AveGene, artificial intelligence (AI) implemented into NGS
At AveGene, we apply artificial intelligence to Next Generation Sequencing (NGS) processes.
Artificial intelligence (AI) and machine learning (ML) significantly improve the efficiency and accuracy of processes in Next Generation Sequencing (NGS), especially in the field of oncological diagnostics. The combination of NGS and AI allows for the processing of vast amounts of data, identification of patterns, and provision of valuable clinical insights.
Data processing and analysis Data processing and analysis
NGS generates large volumes of sequencing data. (tzv. big data). The implementation of AI in these steps includes:
• Filtering and cleaning data: AI tools automatically remove noise and errors in the data, thereby improving the quality of the analysis.
• Alignment: Machine learning algorithms optimize the alignment of sequenced fragments against the reference genome, speeding up the process and increasing accuracy.
• Mutation detection: AI is capable of detecting genetic mutations (somatic and germline variants), including rare variants that traditional methods might overlook.
Example: AI tools like DeepVariant from Google use deep learning for accurate identification of genetic variants.
Interpretation of genetic data
Identifying mutations is just the first step. The challenge is interpreting the results in the context of clinical data. AI can:
• Predict the clinical significance of variants: AI models analyze variants in terms of pathogenicity and clinical relevance (e.g., for personalized treatment).
• Categorize variants: Tools like ClinVar or AI algorithms classify variants as pathogenic, benign, or of unknown significance.
• Association with therapy: AI analyzes genetic changes and recommends appropriate targeted therapies based on databases (e.g. OncoKB, COSMIC).
Prediction and personalization of treatment
AI is applied in the personalization of treatment based on NGS data:
• Tumor response modeling: Algorithms predict how a tumor will respond to certain drugs based on its genetic profile.
• Monitoring treatment efficacy: AI analyzes data from liquid biopsy (ctDNA) and NGS to detect therapy resistance or minimal residual disease. (MRD).
Pattern detection and disease prediction
AI algorithms can analyze extensive sequencing databases and detect genetic signatures that are typical for various types of tumors. This enables:
• Early detection of tumors: AI identifies specific genetic changes in the early stages of the disease.
• Prognosis of disease progression: AI models can predict the risk of metastasis and overall prognosis based on genetic profiles.
Automation of processes
AI is used to automate the entire NGS workflow, from sample preparation to final result interpretation:
• Robots controlled by algorithms perform pipetting, library preparation, and sequencing processes.
• AI-integrated software accelerates decision-making and eliminates human error.
Integration of multi-omic data Integration of multi-omic data
• For a comprehensive understanding of tumor biology, AI integrates data from various "omics" technologies. (gnomic, transcriptomic, proteomic).
• This provides a comprehensive picture of changes in tumor cells and increases the accuracy of diagnosis and therapy personalization.
The implementation of AI in Next Generation Sequencing significantly increases the speed, accuracy, and efficiency of the sequencing process. AI allows doctors to gain deeper insights into the genetic background of tumors, interpret results more accurately, and tailor treatment to the individual needs of the patient. With the continued development of AI, further improvements in the diagnosis, monitoring, and personalization of cancer treatment are expected.