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Guided learning journeys that build knowledge step by step.
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7817 Paths · page 584 / 782
This advanced learning path guides genomics researchers through the process of detecting large structural variants using long-read sequencing data. It covers the necessary foundations in sequencing technologies and alignment, progresses through specialized SV calling algorithms and assembly-based approaches, and includes practical validation strategies. The path emphasizes the unique advantages of PacBio and Oxford Nanopore data for resolving complex genomic rearrangements.
This learning path equips computational biologists with the skills to leverage cloud platforms (AWS, GCP, Azure) for scalable genomic data analysis. It covers cloud fundamentals, storage and batch processing, workflow managers like Nextflow, and best practices for cost and security. The path progresses from basic programming and cloud concepts to advanced workflow deployment and optimization.
This advanced graduate-level learning path equips medical bioinformatics students with the skills to identify and prioritize causative variants from genomic data. Starting from foundational NGS concepts, it covers variant calling for SNVs/indels, CNVs, and structural variants, followed by annotation and filtering strategies to rank candidate variants. The path emphasizes practical application in a clinical or research context.
This path equips genome assembly specialists with advanced strategies to obtain complete, chromosome-level genome sequences. It covers hierarchical and hybrid assembly approaches, gap closing, polishing, and rigorous validation, building on foundational assembly concepts.
This learning path equips genetic epidemiology students with the skills to analyze and interpret genome-wide association study (GWAS) data. It covers essential concepts in population genetics, bioinformatics, and statistical genetics, progressing through quality control, imputation, association testing, meta-analysis, and fine mapping.
This learning path guides epigenomics researchers through the computational analysis of ChIP-seq and related epigenetic data. It covers essential NGS concepts, ChIP-seq specific processing, peak calling, motif analysis, differential binding, chromatin state annotation, and integration with gene expression. The path is designed for graduate-level learners and emphasizes practical bioinformatics skills.
This advanced graduate-level path equips learners with the skills to analyze transcriptomic data, covering NGS basics, alignment, quantification, differential expression, gene set enrichment, and alternative splicing. It progresses from foundational concepts to advanced applications, ensuring a robust understanding of the entire analysis pipeline.
This learning path guides bioinformatics students through building and executing a complete Next-Generation Sequencing (NGS) data analysis pipeline, from raw FASTQ files to annotated variants. It covers quality control, alignment, post-alignment processing, variant calling, annotation, and reporting, emphasizing practical skills and conceptual understanding.
This learning path guides genomics lab students through the essential practical skills for genomic research, from basic molecular biology to DNA extraction, quantification, QC, library preparation, pooling, and automation. It emphasizes hands-on competence and understanding of the underlying principles, preparing learners for real-world genomics workflows.
This learning path explores the ethical, legal, and social implications of genomic technologies for university students. It begins with foundational genomics and bioethics, then examines privacy, discrimination, consent, biobanks, forensic databases, and CRISPR ethics, culminating in a synthesis of societal impacts.