BIOL72241 15 Semester 1 Postgraduate Taught Level 7 Andrew Devereau Unit coordinator 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 This module will develop trainees’ familiarity with data types encountered in genomic laboratories, including: how to hold and statistically evaluate data, use programmatic methods to examine, interrogate and draw conclusions from data and how to communicate the insight derived from the data to support clinical decision making.A large proportion of this module is well suited for a major project that touches many of the competencies detailed. An example project is developing and validating a bioinformatics tool or pipeline and then bringing it into service. 1. Demonstrate the application of SQL, R and a high-level programming language to perform data analyses.2. Apply integrative knowledge of fundamental statistical concepts.3. Critically evaluate and select appropriate statistical tests for genomic datasets.4. Design, build, populate and query genomics databases.5. Critically evaluate, select and apply effective data visualisation methods suitable for genomics datasets. 1. Critically analyse scientific and clinical data 2. Present scientific and clinical data appropriately 3. Formulate a critical argument 4. Evaluate scientific and clinical literature and methods 5. Apply the knowledge of clinical bioinformatics to address specific clinical problems 1. Present information clearly in the form of written reports.2. Communicate complex ideas and arguments in a clear and concise and effective manner. 3. Work effectively as an individual and part of a team. 4. Use relevant literature and electronic resources to collect, select and organise complex scientific information 5. Design a scientifically valid strategy to address a specific research question relevant to modern clinical science practice. 6. Critically evaluate technologies and formulate appropriate analytical strategies to determine diagnosis and optimal clinical management for genomic disorders.7. Select and apply appropriate bioinformatic tools and resources from a core subset to typical diagnostic laboratory cases, contextualised to the scope and practice of a clinical genetics laboratory. 8. Compare major bioinformatics resources for clinical diagnostics, and how their results can be summarised and integrated with other lines of evidence to produce clinically valid reports. 1. Present complex ideas in simple terms in written formats. 2. Actively seek accurate and validated information from all available sources. 3. Interpret data and convert into knowledge for use in the clinical context of individual and groups of patients. 4. Work in partnership with colleagues to analyse problems and develop solutions. 5. Use logical and systematic approaches to problem-solving and decision-making. Databases Designing and using relational databasesCommon RDMS, including: MySQL/MariaDB and PostGresStructured query language (SQL) commandsDatabase programmatic accessData analysisPython3 and data analysis packages such as numpy and pandasR for data analysis, R Studio and tidyverseStatisticsCommon statistical concepts in genomics and bioinformaticsNormal distribution, standard deviation and standard error of the meanSample size and power calculationsOdd ratios and effect sizesLinear and logistic regressionCorrect selection of statistical testsData visualisationPlotting data, ggplot and matplotlibMachine learningMachine learning principlesCritical evaluation of machine learning applications BIOL64560 Introduction to Healthcare Science, Professional Practice and Clinical Leadership Pre-Requisite Compulsory BIOL63440 Introduction to Genomics and Clinical Practice (Clinical Bioinformatics Genomics) Pre-Requisite Compulsory BIOL65650 Clinical Bioinformatics Genomics Year 2 Pre-Requisite Compulsory Lectures 35 115