Applied Bioinformatics MSc

• Dr Maria Anastasiadi

This module provides you with an introduction to the theoretical and practical aspects required to undertake rigorous and valid data analysis of multivariate biological datasets using the R environment. You will learn best practices for experimental design and data collection, inspection and manipulation and visualisation of biological datasets, statistical analysis, and interpretation of the results. You will use R throughout the course to execute these tests using existing R libraries and will also learn to develop bespoke scripts for their individual needs. The course aims to bring you up to an advanced level in using R tools for data science and you will have the opportunity to gain significant hands-on experience throughout the course on several different topics. 

• An introduction to R,
• Introductory statistics – averages, variance, and significance testing,
• Data pre-processing techniques,
• Introduction to Bayesian Statistics,
•  Exploratory data analysis using unsupervised methods (PCA, HCA, k-means).

On successful completion of this module you should be able to:

• Generate R scripts to perform data analysis tasks,
• Critically assess the basic principles of different statistical techniques, be able to implement them programmatically and effectively integrate and devise statistical methods into experimental protocol design,
• Conduct exploratory data analysis and manipulate the data to meet required specifications using different data pre-processing techniques,
• Evaluate the difference between univariate and multivariate analysis,
• Apply exploratory data analysis using unsupervised multivariate analysis methods. 

• Dr Alexey Larionov

This module provides a general introduction to bioinformatics and fundamentals of programming. The module covers the programming basics required by students in order to program in Python, which is nowadays becoming one of the most popular programming languages in the bioinformatics community; and its application in retrieving, parsing and visualising biological sequence data.

Fundamentals of Python programming,

Introduction to Object Oriented Programming (OOP),

Simple mathematical operations,

Modules in Python,

Various data types and Objects,

Control Statements,

Lists, Tuples and Dictionaries,

Functions,

Regular expressions,

Error handling,

File IO,

Programming for biology using BioPython:

DNA sequence manipulation,

Reading protein files,

Performing Multiple Sequence Alignment,

BLAST,

Data Visualisation.

Biological data formats.

On successful completion of this module you should be able to:

• Identify the most important programming structures.
• Retrieve nucleotide, protein sequences and their corresponding metadata from online public data resources.
• Develop custom Python scripts for sequence manipulation.
• Develop Python scripts to automate data handling and curation tasks.
• Develop advanced stand-alone Python programs for the acquisition and consolidation of data from remote databases.

• Dr Alexey Larionov

To provide you with the knowledge of the current trends in analysing epigenomic, proteomic, and metagenomic data and to demonstrate its principles, challenges, and complexities in bioinformatics.

• Introduction to general epigenetics concepts, and to analysis of DNA methylation, histones modification, chromatin structure, and transcription factors binding sites.
• Quality control, pre-processing, and analysis of ATAC-seq data through a standard pipeline.
• Application of bioinformatics to relate ATAC-seq to transcriptomics data and to assess phenotypic outcomes.
• Introduction to practical proteomics (qualitative & quantitative).
• Proteomics repositories and databases (PDB, UNIPROT, etc.).
• Protein/peptide identification algorithms Protein structures and molecular modelling.
• Soil metagenomics: quality control, filtering and assembly to taxonomic classification, clustering, and functional assignment.
• Analysis of microbial community composition and comparative metagenomics using QIIME2 pipeline and selected R packages

On successful completion of this module you should be able to:

• Synthesise information to discuss the key technological development in the acquisition of epigenomic, proteomic and metagenomic data,
• Explain the mode of operation of the most common analytical techniques and how these relate to the quality of the data acquired,
• Critically assess current practices and identify the relative strengths and weaknesses of the techniques covered,
• Discover information using bioinformatics tools and effectively apply the information to biological problems,
• Participate in scientific discussions regarding the relevant omics technologies and evaluate scientific results.

• Professor Fady Mohareb

To introduce you to the techniques that have given rise to the genomic data now available and develop skills and understanding in the bioinformatics approaches that facilitate evaluation and application of these data. Over the past decade, Next-generation DNA Sequencing (NGS) technology has been a huge stimulus for a lot of breakthrough discoveries in biology. This module provides an overview of many core types of NGS projects, including latest protocols in genomic and transcriptomic analyses, genotyping and variant calling as well as detailed hands-on practical sessions of our best practice data-analysis workflows.

• Gene expression analysis using microarray,
• Introduction to Next Generation Sequencing (NGS) Technology,
• Overview of genome assembly and quality control,
• Transcriptome informatics,
• Sequence data analysis web platforms,
• Geneotyping and variant calling.

On successful completion of this module you should be able to:

• Critically evaluate the operation of the most common analytical techniques used in the acquisition of genomic sequence and expression data,
• Critically assess the quality of raw sequence reads and apply various techniques to overcome the challenges of dealing with poor quality data using appropriate software tools,
• Perform gene expression profiling using both first and next generation sequencing data,
• Critically assess current practices and evaluate the relative strengths and weaknesses of the techniques covered and how these relate to the quality of the biological findings,
• Critically contrast a range of NGS tools and related sequence software tools for NGS applications and interpret the output from those tools.

• Dr Maria Anastasiadi

During this module you will learn the main aspects related to the analysis of the metabolic profile in living organisms and explore statistical and computational techniques that are central to the field of metabolomics with particular emphasis on machine learning. Machine learning is a rapidly expanding form of artificial intelligence (AI) which has found many applications in the field of Biosciences. Examples include explanatory analysis of complex biological systems, novel biomarker discovery and prediction modelling. You will have the opportunity to learn the fundamentals of machine learning, from a theoretical perspective and will also acquire practical experience on a wide variety of machine learning techniques, such as generalised linear regression, penalised regression, decision trees and random forests, support vector machines, and deep neural networks which they will apply to biological datasets to develop and validate prediction models for classification and numeric prediction purposes. All practical sessions will be completed using the R environment. 

• Metabolomics: overview and workflow,
• Multivariate classification and biomarker discovery,
• Introduction to machine learning,
• Applications of machine learning in metabolomics,
• Advanced topics in machine learning,
• Applications of machine learning in food metabolomics,
• Introduction to image analysis,
• Advanced topics in R.

On successful completion of this module you should be able to:

• Critically assess various metabolomics analytical and spectral platforms,
• Apply state-of-the-art best practices in machine learning to fit the purpose of the analysis,
• Develop classification and regression models based on multivariate metabolic data,
• Provide examples of specific machine learning algorithms for classification and regression tasks or dual purpose,
• Apply statistical and machine learning procedures covered during the module, to derive biological relevant information from metabolic datasets using R.

• Dr Tomasz Kurowski

To introduce you to concepts of object oriented programming using Java. Java is the pre-eminent programming language for serious application development on the Internet. The module covers Java data objects of primitive and reference data types and introduces you to the fundamentals of programming in Java, with hands-on practical sessions on implementing simple programs using calculations, variables, control statements and loops. 

• Fundamental principles of programming in Java,
• Object-oriented programming using Java,
• Variables and calculations,
• Strings,
• Arrays, ArrayLists and HashMaps,
• GUI programming.

On successful completion of this module you should be able to:

• Identify and apply the most important programming structures,
• Develop Java programs to meet given specifications,
• Implement custom Java classes, interfaces, and packages,
• Implement standalone application interfaces using Java Swing Components.

• Professor Fady Mohareb

This module will cover the latest bioinformatics tools and algorithms involved with developing a high-quality genome assemblies for orphan and previously unsequenced species, as well as improve the continuity and quality of existing genome assembly drafts. .This is established through the integration of advanced NGS and 3GS sequencing data with functional annotation using graph theory algorithms widely applied for various assemblers such de-Brujin and Overlap-layout consensus. This module gives an insight on the details of -omic-scale/big-data-driven life science making use of core platform technologies. 

• How research is conducted in genome bioinformatics and within the broader context of interdisciplinary life sciences. 
• Raw sequencing reads quality control and pre-processing.
• Short reads assembly.
• Long reads assembly.
• Hybrid assembly strategies.
• Genome assembly super-scaffolding, quality assessment and function annotation.

On successful completion of this module you should be able to:

• Critically assess the technical limitations and the underlying biological and experimental assumptions that impact on data quality.
• Apply and optimise various algorithms for short and long reads sequence assembly, including the application of de Bruijn and Overlap layout Consensus graph theories in genome assembly.
• Successfully develop and optimise de-novo genome assemblies for various species.
• Develop in-silico gene prediction models and functional annotation.

• Dr Tomasz Kurowski

Data integration represents a major challenge for bioinformatics research. This module covers the most popular data management, integration and visualisation tools within the bioinformatics community as well as the main concepts of databases design and normalisation.

• Database design and normalisation,
• Development of database access interfaces,
• Design and implementation of data repository Web front-ends,
• Techniques to integrate, interpret, analyse and visualise biological data sets,
• Introduction to interaction networks,
• Data Integration and visualisation.

On successful completion of this module you should be able to:

• Utilise systems software for the visualisation of systems and system interactions,
• Critically apply available tools for data integration,
• Design, normalise and implement databases for experimental datasets,
• Critically assess the main data standards protocols for genomics, as well as the current approaches for modelling and warehousing of life science data,
• Discover systems relationships between data using bioinformatics tools and approaches.