Module #1 Introduction to Personalized Medicine Overview of personalized medicine, its importance, and the role of genomics in personalized medicine.
Module #2 Genomic Data Generation Introduction to next-generation sequencing (NGS) technologies and their applications in personalized medicine.
Module #3 Genomic Data File Formats Introduction to common genomic data file formats such as FASTQ, BAM, and VCF.
Module #4 Quality Control and Preprocessing Introduction to quality control and preprocessing steps for genomic data, including trimming, filtering, and alignment.
Module #5 Genome Assembly and Annotation Introduction to genome assembly and annotation tools, including genome assembly algorithms and gene annotation pipelines.
Module #6 Variant Calling and Filtering Introduction to variant calling and filtering tools, including GATK, FreeBayes, and filtering strategies.
Module #7 Genomic Data Visualization Introduction to genomic data visualization tools, including IGV, UCSC Genome Browser, and Circos.
Module #8 Introduction to Bioinformatics Tools Introduction to commonly used bioinformatics tools, including BLAST, Bowtie, and BWA.
Module #9 RNA-Seq Data Analysis Introduction to RNA-Seq data analysis, including expression quantification, differential expression, and pathway analysis.
Module #10 ChIP-Seq Data Analysis Introduction to ChIP-Seq data analysis, including peak calling, motif analysis, and functional enrichment analysis.
Module #11 GWAS and Association Analysis Introduction to genome-wide association studies (GWAS) and association analysis, including SNP annotation and functional interpretation.
Module #12 Regulatory Element Analysis Introduction to regulatory element analysis, including transcription factor binding site analysis and chromatin state annotation.
Module #13 Cancer Genomics Introduction to cancer genomics, including somatic mutation analysis, copy number analysis, and cancer driver gene identification.
Module #14 Pharmacogenomics Introduction to pharmacogenomics, including pharmacogenetic variant annotation and personalized medicine applications.
Module #15 Genomic Data Integration and Interpretation Introduction to genomic data integration and interpretation, including data fusion and biological network analysis.
Module #16 Clinical Interpretation of Genomic Data Introduction to clinical interpretation of genomic data, including variant classification and reporting.
Module #17 Ethical, Legal, and Social Implications (ELSI) Introduction to ELSI in personalized medicine, including informed consent, data sharing, and genetic privacy.
Module #18 Computational Tools for Genomic Data Analysis Introduction to computational tools for genomic data analysis, including command-line interfaces, R, and Python.
Module #19 Cloud Computing for Genomic Data Analysis Introduction to cloud computing for genomic data analysis, including AWS, Google Cloud, and cloud-based workflows.
Module #20 Machine Learning and Artificial Intelligence in Genomics Introduction to machine learning and artificial intelligence in genomics, including supervised and unsupervised learning, and deep learning.
Module #21 Case Studies in Personalized Medicine Real-world case studies in personalized medicine, including cancer treatment, genetic disease diagnosis, and pharmacogenomics.
Module #22 Genomic Data Sharing and Collaboration Introduction to genomic data sharing and collaboration, including data repositories, sharing protocols, and international collaborations.
Module #23 Regulatory and Reimbursement Considerations Introduction to regulatory and reimbursement considerations in personalized medicine, including FDA regulations and insurance coverage.
Module #24 Future Directions in Personalized Medicine Future directions in personalized medicine, including emerging trends, challenges, and opportunities.
Module #25 Course Wrap-Up & Conclusion Planning next steps in Genomic Data Analysis for Personalized Medicine career
