Bioinformatics MSc/Diploma/Certificate

The modules on the programme are as follows (please find more details below):

• Essentials of Biology, Mathematics and Statistics (20 credits)
• Genomics & Next Generation Sequencing (20 credits)
• Data Analytics & Statistical Machine Learning (20 credits)
• Metabolomics and advanced (omics) technologies (20 credits)
• Computational Biology for Complex Systems (20 credits)
• Interdisciplinary Bioinformatics Group Project (20 credits)
• Individual Project (60 credits)

This module will provide an introduction (or refresher) to essential biological and quantitative theory that underpins modern bioinformatics. Concepts will be introduced via a series of core problems whose details will be explored in greater depth in later modules.

Quantitative topics will include:

• Linear Algebra: basic matrix-vector operations, least-squares
• Probability Theory: Rules of Probability, Conditional Probability, Bayes’ Rule, distributions
• Descriptive Statistics: summary statistics, visualisation
• Hypothesis Testing: Fisher exact, chi-square, t-test
• Correlation and Causation: Parametric and non-parametric measures
• Introduction to Statistical Modelling in the R programming language: linear models, estimation

Furthermore, this module will go through the very essential of biology, biochemistry and biotechnology including cells, proteins, DNA and genes in to reach a level where you are on par to understand the mandatory modules.

The module contains a variety of integrated learning environments, including interactive lectures as well as tutorials to explain and give feedback on aspects of assessment.

By the end of the module you will be able to:

• Understand essential mathematical and statistical concepts and apply the correct techniques to solve elementary data analysis problems
• Correctly apply techniques for the graphical representation and visualisation of data
• Perform essential statistical data analysis in a computer programming language, specifically R
• Understand essential concepts in cell biology and genetics such as the role of DNA, RNA and Proteins and their relation to specific bioinformatics problems.
• Solve quantitative problems inspired by real world bioinformatics that require an understanding of the underlying biology and the application of the correct mathematical and statistical techniques
• Demonstrate the qualities and transferable skills necessary for employment requiring the exercise of initiative and personal responsibility, decision making in complex and unpredictable situations, and the independent learning ability required for continuing professional development

This module will introduce the you to various sides of *Omics:

• Genomics
• Transcriptomics
• Methylation
• Transcription factors analysis
• RNA binding protein analysis
• Chromatin accessibility analysis (e.g. DNase-seq, ATAC-seq)
• Chromatin structure analysis (e.g. HiC, ChIA-PET)

The module will include a coverage of the technological progress:

• History: Sanger sequencing through array technologies
• Next generation Sequencing
• Advanced library construction procedures for specialized assays, including ChIP, DNase, ATAC, HiC, eCLIP, and others

This module will also address specific fields of Classical Genetics, Population Genetics and Cancer Genomics. It will involve a biological, technological and analytical dimension to help you design the best experiment with the appropriate data type and enable its analysis with the latest state of the art approaches.

By the end of the module you should be able to:

• Understand the biological interpretation of the various *omics fields, especially DNA, RNA and Methylation based.
• Understand the various technologies available to measure the various type of information from Sanger sequencing, micro-array, Mass-Spectrometry to Next Generation sequencing
• Analyse the various types of data generated in the field both with command line and web interface such as Galaxy
• Integrate the various type of data to understand the biological implication of the results
• Deal with the complexity of information available to enable the integration of diverse data types

The aim of the module is to provide an in-depth understanding of the state of the art in data integration, mining and analysis with applications in biology and biomedicine.

The module covers topics related to data:

• Data types,
• Data modelling,
• Data management,
• Semantic representation,
• Integration,
• Analysis

The module will include various statistical techniques:

• Frequentist and Bayesian approaches,
• Univariate and multivariate analysis,
• Specific statistics definition.

Furthermore it will present Modelling and Optimisation approaches to deal with large structured, yet heterogeneous, dataset and will include several techniques

• Hidden Markov Models,
• Self Organizing Maps,
• Boot-strapping and resampling procedures,
• Agent-based modelling,
• Statistical Machine Learning.

The module will aslo provide methods to analyze, visualize and integrate the various types of data and includes training on several well used web-based resources such as OMIM, TCGA, DAVID, REACTOME

By the end of the module you will be able to:

• Demonstrate a good understanding of complexity of omics and clinical data and their management including their semantic representation
• Demonstrate an in-depth understanding and ability to perform Data integration, mining and analysis
• Demonstrate conceptual understanding of Computing, Algorithmic and Programming that enables the student to evaluate methodologies and develop critiques of them and, where appropriate, propose new methods
• Deal with the complexity of information available to enable the integration of diverse data types
• Demonstrate self direction and originality in tackling and solving problems to perform the appropriate Modelling and Optimization

Metabolomics and advanced (omics) technologies (20 credits)

This module will introduce you to metabolomics, and you will learn about the data processing and data analysis approaches (e.g. biostatistics and metabolite identification) that are used to interpret data and extract biological insight from the large metabolomics data sets. You will also be introduced to the analytical approaches (e.g. mass spectrometry and NMR spectroscopy) that are used in metabolomics, so that you can appreciate the challenges involved in producing robust and reproducible data sets.

Additionally, this module will introduce you to other emerging and advanced (omics) techniques, including bioimaging and spectroscopy.

The course will include a combination of interactive seminars, hands-on computer workshops, tutorials and a tour of the new Phenome Centre Birmingham.

By the end of the module you will be able to:

• Demonstrate a conceptual understanding of metabolomics, biological imaging and other advanced bioscience technologies.
• Demonstrate a conceptual understanding of the major challenges facing metabolomics, biological imaging and other advanced bioscience technologies.
• Demonstrate a conceptual understanding of a typical bioinformatics workflow to process and analyse metabolomics datasets.
• Perform basic bioinformatics data analysis and extract biological insight from large metabolomics data sets.

This module focuses on big data-driven science leveraging diverse omics modalities in the environmental, ecological and toxicological areas. This module will draw from the fields of molecular biology, genomics, genetics, evolutionary biology, computational biology, toxicology, and risk assessment –though these are not prerequisites for enrolment. Theory and concepts will be highlighted by real world applications drawn from the scientific literature. By involving instructions from industry, government agency and NGO scientists, it means to offer you a variety of dynamically evolving career paths.

Specifically it will contain 3 parts:

• Introduction to Environmental, Ecological and Toxicological Sciences and practical examples – with a focus on research conducted in the University of Birmingham Macrocosms. In the first year, this will focus specifically on BIFoR and DRI-STREAM.
• Data types and problems faced in the study of highly complex environmental and biological systems.
• Computational approaches specific to the field such as complexity theory, hierarchical models, ecological models, population dynamics, and the emerging fields in which Birmingham faculty play a world-leading role: phylogenomic toxicology and molecular ecosystems biology.

By the end of the module you will be able to:

• Demonstrate a fundamental technical understanding of Omics technologies (transcriptomics and metabolomics), high-throughput in vivo and in vitro assays, computational approaches as applied to environmental, ecological, and holobiotic systems (animals and/or plants + their microbiomes)
• Demonstrate a systematic understanding of the emerging field of Molecular Ecosystems Biology, including an emphasis on biotic-abiotic interactions, and the role of the microbiome in establishing the health and resiliency of organisms.
• Demonstrate a conceptual and mechanistic understanding of integrative analysis techniques for multi-omics data – and the ability to apply these techniques to their own research
• Demonstrate a systematic understanding and critical awareness of the implications of research in Biology related fields, especially ethically

This module will pull together students from various backgrounds to tackle an inter-disciplinary project, using mathematical and/or computational approaches to address a real-world research question involving biological data.

You will have lectures on how to prepare scientific publications, posters and presentations as well as on the ethics requirements of research.

You will work in group of 3-5 on a real-life problem proposed by an academic member of UoB or external collaborators. You will have to find the relevant literature, and apply the relevant analytical methods to generate new information that will be presented as a group and individually.

By the end of the module you will be able to:

• Work effectively in an interdisciplinary team
• Carry out a relevant literature search for their topic
• Demonstrate a comprehensive understanding of the broad world of *Omics in the context of complex biological, clinical, or environmental data.
• Choose appropriate computational and/or mathematical approaches to perform analysis of *Omics data; evaluate methodologies and develop critiques of them and, where appropriate, propose new methods
• Demonstrate a systematic understanding and critical awareness of the implications of research in biology-related fields, including an understanding of ethics
• Present the results of the project in written and oral form.

This module will put you in real-life situation of a bioinformatics project with practical problem to solve proposed by an academic member of UoB. You will have to find the relevant literature, and apply the relevant analytical methods to generate new information and present in written and oral form.

By the end of the module you will be able to:

• Present your topic background, approach, analysis, results and conclusionsin in written and oral form
• Perform a bioinformatics analysis and/or development for the project
• A conceptual understanding of Computing, Algorithmic and Programming that enables the student to evaluate methodologies and develop critiques of them and, where appropriate, propose new methods
• Demonstrate self direction and originality in tackling and solving problems to perform the appropriate Modelling and Optimization