Please find below a range of online resources relating to the Genomics Education Programme.
Although we make every effort to ensure these links are accurate, up to date and relevant, the Genomics Education Programme cannot take responsibility for pages maintained by external providers. If you come across any external links that don't work, we would be grateful if you could report them to the web content team.
Please note that external links from this website may include material of a political nature. The Genomics Education Programme takes no responsibility for information contained on external links from this website. Views expressed on external sites are not necessarily those of HEE or the Genomics Education Programme itself.
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|Gen-Equip, Genetics Education for Primary Care|
We are focussing on the needs of professionals in primary care: general practitioners, primary care paediatricians, midwives, and primary care nurses. However, any health professional is welcome to use the training. Each module is based on a case and covers a different type of genetic condition you might see in primary care.
The modules can be taken free of charge. They are accredited by both the Royal College of General Practitioners and the European Accreditation Network and can be used for your CPD.
|MSc Specialist Practice (Cancer) NonSLA|
This flexible master's course in Specialist Practice (cancer pathway) enables healthcare professionals to develop an expert knowledge base, higher decision making skills and professional competencies to deliver care within the integrated multi-professional clinical teams that will form the basis of healthcare delivery going forward.
|Cancer and infectious diseases|
|Tales from the Genome|
This course is a journey into the biology of the human genome and will highlight the scientific, social, and personal perspectives of people living with a variety of traits.
|An Introduction to Clinical Genetics And The Dawn Of Genomic Medicine|
On this CPD / CME Webinar for GPs, first broadcast live on Tuesday 27th September, Dr Julian Barwell, Consultant in Clinical Genetics and Honorary Professor in the department of Cancer Studies at University of Leicester on the introduction to Clinical Genetics and the Dawn of Genomic Medicine.
|Data Analysis for Life Scientists 3: Statistical Inference and Modeling for High-throughput Experiments|
In this course you’ll learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis. We then introduce statistical modeling and how it is applied to high-throughput data. In particular, we will discuss parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation.
|Data Analysis for Life Scientists 2: Introduction to Linear Models and Matrix Algebra|
Matrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data. In this introductory data analysis course, we will use matrix algebra to represent the linear models that commonly used to model differences between experimental units. We perform statistical inference on these differences.
|Data Analysis for Life Scientists 7: Case Studies in Functional Genomics|
We will explain how to start with raw data, and perform the standard processing and normalization steps to get to the point where one can investigate relevant biological questions. Throughout the case studies, we will make use of exploratory plots to get a general overview of the shape of the data and the result of the experiment.
|Data Analysis for Life Scientists 6: High-performance Computing for Reproducible Genomics|
Learn how to bridge from diverse genomic assay and annotation structures to data analysis and research presentations via innovative approaches to computing.
|Data Analysis for Life Scientists 5: Introduction to Bioconductor: Annotation and Analysis of Genomes and Genomic Assays|
We begin with an introduction to the biology, explaining what we measure and why. Then we focus on the two main measurement technologies: next generation sequencing and microarrays. We then move on to describing how raw data and experimental information are imported into R and how we use Bioconductor classes to organize these data, whether generated locally, or harvested from public repositories or institutional archives.
|Data Analysis for Life Scientists 4: High-Dimensional Data Analysis|
If you’re interested in data analysis and interpretation, then this is the data science course for you. We start by learning the mathematical definition of distance and use this to motivate the use of the singular value decomposition (SVD) for dimension reduction and multi-dimensional scaling and its connection to principle component analysis.
|Data Analysis for Life Scientists 1: Statistics and R|
An introduction to basic statistical concepts and R programming skills necessary for analysing data in the life sciences.
|Bioinformatics Specialisation 6 Courses|
Join the educators on the frontier of bioinformatics to look for hidden messages in DNA without ever needing to put on a lab coat. After warming up your algorithmic muscles, we will learn how to apply popular bioinformatics software tools to real experimental datasets. This course is aimed at bioinformaticians working in genomics and precision medicine.
This pair of courses is useful to any student considering graduate school in the biological sciences, as well as students considering molecular medicine. Both provide an overview of the many different bioinformatic tools that are out there.
Machine learning is the science of getting computers to act without being explicitly programmed. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. The course would be suitable for bioinformaticians with an interest in genomic and precision medicine. Or, any member of the public with a mathematical or statistical background and is curious to learn about this fascinating field.
|Master Algorithmic Programming Techniques 5 Courses|