All Bioinformatics courses

Have you heard about High Performance Computing, but are not sure what it is or whether it is relevant for your work? Would you like to use a HPC, but are not sure where to start? Are you using your personal computer to run computationally demanding tasks, which take long and slow down your work? Do you need to use software that runs on Linux, but don't have access to a Linux computer? If any of these questions apply to you, then this course might be for you!

Knowing how to work on a High Performance Computing system is an essential skill for applications such as bioinformatics, big-data analysis, image processing, machine learning, parallelising tasks, and other high-throughput applications.

In this course we will cover the basics of High Performance Computing, what it is and how you can use it in practice. This is a hands-on workshop, which should be accessible to researchers from a range of backgrounds and offering several opportunities to practice the skills we learn along the way.

As an optional session for those interested, we will also introduce the (free) HPC facilities available at Cambridge University (the course is not otherwise Cambridge-specific).

If you do not have a University of Cambridge Raven account please book or register your interest here.

• ♿ The training room is located on the first floor and there is currently no wheelchair or level access.
• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance, including for University of Cambridge students. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.
• Guidance on visiting Cambridge and finding accommodation is available here.

THIS EVENT IS NOW FULLY BOOKED!

The aim of this 5 days course is to develop motivated participants toward becoming independent BioImage Analysts in an imaging facility or research role. Participants will be taught theory and algorithms relating to bioimage analysis using Python as the primary coding language.

Lectures will focus on image analysis theory and applications. Topics to be covered include: Image Analysis and image processing, Python and Jupyter notebooks, Visualisation, Fiji to Python, Segmentation, Omero and Python, Image Registration, Colocalisation, Time-series analysis, Tracking, Machine Learning, and Applied Machine Learning.

The bulk of the practical work will focus on Python and how to code algorithms and handle data using Python. Fiji will be used as a tool to facilitate image analysis. Omero will be described and used for some interactive coding challenges.

Research spotlight talks will demonstrate research of instructors/scientists using taught techniques in the wild.

This event is organized in collaboration with the Image Analysis Focused Interest Group and is sponsored by the Royal Microscopical Society.

The training room is located on the first floor and there is currently no wheelchair or level access available to this level.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

One of the most important tasks of systems biology is to create explanatory and predictive models of complex biological systems. Availability of gene expression data in different conditions has paved the way for reconstructing direct or indirect regulatory connections between various genes and gene products. Most often, we are not interested in single interactions between gene products; instead, we try to reconstruct networks that provide insights into the investigated biological processes or the entire system as a whole.

This webinar will expand upon the concept of Gene Co-expression Networks to elucidate Weighted Gene Co-expression Network Analysis (WGCNA), and introduce the importance of visualising clustered gene expression profiles as single ‘Eigengenes’. It will describe the complete protocol for WGCNA analysis starting from normalised Gene Expression Datasets (Microarrays or RNA-Seq). This will be followed by a discussion on methods of extraction and analysis of consensus modules and Network motifs from Gene Co-Expression Networks and Transcriptional Regulatory Networks.

The webinar will be presented in the form of a lecture and tutorial with screenshots that enable listeners to emulate the protocols in R. Note that this is a webinar and not a coding exercise. Links to further reading and practice will be shared.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

This course will focus on computational methods for analysing cellular images and extracting quantitative data from them. The aim of this course is to familiarise the participants with computational image analysis methodologies, and to provide hands-on training in running quantitative analysis pipelines.

On day 1 we will introduce principles of image processing and analysis, giving an overview of commonly used algorithms through a series of talks and practicals based on Fiji, an extensible open source software package.

On day 2, we will cover time series processing and cell tracking using TrackMate. The afternoon of day two will focus on understanding the basics of deconvolution and colocalisation, using tools in Fiji to look at basic examples of how to apply deconvolution and how to carry out colocalisation analysis in fluorescence microscopy.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

This course will focus on computational methods for analysing cellular images and extracting quantitative data from them. The aim of this course is to familiarise the participants with computational image analysis methodologies, and to provide hands-on training in running quantitative analysis pipelines.

On day 1 we will introduce principles of image processing and analysis, giving an overview of commonly used algorithms through a series of talks and practicals based on Fiji, an extensible open source software package.

On day 2, we will cover time series processing and cell tracking using TrackMate and advanced image segmentation using Ilastik. Additionally, in the afternoon we will run a study design and data clinic (sign up will be required) for participants that wish to discuss their experiments.

On day 3, we will describe the open Icy platform developed at the Institut Pasteur. Icy is a next-generation, user-friendly software offering powerful acquisition, visualisation, annotation and analysis algorithms for 5D bioimaging data, together with unique automation/scripting capabilities (notably via its graphical programming interface) and tight integration with existing software (e.g. ImageJ, Matlab, Micro-Manager).

The training room is located on the first floor and there is currently no wheelchair or level access available to this level.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

LithoGraphX is a software to visualize, process and analyse 3D images and meshes.

On the first day of this course, we will demonstrate how to use LithoGraphX to visualize, clean and process 2D and 3D images. We will cover: (i) how to extract cell shape from 2D or 3D images by marking the cell wall or membrane, (ii) how to extract key morphological features and (iii) how to use these features to build a cell classifier. The first day is intended for biologists and computer scientists interested in using LithoGraphX.

On the second day, we will see how to write and distribute extensions to LithoGraphX. To this purpose, we will learn more about the internals of LithoGraphX and its API both in C++ and Python. The second day is intended for computer scientists wanting either to write their own algorithm or automate complex protocols.

Participants can choose to register for both days or for individual days, depending on their interest and background knowledge.

The timetable for this event can be found here.

This course is organized in collaboration with Dr Susana Sauret-Gueto from the OpenPlant Lab of the Department of Plant Sciences of the University of Cambridge.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to Book or register Interest by linking here.

One of the most important tasks of systems biology is to create explanatory and predictive models of complex biological systems. Availability of gene expression data in different conditions has paved the way for reconstructing direct or indirect regulatory connections between various genes and gene products. Most often, we are not interested in single interactions between gene products; instead, we try to reconstruct networks that provide insights into the investigated biological processes.

This webinar will introduce the importance and applications of Gene Expression Datasets (Microarrays and RNA-Seq), followed by methods of extraction and analysis of Co-Expression Networks and Transcriptional Regulatory Networks from these datasets. The webinar will focus on the pros and cons of Weighted and Unweighted Networks, citing examples to aid decisions about which networks to use and when.

The webinar will be presented in the form of a lecture and tutorial with screenshots that enable listeners to emulate the protocols in R. Note that this is a webinar and not a coding exercise. Links to further reading and practice will be shared.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

The vast majority of data produced fits the criteria of labelled data (with either continuous of categorical labels); the machine learning task of discriminating classes (for categorical outputs) or predicting future values (continuous outputs) will be discussed in detail, focusing both on classical methods – k nearest neighbours, decision tree based methods and support vector machine – and on the importance and discriminative power of features.

The module will provide support in generating models (using R as programming environment), critically assessing the optimisation of hyperparameters and evaluating the usefulness of the model with respect to the initial question. The examples presented throughout stem from biological examples, yet the skills and critical assessment of outputs are transferrable.

If you do not have a University of Cambridge Raven account please book or register your interest here.

• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance, including for University of Cambridge students. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.

DECIPHER is a collaborative data sharing and interpretation platform that enables the secure upload, analysis and subsequent sharing of anonymised phenotype-linked patient variant data in rare genetic disorders.

DECIPHER is a worldwide user community of over 250 clinical genetics centres and research groups from over 40 countries that utilise the built-in tools for aiding the interpretation of variants as well as to discover other patients that share similar phenotype and genomic findings.

DECIPHER facilitates collaboration and exchange of information between a global community of clinical centers and researchers leading thereby accelerating discovery and diagnosis. Access to consented anonymised records is free to all users. User accounts are provided to bona-fide clinicians and lab scientists to enable deposition and sharing of anonymised patient data.

The purpose of this half-day workshop is to acquaint participants with the DECIPHER website and database and introduce the various built-in tools for visualisation and interpretation of phenotype-linked genomic variation in anonymised consented patient data. It is hoped that by the end of this workshop, users will be able to carry out effective searches of data, use the built-in genome browser to visualise variation in context of other pathogenic and reference data sources, find other patients with similar variants and shared phenotypes, and identify most likely causes of phenotypic presentation by gene prioritisation.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to Book or register Interest by linking here.

This course is aimed to provide the tools to conduct Bayesian inference in common situations.

We will be contrasting Bayesian Inference with classical hypothesis testing, covering conjugate distributions and credible intervals. We will also look at modern computational methods such as MCMC approaches using the BUGS library.

If you do not have a University of Cambridge Raven account please book or register your interest here.

• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance, including for University of Cambridge students. After you have booked a place, if you are unable to attend any of the live sessions, please email the Bioinfo Team.
• Further details regarding eligibility criteria are available here.