We have 6 courses running from the end on June through to the end of July that cover a wide range of topics from beginner to advanced level.
1. ADVANCED PYTHON FOR BIOLOGISTS (APYB03)
2. MICROBIOME DATA ANALYSIS USING QIIME2 (MBQM01)
3. BIOACOUSTICS FOR ECOLOGISTS: HARDWARE, SURVEY DESIGN AND DATA ANALYSIS (BIAC01)
4. INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING USING R (IBHM03)
5. ANALYSING ENVIRONMENTAL ADAPTATION USING LANDSCAPE GENETICS (EDAP01)
6. INTRODUCTION TO SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R (ISPE01)
1. June 17th – 21st 2019
ADVANCED PYTHON FOR BIOLOGISTS (APYB03)
Glasgow, Scotland, Dr. Martin Jones
Python is a dynamic, readable language that is a popular platform for all types of bioinformatics work, from simple one-off scripts to large, complex software projects. This workshop is aimed at people who already have a basic knowledge of Python and are interested in using the language to tackle larger problems. In it, we will look in detail at the parts of the language which are particularly useful in scientific programming, and at the tools Python offers for making development faster and easier. The course will use examples and exercises drawn from various aspects of bioinformatics work. After completing the workshop, students should be in a position to (1) take advantage of the advanced language features in their own programs and (2) use appropriate tools when developing software programs.
2. June 24th – 28th 2019
MICROBIOME DATA ANALYSIS USING QIIME2 (MBQM01)
Glasgow, Scotland, Dr. Yoshiki Vazquez Baeza, Dr. Antonio Gonzalez Pena
This course will provide a theoretical, analytical and practical introduction to QIIME 2 (canonically pronounced ‘chime two’), which stands for Quantitative Insights into Microbial Ecology 2, and Qiita (canonically pronounced ‘cheetah’), a multiomics and multi-study online tool. QIIME 2 and Qiita are open source software packages for comparison and analysis of microbial communities, primarily based on high-throughput amplicon sequencing data (such as SSU rRNA) generated on a variety of platforms, but also supporting analysis of other types of data (such as shotgun metagenomic, metabolomics or proteomics). The main Qiita deployment (http://qiita.microbio.me/) allows users to manage and analyze large studies, their metadata and the multiple data types generated from the same samples. Additionally, it allows users to combine their samples with other published and public studies available in the system. QIIME 2 is a stand-alone environment for the analysis of individual microbiome data sets that can be used on your laptop, university computational resources, and cloud computing resources.
3. July 1st – 5th 2019
BIOACOUSTICS FOR ECOLOGISTS: HARDWARE, SURVEY DESIGN AND DATA ANALYSIS (BIAC01)
Glasgow, Scotland, Dr. Paul Howden-Leach
This course will introduce and explain the different applications for bioacoustics to answer ecological questions. Starting with a detailed overview of the correct and most efficient methods of data collecting in the field, this course will then go on to show delegates cutting edge methods for analysing and interpreting different types of bioacoustic data.
By the end of this 5-day practical course, attendees will have the capacity to set up and deploy recording devices, download acoustic data, how to analyse this data and report the results.
Bioacoustic methods are becoming increasingly recognised as a valuable approach for ecological surveying. Bioacoustics can be used to effectively replace some current techniques whilst increasing the quality of the data collected or can be used in unison to compliment them. They are particularly useful for developing long-term, permanent datasets that can be independently reviewed, particularly for rare species with low detectability, or when working in difficult environments.
The course will provide a practical introduction to bioacoustics methods, with a mix of lectures and practical workshops, and some optional fieldwork. It will start with a basic introduction to sound and recording theory, before developing hands-on skills in setting-up and deploying a range of acoustic and ultrasonic audio recorders.
4. July 8th – 12th 2019
INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING USING R (IBHM03)
Glasgow, Scotland, Dr. Andrew Parnell
This course will cover introductory hierarchical modelling for real-world data sets from a Bayesian perspective. These methods lie at the forefront of statistics research and are a vital tool in the scientist’s toolbox. The course focuses on introducing concepts and demonstrating good practice in hierarchical models. All methods are demonstrated with data sets which participants can run themselves. Participants will be taught how to fit hierarchical models using the Bayesian modelling software Jags and Stan through the R software interface. The course covers the full gamut from simple regression models through to full generalised multivariate hierarchical structures. A Bayesian approach is taken throughout, meaning that participants can include all available information in their models and estimates all unknown quantities with uncertainty. Participants are encouraged to bring their own data sets for discussion with the course tutors.
5. July 15th – 19th 2019
ANALYSING ENVIRONMENTAL ADAPTATION USING LANDSCAPE GENETICS (EDAP01)
Glasgow, Dr. Matt Fitzpatrick
Local adaptation to climate and other environmental drivers increasingly is being studied at the molecular level using high-throughput sequencing methods, with applications spanning both model and non-model organisms. At the same time, statistical tools for modeling and mapping patterns of biodiversity have seen increasing application, including to the challenge of understanding the drivers of spatial variation in adaptive genomic variation and mapping these patterns under current and future climate. This 5-day course will provide the skill set necessary to analyze sequence data for signatures of natural selection and to apply spatial modeling techniques to these patterns to quantify and map population-level genetic variation using two spatial modelling algorithms – Generalized Dissimilarity Modelling (GDM) and Gradient Forest (GF).
The course will include introductory lectures, instruction on using the Linux command line for manipulation of genomic data, guided computer coding in R, and exercises for the participants, with an emphasis on visualization and reproducible workflows. Portions of each day will be allotted for students to work through their own datasets with the instructors.
This course is intended for research scientists, postdoctoral researchers, and graduate students interested in learning how to analyze genomic data for signals of adaptation using population genetic tools and the application of spatial modeling understanding and mapping landscape genomic patterns in R.
After successfully completing this course students will:
• Understand the theory and techniques for detecting signals of natural selection using genomic data, focusing on multi-population and landscape approaches
• Understand the statistical underpinnings of spatial modeling methods (GDM and GF) for analyzing and mapping adaptive genomic variation
• Be able to develop, evaluate and apply GDM and GF for quantifying and mapping spatial genetic patterns
• Estimate population-level vulnerability to climate change
• Students are highly encouraged to bring their own data to the course.
6. July 29th – August 2nd 2019
INTRODUCTION TO SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R (ISPE01)
Glasgow, Scotland, Dr. Jakub Nowosad
The aim of the course is to introduce you to a spatial data processing, analysis, and visualization capabilities of the R programming language. It will teach a range of techniques using a mixture of lectures, computer exercises and case studies.
By the end of the course participants should:
• Understand the basic concepts of spatial data analysis
• Know R’s spatial capabilities
• Understand how to import a range of spatial data sources into R
• Be confident with using R’s command-line interface (CLI) for spatial data processing
• Be able to perform a range of attribute operations (e.g. subsetting and joining), spatial operations (e.g. distance relations, topological relations), and geometry operations (e.g. clipping, aggregations)
• Understand coordinate reference systems (CRSs), be able to decide which CRS to use, and how to reproject spatial data
• Know how to visualize the results of a spatial analysis in the form of static and interactive maps
• Have the confidence to apply spatial analysis skills to their own projects
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Check out our sister sites,
www.PRstatistics.com (Ecology and Life Sciences)
www.PRstatistics.com/consultancy (Statistical and bioinformatics consultancy in all fields)
www.PRinformatics.com (Bioinformatics and data science)
www.PSstatistics.com (Behaviour and cognition)
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