Decoding Breast Cancer Genomics: Navigating NGS Data
From data to discovery — decode cancer with precision using advanced multi-omics pipelines. Master high-throughput next-generation sequencing analysis to identify oncology variants and biomarkers.
Course Description
Welcome to an advanced training initiative designed to unlock the molecular complexities of oncology datasets. "Decoding Breast Cancer Genomics: Navigating NGS Data" is a free international webinar tailored to modern life science professionals. As shown in 20..png, this program emphasizes a clear pipeline: from data to discovery — decode cancer with precision. Participants will explore how artificial intelligence models and deep learning algorithms accelerate the clinical interpretation of genomic variants. Throughout this course, you will dive deep into processing next-generation sequencing (NGS) data, variant calling workflows, and oncogene identification. We will investigate mutations within specific exons, introns, and regulatory regions to understand tumor progression. Guided by live expert mentorship, you will learn to navigate complex biological databases and extract predictive biomarkers. By applying advanced bioinformatics pipelines, you will gain the specialized dry-lab capability required to contribute to modern precision oncology and personalized medicine.
What You'll Learn
Core computational biology workflows for analyzing raw Next-Generation Sequencing (NGS) datasets from breast cancer samples.
How to implement artificial intelligence frameworks for precise clinical variant interpretation and mutation filtering.
Practical methods to run sequence alignment tools, quality check reads, and execute somatic variant calling pipelines.
Detailed approaches to distinguish driver mutations from passenger variants in critical oncogenes like BRCA1 and BRCA2.
Strategies for translating computational genomic discoveries into actionable precision medicine frameworks and research.
Curriculum
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Introduction to breast cancer heterogeneity, clinical oncology benchmarks, and modern genomics frameworks.
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High-throughput data collection, quality control protocols, and preprocessing of raw fastq NGS files.
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Reference genome mapping, sequence alignment tools, and processing BAM/SAM files.
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Somatic and germline variant calling pipelines using computational intelligence filters.
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Functional annotation of variants, identifying mutations in exons/introns, and utilizing cosmic databases.
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Utilizing machine learning for biomarker discovery and clinical oncology report interpretation.
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