Bioinformatics course timetable
June 2024
| Fri 21 |
In this course you will acquire practical skills in RNA-seq data analysis. You will learn about quality control, alignment, and quantification of gene expression against a reference transcriptome. Additionally, you will learn to conduct downstream analysis in R, exploring techniques like PCA and clustering for exploratory analysis. The course also covers differential expression analysis using the DESeq2 R/Bioconductor package. Furthermore, the course covers how to generate visualisations like heatmaps and performing gene set testing to link differential genes with established biological functions or pathways.
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 24 |
In this course you will acquire practical skills in RNA-seq data analysis. You will learn about quality control, alignment, and quantification of gene expression against a reference transcriptome. Additionally, you will learn to conduct downstream analysis in R, exploring techniques like PCA and clustering for exploratory analysis. The course also covers differential expression analysis using the DESeq2 R/Bioconductor package. Furthermore, the course covers how to generate visualisations like heatmaps and performing gene set testing to link differential genes with established biological functions or pathways.
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 25 |
In this course you will acquire practical skills in RNA-seq data analysis. You will learn about quality control, alignment, and quantification of gene expression against a reference transcriptome. Additionally, you will learn to conduct downstream analysis in R, exploring techniques like PCA and clustering for exploratory analysis. The course also covers differential expression analysis using the DESeq2 R/Bioconductor package. Furthermore, the course covers how to generate visualisations like heatmaps and performing gene set testing to link differential genes with established biological functions or pathways.
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 26 |
This workshop focuses on expression proteomics, which aims to characterise the protein diversity and abundance in a particular system. You will learn about the bioinformatic analysis steps involved when working with these kind of data, in particular several dedicated proteomics Bioconductor packages, part of the R programming language. We will use real-world datasets obtained from label free quantitation (LFQ) as well as tandem mass tag (TMT) mass spectrometry. We cover the basic data structures used to store and manipulate protein abundance data, how to do quality control and filtering of the data, as well as several visualisations. Finally, we include statistical analysis of differential abundance across sample groups (e.g. control vs. treated) and further evaluation and biological interpretation of the results via gene ontology analysis. By the end of this workshop you should have the skills to make sense of expression proteomics data, from start to finish.
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 27 |
This workshop focuses on expression proteomics, which aims to characterise the protein diversity and abundance in a particular system. You will learn about the bioinformatic analysis steps involved when working with these kind of data, in particular several dedicated proteomics Bioconductor packages, part of the R programming language. We will use real-world datasets obtained from label free quantitation (LFQ) as well as tandem mass tag (TMT) mass spectrometry. We cover the basic data structures used to store and manipulate protein abundance data, how to do quality control and filtering of the data, as well as several visualisations. Finally, we include statistical analysis of differential abundance across sample groups (e.g. control vs. treated) and further evaluation and biological interpretation of the results via gene ontology analysis. By the end of this workshop you should have the skills to make sense of expression proteomics data, from start to finish.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
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July 2024
| Mon 1 |
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. Additional information
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| Tue 2 |
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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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. Additional information
|
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| Wed 3 |
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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| Thu 4 |
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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| Fri 5 |
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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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 and cover the bioinformatic steps in processing ChIP-seq data. We will then introduce the use of the graphical program SeqMonk to explore and visualise your data. Finally, you will perform peak calling and perform differential enrichment analysis.
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 8 |
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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| Tue 9 |
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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| Wed 10 |
Core Statistics using R (IN-PERSON)
Finished
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
|
| Thu 11 |
Core Statistics using R (IN-PERSON)
Finished
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
|
| Fri 12 |
Core Statistics using R (IN-PERSON)
Finished
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
|
| Mon 15 |
This course gives an introduction to linear mixed effects models, also called multi-level models or hierarchical models, for the purposes of using them in your own research or studies. We emphasise the practical skills and key concepts needed to work with these models, using applied examples and real datasets. After completing the course, you should have:
Please note that this course builds on knowledge of linear modelling, therefore should not be considered a general introduction to statistical modelling.
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 16 |
This course gives an introduction to linear mixed effects models, also called multi-level models or hierarchical models, for the purposes of using them in your own research or studies. We emphasise the practical skills and key concepts needed to work with these models, using applied examples and real datasets. After completing the course, you should have:
Please note that this course builds on knowledge of linear modelling, therefore should not be considered a general introduction to statistical modelling.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
|
| Thu 18 |
Many experimental designs end up producing lists of hits, usually based around genes or transcripts. Sometimes these lists are small enough that they can be examined individually, but often it is useful to do a more structured functional analysis to try to automatically determine any interesting biological themes which turn up in the lists. This course looks at the various software packages, databases and statistical methods which may be of use in performing such an analysis. As well as being a practical guide to performing these types of analysis the course will also look at the types of artefacts and bias which can lead to false conclusions about functionality and will look at the appropriate ways to both run the analysis and present the results for publication.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
|
| Mon 22 |
This comprehensive course equips you with essential skills and knowledge in bacterial genomics analysis, primarily using Illumina-sequenced samples. You'll gain an understanding of how to select the most appropriate analysis workflow, tailored to the genome diversity of a given bacterial species. Through hands-on training, you'll apply both de novo assembly and reference-based mapping approaches to obtain bacterial genomes for your isolates. You will apply standardised workflows for genome assembly and annotation, including quality assessment criteria to ensure the reliability of your results. Along with typing bacteria using methods such as MLST, you'll learn how to construct phylogenetic trees using whole genome and core genome alignments, enabling you to explore the evolutionary relationships among bacterial isolates. You’ll extend this to estimate a time-scaled phylogeny using a starting phylogenetic tree. Lastly, you'll apply methods to detect antimicrobial resistance genes. As examples we will use Mycobacterium tuberculosis, Staphylococcus aureus and Streptococcus pneumoniae, allowing you to become well-equipped to conduct bacterial genomics analyses on a range of species.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
|
| Tue 23 |
This comprehensive course equips you with essential skills and knowledge in bacterial genomics analysis, primarily using Illumina-sequenced samples. You'll gain an understanding of how to select the most appropriate analysis workflow, tailored to the genome diversity of a given bacterial species. Through hands-on training, you'll apply both de novo assembly and reference-based mapping approaches to obtain bacterial genomes for your isolates. You will apply standardised workflows for genome assembly and annotation, including quality assessment criteria to ensure the reliability of your results. Along with typing bacteria using methods such as MLST, you'll learn how to construct phylogenetic trees using whole genome and core genome alignments, enabling you to explore the evolutionary relationships among bacterial isolates. You’ll extend this to estimate a time-scaled phylogeny using a starting phylogenetic tree. Lastly, you'll apply methods to detect antimicrobial resistance genes. As examples we will use Mycobacterium tuberculosis, Staphylococcus aureus and Streptococcus pneumoniae, allowing you to become well-equipped to conduct bacterial genomics analyses on a range of species.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
|
| Wed 24 |
This comprehensive course equips you with essential skills and knowledge in bacterial genomics analysis, primarily using Illumina-sequenced samples. You'll gain an understanding of how to select the most appropriate analysis workflow, tailored to the genome diversity of a given bacterial species. Through hands-on training, you'll apply both de novo assembly and reference-based mapping approaches to obtain bacterial genomes for your isolates. You will apply standardised workflows for genome assembly and annotation, including quality assessment criteria to ensure the reliability of your results. Along with typing bacteria using methods such as MLST, you'll learn how to construct phylogenetic trees using whole genome and core genome alignments, enabling you to explore the evolutionary relationships among bacterial isolates. You’ll extend this to estimate a time-scaled phylogeny using a starting phylogenetic tree. Lastly, you'll apply methods to detect antimicrobial resistance genes. As examples we will use Mycobacterium tuberculosis, Staphylococcus aureus and Streptococcus pneumoniae, allowing you to become well-equipped to conduct bacterial genomics analyses on a range of species.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
|
| Thu 25 |
This comprehensive course equips you with essential skills and knowledge in bacterial genomics analysis, primarily using Illumina-sequenced samples. You'll gain an understanding of how to select the most appropriate analysis workflow, tailored to the genome diversity of a given bacterial species. Through hands-on training, you'll apply both de novo assembly and reference-based mapping approaches to obtain bacterial genomes for your isolates. You will apply standardised workflows for genome assembly and annotation, including quality assessment criteria to ensure the reliability of your results. Along with typing bacteria using methods such as MLST, you'll learn how to construct phylogenetic trees using whole genome and core genome alignments, enabling you to explore the evolutionary relationships among bacterial isolates. You’ll extend this to estimate a time-scaled phylogeny using a starting phylogenetic tree. Lastly, you'll apply methods to detect antimicrobial resistance genes. As examples we will use Mycobacterium tuberculosis, Staphylococcus aureus and Streptococcus pneumoniae, allowing you to become well-equipped to conduct bacterial genomics analyses on a range of species.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
|
| Fri 26 |
This comprehensive course equips you with essential skills and knowledge in bacterial genomics analysis, primarily using Illumina-sequenced samples. You'll gain an understanding of how to select the most appropriate analysis workflow, tailored to the genome diversity of a given bacterial species. Through hands-on training, you'll apply both de novo assembly and reference-based mapping approaches to obtain bacterial genomes for your isolates. You will apply standardised workflows for genome assembly and annotation, including quality assessment criteria to ensure the reliability of your results. Along with typing bacteria using methods such as MLST, you'll learn how to construct phylogenetic trees using whole genome and core genome alignments, enabling you to explore the evolutionary relationships among bacterial isolates. You’ll extend this to estimate a time-scaled phylogeny using a starting phylogenetic tree. Lastly, you'll apply methods to detect antimicrobial resistance genes. As examples we will use Mycobacterium tuberculosis, Staphylococcus aureus and Streptococcus pneumoniae, allowing you to become well-equipped to conduct bacterial genomics analyses on a range of species.
If you do not have a University of Cambridge Raven account please book or register your interest here. Additional information
|
