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Bioinformatics is an increasingly important skill for biological scientists. Many bioinformatic tools can only be run on Unix based operating systems. This course is aimed at beginners and novices to the command line; it will give an overview of fundamental unix commands, directory navigation, and file manipulation. The course will act as a perfect primer for those interested in our other Bioinformatic courses.

There are 3 sequencing technologies that are heavily used for biological research; Illumina, PacBio, and ONT. These three technologies each have their own errors and biases. At the end of this course you will understand these error profiles, and be able to use command line tools to measure sequencing quality and conduct quality control.

”Really enjoyed the MCQs to check knowledge particularly when looking at plots that would be easy to glance at without understanding” (Feedback November 2023)

Sequencing of the 16S rRNA gene is a well established method of determining the bacterial taxonomic composition of microbiomes. This has been used for human and animal body sites, soil, sewage, clouds, deserts, permafrost and many other environments. This course will give you the ability to describe the advantages and disadvantages of 16S rRNA sequencing, and analyse 16S rRNA datasets with the QIIME2 bioinformatics platform.

Sequencing DNA barcodes from mixed sources of DNA is an increasingly used way to survey biodiversity, whether analysing dietary content from faecal-derived DNA or monitoring aquatic species from water-derived DNA. This course will give an overview of metabarcoding with different barcoding genes to target particular taxa. Using an example data set we will go from raw sequence data through to assigning taxonomy to the sequence variants.

Sequencing DNA barcodes from mixed sources of DNA is an increasingly used way to survey biodiversity, whether analysing dietary content from faecal-derived DNA or monitoring aquatic species from water-derived DNA. This course gave an overview of metabarcoding with different barcoding genes to target particular taxa. Using an example data set we went from raw sequence data through to assigning taxonomy to the sequence variants.

The course also described the process of sharing, finding, and using publicly available data.

Generating DNA sequence barcodes from mixed sources of DNA is an increasingly-used and efficient way to survey biodiversity, whether monitoring aquatic species by sampling water-derived DNA, characterising communities from soil samples, or analysing diet from faecal-derived DNA. This course provides an overview of metabarcoding with different barcoding genes to target particular taxa, and consists of a technical wet lab component and a bioinformatics component. 

During the wet lab program, we gave an overview of experimental design, headed outside for a field sampling demonstration and returned to the laboratory for a full course on the metabarcoding process. The laboratory techniques included DNA extraction, polymerase chain reaction (PCR), gel electrophoresis, magnetic bead cleaning and quantification. Finally, we outlined the different platforms that are typically used for sequencing.

In the bioinformatics component, we used an example data set to go from raw sequence data through to assigning taxonomy to identified sequence variants. We then conducted typical downstream analyses to quantify and examine differences in diversity.

The workshop was held over two half days and three full days.

There are many unknown and unculturable microbes found in a vast array of different environments. Shotgun metagenomics is an approach to capture all the genetic information in a sample, providing the taxonomic and metabolic information of all present organisms. In this course we will analyse shotgun metagenomic sequencing data from stool samples to compare western and Korean diets. At the end of the course you will be able to quantify the taxonomic composition with Kraken2 & Bracken, and determine the metabolic profile with the bioBakery suite of tools.

This course will introduce participants to a variety of population genomics analyses for large next generation sequencing (NGS) datasets. It will include discussion on single nucleotide polymorphism (SNP) calling and filtering options, an introduction to population genomics statistics, examining genetic structure, and Fst-based analyses such as outlier detection/genomic islands of divergence.

With the proliferation of Next Generation Sequencing, data sets for the study of phylogenetic relationships are getting increasingly larger, and data sets of genome-wide markers from a multitude of taxa are now readily attainable to address questions around the evolution, biogeography and accurate delineation of biodiversity. The tools used to conduct typical phylogenetic analyses have advanced to accommodate these larger data sets, opposed to studies of single genes or a few data partitions or sequence alignments that were common a decade ago. In this workshop, we will use several example data sets and work through quality control of the data, prepare genome-wide SNP data for phylogenetic analysis, construct and evaluate phylogenetic trees, date phylogenies, and examine gene tree and species tree conflicts in  delimiting species.

With the advent and improvement of long read sequencing technologies, bacterial genome assembly and annotation is easier and more effective than ever. At the end of this course you will be able to perform genome assembly and polishing of PacBio sequencing followed by annotation with prokka. Additionally you will be able to assess the quality of the genome with contiguity measures from QUAST and genome completeness estimation with BUSCO.

RNA-Seq (AKA whole transcriptomic shotgun sequencing) aims to determine the presence and quantity of RNA in a biological sample at a given moment in time. This allows you to determine the level of expression of these genes and therefore compare these expressions across different sample groups, i.e. Differential Gene Expression (DGE). At the end of this course you will be able to align the transcriptomes of samples to a reference genome to determine gene expression levels. Following this you will be able to carry out Differential Gene Expression analysis to determine which genes are up and down regulated between sample groups.

R is a highly used programming language for visualisation of data and statistical analysis. There are many R packages for the analysis of biological datasets. This course is aimed at beginners and novices to R. It will give an in depth overview of the foundations of R and Rstudio plus introduce data sorting, visualisation and statistical analysis.

Eukaryotic genomes can be large and complex and there have been many recent developments in the tools available for optimal genome assembly. This course will give an overview of long read genome assembly using PacBio HiFi sequencing and scaffolding using Hi-C reads. We will cover initial assessment of the sequencing data, genome assembly and scaffolding, determining the contiguity and completeness of the assembly and also explore visualisation tools.

A lot of different analysis and visualisations can be carried out with community data. This includes taxonomy and functional abundance tables from 16S rRNA and Shotgun metagenomics analysis. This workshop will introduce you to the phyloseq R object; a specialised object containing an abundance, taxonomy, and metadata table. You will learn how to import your data into a phyloseq object, analyse your data and produce bespoke visualisations in R with the packages qiime2R, phyloseq, and microbiome.