Bioinformatics course timetable
July 2023
| Tue 18 |
THIS COURSE IS NOT RETURNING IN ITS CURRENT FORM. PLEASE CHECK OUR WEBSITE FOR MORE INFORMATION. Machine learning gives computers the ability to learn without being explicitly programmed. It encompasses a broad range of approaches to data analysis with applicability across the biological sciences. Lectures will introduce commonly used algorithms and provide insight into their theoretical underpinnings. In the practicals students will apply these algorithms to real biological data-sets using the R language and environment. 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. |
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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. |
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| Wed 19 |
THIS COURSE IS NOT RETURNING IN ITS CURRENT FORM. PLEASE CHECK OUR WEBSITE FOR MORE INFORMATION. Machine learning gives computers the ability to learn without being explicitly programmed. It encompasses a broad range of approaches to data analysis with applicability across the biological sciences. Lectures will introduce commonly used algorithms and provide insight into their theoretical underpinnings. In the practicals students will apply these algorithms to real biological data-sets using the R language and environment. 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. |
| Thu 20 |
Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a method used to identify binding sites for transcription factors, histone modifications and other DNA-binding proteins across the genome. In this course, we will cover the fundamentals of ChIP-seq data analysis, from raw data to downstream applications. We will start with an introduction to ChIP-seq methods, including important considerations when designing your experiments. We will cover the bioinformatic steps in a standard ChIP-seq analysis workflow, covering raw data quality control, trimming/filtering, mapping, duplicate removal, post-mapping quality control, peak calling and peak annotation. We will discuss metrics used for quality assessment of the called peaks when multiple replicates are available, as well as the analysis of differential binding across sample groups. Throughout the course we will also cover tools and packages that can be used for visualising and exploring your results.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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September 2023
| Mon 11 |
This course provides a practical introduction to the writing of Python programs for the complete novice. Participants are lead through the core concepts of Python including Python syntax, data structures and reading/writing files. These are illustrated by a series of example programs. Upon completion of the course, participants will be able to write simple Python programs.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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| Thu 14 |
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.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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| Fri 15 |
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.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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| Mon 18 |
This week-long course is aimed at people with little or no experience using statistical analyses in research. It introduces participants to core concepts in statistics and experimental design, aimed at ensuring that the resulting data is able to address the research question using appropriate statistical methods. The interactive course gives participants a hands-on, applied foundation in statistical data analysis and experimental design. Group exercises and discussions are combined with short lectures that introduce key theoretical concepts. Computational methods are used throughout the course, using the R programming language. Formative assessment exercises allow participants to test their understanding throughout the course and encourage questions and critical thinking. By the end of the course participants will be able to critically evaluate and design effective research questions, linking experimental design concepts to subsequent statistical analyses. It will allow participants to make informed decisions on which statistical tests are most appropriate to their research questions. The course will provide a solid grounding for further development of applied statistical competencies. If you do not have a University of Cambridge Raven account please book or register your interest here. |
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This course provides a practical introduction to the writing of Python programs for the complete novice. Participants are lead through the core concepts of Python including Python syntax, data structures and reading/writing files. These are illustrated by a series of example programs. Upon completion of the course, participants will be able to write simple Python programs.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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| Tue 19 |
This week-long course is aimed at people with little or no experience using statistical analyses in research. It introduces participants to core concepts in statistics and experimental design, aimed at ensuring that the resulting data is able to address the research question using appropriate statistical methods. The interactive course gives participants a hands-on, applied foundation in statistical data analysis and experimental design. Group exercises and discussions are combined with short lectures that introduce key theoretical concepts. Computational methods are used throughout the course, using the R programming language. Formative assessment exercises allow participants to test their understanding throughout the course and encourage questions and critical thinking. By the end of the course participants will be able to critically evaluate and design effective research questions, linking experimental design concepts to subsequent statistical analyses. It will allow participants to make informed decisions on which statistical tests are most appropriate to their research questions. The course will provide a solid grounding for further development of applied statistical competencies. If you do not have a University of Cambridge Raven account please book or register your interest here. |
| Wed 20 |
This week-long course is aimed at people with little or no experience using statistical analyses in research. It introduces participants to core concepts in statistics and experimental design, aimed at ensuring that the resulting data is able to address the research question using appropriate statistical methods. The interactive course gives participants a hands-on, applied foundation in statistical data analysis and experimental design. Group exercises and discussions are combined with short lectures that introduce key theoretical concepts. Computational methods are used throughout the course, using the R programming language. Formative assessment exercises allow participants to test their understanding throughout the course and encourage questions and critical thinking. By the end of the course participants will be able to critically evaluate and design effective research questions, linking experimental design concepts to subsequent statistical analyses. It will allow participants to make informed decisions on which statistical tests are most appropriate to their research questions. The course will provide a solid grounding for further development of applied statistical competencies. If you do not have a University of Cambridge Raven account please book or register your interest here. |
| Thu 21 |
This week-long course is aimed at people with little or no experience using statistical analyses in research. It introduces participants to core concepts in statistics and experimental design, aimed at ensuring that the resulting data is able to address the research question using appropriate statistical methods. The interactive course gives participants a hands-on, applied foundation in statistical data analysis and experimental design. Group exercises and discussions are combined with short lectures that introduce key theoretical concepts. Computational methods are used throughout the course, using the R programming language. Formative assessment exercises allow participants to test their understanding throughout the course and encourage questions and critical thinking. By the end of the course participants will be able to critically evaluate and design effective research questions, linking experimental design concepts to subsequent statistical analyses. It will allow participants to make informed decisions on which statistical tests are most appropriate to their research questions. The course will provide a solid grounding for further development of applied statistical competencies. If you do not have a University of Cambridge Raven account please book or register your interest here. |
| Fri 22 |
This week-long course is aimed at people with little or no experience using statistical analyses in research. It introduces participants to core concepts in statistics and experimental design, aimed at ensuring that the resulting data is able to address the research question using appropriate statistical methods. The interactive course gives participants a hands-on, applied foundation in statistical data analysis and experimental design. Group exercises and discussions are combined with short lectures that introduce key theoretical concepts. Computational methods are used throughout the course, using the R programming language. Formative assessment exercises allow participants to test their understanding throughout the course and encourage questions and critical thinking. By the end of the course participants will be able to critically evaluate and design effective research questions, linking experimental design concepts to subsequent statistical analyses. It will allow participants to make informed decisions on which statistical tests are most appropriate to their research questions. The course will provide a solid grounding for further development of applied statistical competencies. If you do not have a University of Cambridge Raven account please book or register your interest here. |
| Wed 27 |
Recent technological advances have made it possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing (scRNA-seq). Even though scRNA-seq makes it possible to address problems that are intractable with bulk RNA-seq data, analysing scRNA-seq is also more challenging. In this course we will be surveying the existing problems as well as the available computational and statistical frameworks available for the analysis of scRNA-seq.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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| Thu 28 |
Recent technological advances have made it possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing (scRNA-seq). Even though scRNA-seq makes it possible to address problems that are intractable with bulk RNA-seq data, analysing scRNA-seq is also more challenging. In this course we will be surveying the existing problems as well as the available computational and statistical frameworks available for the analysis of scRNA-seq.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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| Fri 29 |
Recent technological advances have made it possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing (scRNA-seq). Even though scRNA-seq makes it possible to address problems that are intractable with bulk RNA-seq data, analysing scRNA-seq is also more challenging. In this course we will be surveying the existing problems as well as the available computational and statistical frameworks available for the analysis of scRNA-seq.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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October 2023
| Fri 13 |
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.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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| Wed 18 |
The Unix shell (command line) is a powerful and essential tool for modern researchers, in particular those working in computational disciplines such as bioinformatics and large-scale data analysis. In this course we will explore the basic structure of the Unix operating system and how we can interact with it using a basic set of commands. You will learn how to navigate the filesystem, manipulate text-based data and combine multiple commands to quickly extract information from large data files. You will also learn how to write scripts, use programmatic techniques to automate task repetition, and communicate with remote servers (such as High Performance Computing servers).
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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| Thu 19 |
The Unix shell (command line) is a powerful and essential tool for modern researchers, in particular those working in computational disciplines such as bioinformatics and large-scale data analysis. In this course we will explore the basic structure of the Unix operating system and how we can interact with it using a basic set of commands. You will learn how to navigate the filesystem, manipulate text-based data and combine multiple commands to quickly extract information from large data files. You will also learn how to write scripts, use programmatic techniques to automate task repetition, and communicate with remote servers (such as High Performance Computing servers).
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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| Fri 20 |
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.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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| Wed 25 |
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. Additional information
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| Thu 26 |
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. Additional information
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| Fri 27 |
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. Additional information
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This award winning course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. 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:
R is an open source programming language so all of the software we will use in the course is free. 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 and moreover know when, and when not, to apply these techniques.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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November 2023
| Fri 3 |
This award winning course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. 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:
R is an open source programming language so all of the software we will use in the course is free. 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 and moreover know when, and when not, to apply these techniques.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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