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Guided learning journeys that build knowledge step by step.
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7817 Paths · page 586 / 782
This learning path equips agricultural and plant science students with the knowledge to apply genomics in plant breeding. It covers foundational genetics, genome sequencing, association mapping, genomic selection, stress tolerance, and GM crops, emphasizing practical applications in crop improvement.
This path provides an advanced, graduate-level understanding of mitochondrial genomes, covering their structure, replication, transcription, inheritance patterns, heteroplasmy, and the molecular basis of mitochondrial diseases. It also includes practical approaches to sequencing and analyzing mitochondrial genomes, connecting basic genetics to clinical and research applications.
This advanced learning path equips medical and pharmaceutical students with the knowledge to understand how genomic variation influences drug metabolism and response. It covers core pharmacogenomic concepts, key genes and pathways, clinical implementation, and dosing strategies, culminating in the application of pharmacogenomic data to predict adverse reactions and optimize therapy.
This advanced graduate-level path equips medical and genetics students with the knowledge and skills to apply genomics in clinical care. It covers foundational medical genetics, sequencing technologies, variant interpretation following ACMG guidelines, and pharmacogenomics, emphasizing practical application in diagnostics and treatment decisions.
This advanced graduate-level path explores the molecular forces and mechanisms that shape genome evolution, from comparative genomics foundations to gene duplication, functional divergence, whole-genome duplication, and ancestral reconstruction. Learners will develop a systems-level understanding of how genomes change over time and how to analyze these processes.
This learning path guides systems biology students through the process of integrating genomic data with transcriptomic, proteomic, and metabolomic datasets. It covers foundational omics technologies, data preprocessing, statistical integration methods, network analysis, and practical implementation using computational tools.
This advanced learning path covers the methods and applications of single-cell genomics, from fundamental concepts to advanced data analysis. It includes single-cell isolation, DNA-seq, RNA-seq, ATAC-seq, and computational methods for understanding cellular heterogeneity and trajectory inference.
This advanced graduate-level path provides a systematic understanding of large-scale genomic structural variations, including CNVs, inversions, translocations, and LOH, and equips learners with knowledge of detection methods such as array CGH and optical mapping. It builds from foundational genome analysis concepts through to advanced detection strategies and data interpretation.
This advanced learning path guides biomedical students from foundational genetics and cancer biology through the technologies and bioinformatics needed to analyze cancer genomes. It covers somatic mutations, copy number variations, tumor heterogeneity, and mutational signatures, culminating in their application in precision oncology.
A comprehensive learning path for population genetics students to master the use of genomic data in studying population genetics. It covers core concepts from basic population genetics to advanced analyses of nucleotide diversity, linkage disequilibrium, selection signatures, demography, and admixture, with practical applications.