Bioinformatics course timetable

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This course covers concepts and strategies for working more effectively with Python with the aim of writing reusable code, using function and libraries. Participants will acquire a working knowledge of key concepts which are prerequisites for advanced programming in Python e.g. writing modules and classes.

Note: this course is the continuation of the Introduction to Solving Biological Problems with Python; participants are expected to have attended the introductory Python course and/or have acquired some working knowledge of Python. This course is also open to Python beginners who are already fluent in other programming languages as this will help them to quickly get started in Python.

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.

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This course covers concepts and strategies for working more effectively with Python with the aim of writing reusable code, using function and libraries. Participants will acquire a working knowledge of key concepts which are prerequisites for advanced programming in Python e.g. writing modules and classes.

Note: this course is the continuation of the Introduction to Solving Biological Problems with Python; participants are expected to have attended the introductory Python course and/or have acquired some working knowledge of Python. This course is also open to Python beginners who are already fluent in other programming languages as this will help them to quickly get started in Python.

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.

PLEASE NOTE that this course will be taught live online, with tutors available to help you throughout if have any questions. All resources and lectures will be recorded and uploaded to the course VLE page so that you will be able to access that information even if technical or time zone restrictions means that you aren't able to join us for all of the live sessions.

This award winning virtually delivered course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

1. Use R or Python confidently for statistics and data analysis
2. Be able to analyse datasets using standard statistical techniques
3. Know which tests are and are not appropriate

Both R and Python are free software environments that are suitable for statistical and data analysis.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory.

After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.

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.

PLEASE NOTE that this course will be taught live online, with tutors available to help you throughout if have any questions. All resources and lectures will be recorded and uploaded to the course VLE page so that you will be able to access that information even if technical or time zone restrictions means that you aren't able to join us for all of the live sessions.

This award winning virtually delivered course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

1. Use R or Python confidently for statistics and data analysis
2. Be able to analyse datasets using standard statistical techniques
3. Know which tests are and are not appropriate

Both R and Python are free software environments that are suitable for statistical and data analysis.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory.

After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.

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.

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

The Ensembl Project provides a comprehensive and integrated source of annotation of, mainly vertebrate, genome sequences. This workshop offers a comprehensive practical introduction to the use of the Ensembl genome browser as well as essential background information.

This course will focus on the vertebrate genomes in Ensembl, however much of what will be covered is also applicable to the non-vertebrates (plants, bacteria, fungi, metazoa and protists) in Ensembl Genomes.

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.

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This course provides a refresher on the foundations of statistical analysis. The emphasis is on interpreting the results of a statistical test, and being able to determine the correct test to apply.

Practicals are conducted using a series of online apps, and we will not teach a particular statistical analysis package, such as R. For courses that teach R, please see the links under "Related courses" .

This event is part of a series of training courses organized in collaboration with the Bioinformatics Core Facility at CRUK Cambridge Institute.

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.

PLEASE NOTE that this course will be taught live online, with tutors available to help you throughout if have any questions. All resources and lectures will be recorded and uploaded to the course VLE page so that you will be able to access that information even if technical or time zone restrictions means that you aren't able to join us for all of the live sessions.

This award winning virtually delivered course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

1. Use R or Python confidently for statistics and data analysis
2. Be able to analyse datasets using standard statistical techniques
3. Know which tests are and are not appropriate

Both R and Python are free software environments that are suitable for statistical and data analysis.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory.

After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.

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.

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

The Ensembl project provides a comprehensive and integrated source of annotation of mainly vertebrate genome sequences.

This workshop is aimed at researchers and developers interested in exploring Ensembl beyond the website. The workshop covers how to use the Ensembl REST APIs, including understanding the major endpoints and how to write scripts to call them.

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.

PLEASE NOTE that this course will be taught live online, with tutors available to help you throughout if have any questions. All resources and lectures will be recorded and uploaded to the course VLE page so that you will be able to access that information even if technical or time zone restrictions means that you aren't able to join us for all of the live sessions.

This award winning virtually delivered course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

1. Use R or Python confidently for statistics and data analysis
2. Be able to analyse datasets using standard statistical techniques
3. Know which tests are and are not appropriate

Both R and Python are free software environments that are suitable for statistical and data analysis.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory.

After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.

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.

PLEASE NOTE that this course will be taught live online, with tutors available to help you throughout if have any questions. All resources and lectures will be recorded and uploaded to the course VLE page so that you will be able to access that information even if technical or time zone restrictions means that you aren't able to join us for all of the live sessions.

This award winning virtually delivered course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

1. Use R or Python confidently for statistics and data analysis
2. Be able to analyse datasets using standard statistical techniques
3. Know which tests are and are not appropriate

Both R and Python are free software environments that are suitable for statistical and data analysis.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory.

After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.

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.

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors to assist you with instant and personalised feedback and to help you to run/execute the scripts which we will be using during the course. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

R is one of the leading programming languages in Data Science. It is widely used to perform statistics, machine learning, visualisations and data analyses. It is an open source programming language so all the software we will use in the course is free. This course is an introduction to R designed for participants with no programming experience. We will start from scratch by introducing how to start programming in R and progress our way and learn how to read and write to files, manipulate data and visualise it by creating different plots - all the fundamental tasks you need to get you started analysing your data. During the course we will be working with one of the most popular packages in R; tidyverse that will allow you to manipulate your data effectively and visualise it to a publication level standard.

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.

PLEASE NOTE that this course will be taught live online, with tutors available to help you throughout if have any questions. All resources and lectures will be recorded and uploaded to the course VLE page so that you will be able to access that information even if technical or time zone restrictions means that you aren't able to join us for all of the live sessions.

This award winning virtually delivered course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

1. Use R or Python confidently for statistics and data analysis
2. Be able to analyse datasets using standard statistical techniques
3. Know which tests are and are not appropriate

Both R and Python are free software environments that are suitable for statistical and data analysis.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory.

After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.

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.

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This workshop is designed for researchers interested in learning about functional genomics data, how to access, retrieve and use the data from ArrayExpress and hands-on experience in using Expression Atlas, a resource to find information about gene and protein expression across species and biological conditions such as different tissues, cell types, developmental stages and diseases among others. This will include an overview on how gene expression data is curated and analysed in Expression Atlas and a practical activity to demonstrate how to access and visualise gene expression analysis results. These activities should help you answer questions such as "where is my favourite gene expressed?" or "how does its expression change in a disease?".

This workshop is not going to be a session on how to run your own bioinformatics analysis but to use the tools that have been developed in order to be able to take advantage of others’ work and prepare your work to be reproducible.

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.

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This workshop is designed for researchers interested in learning about functional genomics data, how to access, retrieve and use the data from ArrayExpress and hands-on experience in using Expression Atlas, a resource to find information about gene and protein expression across species and biological conditions such as different tissues, cell types, developmental stages and diseases among others. This will include an overview on how gene expression data is curated and analysed in Expression Atlas and a practical activity to demonstrate how to access and visualise gene expression analysis results. These activities should help you answer questions such as "where is my favourite gene expressed?" or "how does its expression change in a disease?".

This workshop is not going to be a session on how to run your own bioinformatics analysis but to use the tools that have been developed in order to be able to take advantage of others’ work and prepare your work to be reproducible.

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.

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

RNA-Seq technology has been transformative in our ability to explore gene content and gene expression in all realms of biology, and de novo transcriptome assembly has enabled opportunities to expand transcriptome analysis to non-model organisms.

This course provides an overview of modern applications of transcriptome sequencing and popular tools, and algorithms, for exploring transcript reconstruction and expression analysis in a genome-free manner.

Attendees will perform quality assessment and upstream analysis of both Illumina and long reads single molecule sequencing data; the derived transcriptomes will be compared, annotated and used as reference for quantifying transcript expression, leveraging on Bioconductor tools for differential expression analysis. Additional methods will be explored for characterising the assembled transcriptome and revealing biological findings.

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.

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

RNA-Seq technology has been transformative in our ability to explore gene content and gene expression in all realms of biology, and de novo transcriptome assembly has enabled opportunities to expand transcriptome analysis to non-model organisms.

This course provides an overview of modern applications of transcriptome sequencing and popular tools, and algorithms, for exploring transcript reconstruction and expression analysis in a genome-free manner.

Attendees will perform quality assessment and upstream analysis of both Illumina and long reads single molecule sequencing data; the derived transcriptomes will be compared, annotated and used as reference for quantifying transcript expression, leveraging on Bioconductor tools for differential expression analysis. Additional methods will be explored for characterising the assembled transcriptome and revealing biological findings.

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.

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

RNA-Seq technology has been transformative in our ability to explore gene content and gene expression in all realms of biology, and de novo transcriptome assembly has enabled opportunities to expand transcriptome analysis to non-model organisms.

This course provides an overview of modern applications of transcriptome sequencing and popular tools, and algorithms, for exploring transcript reconstruction and expression analysis in a genome-free manner.

Attendees will perform quality assessment and upstream analysis of both Illumina and long reads single molecule sequencing data; the derived transcriptomes will be compared, annotated and used as reference for quantifying transcript expression, leveraging on Bioconductor tools for differential expression analysis. Additional methods will be explored for characterising the assembled transcriptome and revealing biological findings.

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.

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

RNA-Seq technology has been transformative in our ability to explore gene content and gene expression in all realms of biology, and de novo transcriptome assembly has enabled opportunities to expand transcriptome analysis to non-model organisms.

This course provides an overview of modern applications of transcriptome sequencing and popular tools, and algorithms, for exploring transcript reconstruction and expression analysis in a genome-free manner.

Attendees will perform quality assessment and upstream analysis of both Illumina and long reads single molecule sequencing data; the derived transcriptomes will be compared, annotated and used as reference for quantifying transcript expression, leveraging on Bioconductor tools for differential expression analysis. Additional methods will be explored for characterising the assembled transcriptome and revealing biological findings.

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.

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

RNA-Seq technology has been transformative in our ability to explore gene content and gene expression in all realms of biology, and de novo transcriptome assembly has enabled opportunities to expand transcriptome analysis to non-model organisms.

This course provides an overview of modern applications of transcriptome sequencing and popular tools, and algorithms, for exploring transcript reconstruction and expression analysis in a genome-free manner.

Attendees will perform quality assessment and upstream analysis of both Illumina and long reads single molecule sequencing data; the derived transcriptomes will be compared, annotated and used as reference for quantifying transcript expression, leveraging on Bioconductor tools for differential expression analysis. Additional methods will be explored for characterising the assembled transcriptome and revealing biological findings.

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.