skip to navigation skip to content

All Bioinformatics courses

Show:
Show only:

Showing courses 1-10 of 113
Courses per page: 10 | 25 | 50 | 100

Analysis of bulk RNA-seq data Mon 2 Sep 2019   09:30 Finished

The aim of this course is to familiarize the participants with the primary analysis of RNA-seq data.

This course starts with a brief introduction to RNA-seq and discusses quality control issues. Next, we will present the alignment step, quantification of expression and differential expression analysis. For downstream analysis we will focus on tools available through the Bioconductor project for manipulating and analysing bulk RNA-seq.

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.

Analysis of DNA Methylation using Sequencing Wed 20 Nov 2019   09:30 [Places]

This course will cover all aspects of the analysis of DNA methylation using sequencing, including primary analysis, mapping and quality control of BS-Seq data, common pitfalls and complications.

It will also include exploratory analysis of methylation, looking at different methods of quantitation, and a variety of ways of looking more widely at the distribution of methylation over the genome. Finally the course will look at statistical methods to predict differential methylation.

The course will be comprised of a mixture of theoretical lectures and practicals covering a range of different software packages.

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.

This advanced course will cover high-throughput sequencing data processing, ChIP-seq data analysis (including alignment, peak calling), differences in analyses methods for transcription factors (TF) binding and epigenomic datasets, a range of downstream analysis methods for extracting meaningful biology from ChIP-seq data and will provide an introduction to the analysis of open chromatin with ATAC-seq and long-distance interactions with chromosomal conformation capture based Hi-C datasets.

Materials for this course can be found here.

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 provides an introduction to the tools available through the Bioconductor project for manipulating and analysing high-throughput sequencing (HTS) data. We will present workflows for the analysis of ChIP-Seq and RNA-seq data starting from aligned reads in bam format. We will also describe the various resources available through Bioconductor to annotate and visualize HTS data, which can be applied to any type of sequencing experiment.

The course timetable is available here.

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

Analysis of mapped NGS data with SeqMonk Wed 3 Feb 2016   09:30 Finished

SeqMonk is a graphical program for the visualisation and analysis of large mapped sequencing datasets such as ChIP-Seq, RNA-Seq, and BS-Seq.

The program allows you to view your reads against an annotated genome and to quantitate and filter your data to let you identify regions of interest. It is a friendly way to explore and analysis very large datasets.

This course provides an introduction to the main features of SeqMonk and will run through the analysis of a couple of different datasets to show what sort of analysis options it provides.

Further information is available here.

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

Analysis of RNA-seq data with Bioconductor Wed 28 Mar 2018   09:30 Finished

This course provides an introduction to the tools available through the Bioconductor project for manipulating and analysing bulk RNA-seq data. We will present a workflow for the analysis RNA-seq data starting from aligned reads in bam format and producing a list of differentially-expressed genes. We will also describe the various resources available through Bioconductor to annotate, visualise and gain biological insight from the differential expression results.

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

Analysis of single cell RNA-seq data Mon 16 Dec 2019   09:30 [Full]

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.

The course website providing links to the course materials can be found here.

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.

Analysis of small RNA-seq data new Tue 2 May 2017   09:30 Finished

This course focuses on methods for the analysis of small non-coding RNA data obtained from high-throughput sequencing (HTS) applications (small RNA-seq). During the course, approaches to the investigation of all classes of small non-coding RNAs will be presented, in all organisms.

Day 1 will focus on the analysis of microRNAs and day 2 will cover the analysis of other types of small RNAs, including Piwi-interacting (piRNA), small interfering (siRNA), small nucleolar (snoRNA) and tRNA-derived (tsRNA).

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

Through the use of real world examples and the JMP, JMP Pro, and JMP Genomics software, we will cover best practices used in both industry and academia today to visually explore data, plan biological experiments, detect differential expression patterns, find signals in next-generation sequencing data and easily discover statistically appropriate biomarker profiles and patterns.

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.

An Introduction to Machine Learning Wed 2 Oct 2019   09:30 [Full]

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 be aware that the course syllabus is currently being updated following feedback from the last event; therefore the agenda below will be subjected to changes.

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.

[Back to top]