Automated Genomic Variant Interpretation with AI Integration

Automated Genomic Variant Interpretation Pipeline

1. Data Acquisition

1.1 Sample Collection

Collect genomic samples from patients or research subjects using standardized protocols.

1.2 Sequencing

Utilize next-generation sequencing (NGS) technologies such as Illumina or Thermo Fisher platforms to obtain raw genomic data.

2. Data Preprocessing

2.1 Quality Control

Implement tools like FastQC to assess the quality of the sequencing data and filter out low-quality reads.

2.2 Alignment

Use AI-driven alignment tools such as BWA or Bowtie2 to align the sequenced reads to a reference genome.

3. Variant Calling

3.1 Variant Identification

Employ variant calling software like GATK or FreeBayes to identify genomic variants from the aligned data.

3.2 AI Integration

Incorporate AI algorithms, such as DeepVariant, to enhance the accuracy of variant calling by leveraging deep learning techniques.

4. Annotation and Interpretation

4.1 Functional Annotation

Utilize tools like ANNOVAR or VEP (Variant Effect Predictor) to annotate variants with functional information.

4.2 AI-Driven Interpretation

Implement AI-based platforms such as VarSome or Sophia Genetics that utilize machine learning to provide clinical interpretations of variants.

5. Reporting

5.1 Automated Report Generation

Generate comprehensive reports using tools like Bioinformatics Report Generator, which can automatically compile variant annotations and interpretations.

5.2 Review and Verification

Facilitate a review process by incorporating AI systems that flag uncertain interpretations for expert analysis.

6. Feedback Loop

6.1 Continuous Learning

Incorporate feedback mechanisms where clinical outcomes are analyzed to refine AI models, enhancing future variant interpretations.

6.2 Data Integration

Integrate new genomic data and clinical findings into the AI systems to improve accuracy and predictive capabilities over time.