Nanoscience Fundamentals Training Course

This is a practical workshop that introduces the fundamentals of bioinformatics. It is designed for individuals with a foundational background in biology and molecular biology who wish to learn how to process and analyse biological data using computational tools. The course has been developed specifically for:

• Researchers working in experimental laboratories on biological macromolecules such as proteins, RNA and DNA.
• Scientists whose primary expertise lies outside biology but who need to handle biological data.
• Managers launching new products (for example, pharmaceuticals) who wish to expand their understanding of contemporary bioinformatics.
• Software developers planning to create programs that interact with biological molecules.

The course is split into two components: theoretical and practical. Lectures are interspersed with straightforward hands-on exercises, quizzes and tests to ensure participants grasp the key concepts.

Throughout the training, you will be taken on a guided tour covering all essential aspects of biological data analysis. This workshop offers an ideal opportunity to become familiar with bioinformatics and its diverse applications. By the end, you should be equipped to address fundamental bioinformatics challenges: you will know where to source required data, how to extract relevant information, and how basic software and algorithms function. Our goal is to enhance your understanding of biological data.

Upon completion of the course, you will be able to work with biological data using bioinformatics tools.

This instructor-led, live training in New Zealand (delivered either online or on-site) is designed for healthcare professionals and workers who wish to gain intermediate to advanced-level knowledge of various genetic sequencing and testing operations.

By the end of this training, participants will be able to:

• Learn and understand key concepts of genetics.
• Understand how genetic sequencing and testing operations function.

Good Clinical Practice (GCP) is a set of guidelines for clinical research conducted on human participants. GCP serves as the international standard for designing, conducting, monitoring, documenting, analysing, and reporting clinical trials.

In this instructor-led, live training, participants will learn the most important guidelines and fundamental principles applied in Good Clinical Practice (GCP) for clinical trials and research involving human participants. This course provides an overview of the different GCP areas, with slightly more emphasis on the ICH-centric GCP for Investigational Drugs and Biologics.

By the end of this training, participants will be able to:

• Understand the fundamental principles of GCP for investigational drugs and biologics as established by the International Conference on Harmonisation (ICH)
• Apply the guidelines stipulated by GCP to their clinical research and trials

Audience

• Clinical investigators
• Clinical research professionals
• Healthcare and medical professionals
• Clinical trials support staff
• Research and development staff

Format of the course

• Part lecture, part discussion, exercises and extensive hands-on practice

Microbial genomics and metagenomics integrate sequencing technologies with computational analysis to profile microbial communities, characterise genomes, and detect functional traits such as antimicrobial resistance.

This instructor-led, live training (available online or on-site) is designed for beginner to intermediate-level researchers and bioinformaticians who wish to gain hands-on experience analysing microbial genomics and metagenomic sequencing data.

By the end of this training, participants will be able to:

• Describe common sequencing workflows and key file formats used in microbial genomics.
• Perform quality control and preprocessing of raw NGS reads.
• Carry out taxonomic profiling using marker-based and k-mer approaches.
• Assemble and annotate microbial genomes and plasmids.
• Detect SNPs, construct phylogenies, and interpret population structure.
• Predict antimicrobial resistance genes and summarise functional profiles.
• Implement reproducible workflows using Conda, Docker, and notebook-based reports.

Course Format

• Lectures introducing concepts and tools (20%).
• Hands-on practical sessions using example datasets (60%).
• Open discussions and guided analysis, enabling participants to apply methods to their own data (20%).

Course Customisation Options

• To request a customised workshop focusing on clinical, environmental, or industrial metagenomics, please contact us to arrange.

This is a hands-on course that demonstrates why programming is a powerful tool for solving biological problems. Throughout the course, participants will learn the Python programming language, widely regarded as both powerful and user-friendly. The course serves as a practical illustration of how bioinformatics can enhance the work of biologists.

It is specifically designed for individuals without a computer science background who wish to learn programming.

This course is suitable for:

• Researchers working with biological data.
• Scientists looking to automate routine tasks and analyse data.
• Managers interested in understanding how programming can streamline workflows and project execution.

By the end of the course, participants will be able to write short programs that enable them to manipulate, analyse, and handle biological data, as well as present results in graphical formats.

This course aims to give researchers an understanding of the principles of statistical design and analysis and their relevance to research across a range of scientific disciplines.

It covers key probability and statistical methods, primarily through practical examples. The training comprises approximately 30% lectures and 70% guided quizzes and laboratory sessions.

For closed courses, we can tailor the examples and materials to a specific field (such as psychology testing, the public sector, biology, genetics, etc.).

For public courses, a mix of examples is used.

Although various software tools are used throughout the course (from Microsoft Excel to SPSS, Statgraphics, and others), the primary focus remains on understanding the principles and processes that guide research, reasoning, and conclusions.

This course can be delivered as a blended learning programme, incorporating homework and assignments.