetic variants analysis pipeline consists of 1) quality control of raw data, 2) production of the clean data, 3) unspliced mapping of the clean data onto a reference genome, 4) post-alignment processing, 5) quality control of the mapped reads, 6) variant calling, annotation and prioritization.
Key Features and Advantages of WGS Bioinformatic Analysis Service Include but Are Not Limited to:
- Large-scale WGS data analysis
- Rich experience in cancer WGS data analysis
- High accuracy and high-throughput
- Highly professional Ph.D. level scientists
- Fast turnover time
- Competitive and affordable price
With years of research and development experience in the field of deep sequencing, Creative Biolabs has accomplished over hundreds of WGS projects. Our seasoned scientists can provide the best service of large-scale WGS data analysis at the most competitive cost.
Please contact us for more information and a detailed quote.
Reference
- Nakagawa, H.; et al. Cancer whole-genome sequencing: present and future. Oncogene. 2015, 34(49):5943-50.
- Whole Exome Sequencing (WES) Bioinformatic Analysis
- Targeted Sequencing Bioinformatics Analysis
- Whole Transcriptome Sequencing (WTS) Bioinformatics Analysis
- SuPrecision™ Pipeline for Immune Repertoire Analysis
