Introduction to Bioinformatics using LINUX http://www.prstatistics.com/course/introduction-to-bioinformatics-using- linux-ibul02/
Instructor: Dr. Martin Jones This course will run from 16th - 20th October at SCENE (the Scottish Centre for Ecology and the Natural Environment), Loch Lomond National Park, Glasgow. Course overview: Most high-throughput bioinformatics work these days takes place on the Linux command line. The programs which do the majority of the computational heavy lifting ― genome assemblers, read mappers, and annotation tools ― are designed to work best when used with a command-line interface. Because the command line can be an intimidating environment, many biologists learn the bare minimum needed to get their analysis tools working. This means that they miss out on the power of Linux to customize their environment and automate many parts of the bioinformatics workflow. This course will introduce the Linux command line environment from scratch and teach students how to make the most of its tools to achieve a high level of productivity when working with biological data. Availability: 15 places total. Course programme Monday 16th �C Classes from 09:00 to 17:00 (approximately) ● Session 1 - The design of Linux In the first session we briefly cover the design of Linux: how is it different from Windows/OSX and how is it best used? We'll then jump straight onto the command line and learn about the layout of the Linux filesystem and how to navigate it. We'll describe Linux's file permission system (which often trips up beginners), how paths work, and how we actually run programs on the command line. We'll learn a few tricks for using the command line more efficiently, and how to deal with programs that are misbehaving. We'll finish this session by looking at the built in help system and how to read and interpret manual pages. ● Session 2 - System management We'll first look at a few command line tools for monitoring the status of the system and keeping track of what's happening to processor power, memory, and disk space. We'll go over the process of installing new software from the built in repositories (which is easy) and from source code downloads (which is trickier). We'll also introduce some tools for benchmarking software (measuring the time/memory requirements of processing large datasets). Tuesday 17th - Classes from 09:00 to 17:00 (approximately) ● Session 3 - Manipulating tabular data Many data types we want to work with in bioinformatics are stored as tabular plain text files, and here we learn all about manipulating tabular data on the command line. We'll start with simple things like extracting columns, filtering and sorting, searching for text before moving on to more complex tasks like searching for duplicated values, summarizing large files, and combining simple tools into long commands. ● Session 4 - Constructing pipelines In this session we will look at the various tools Linux has for constructing pipelines out of individual commands. Aliases, shell redirection, pipes, and shell scripting will all be introduced here. We'll also look at a couple of specific tools to help with running tools on multiple processors, and for monitoring the progress of long running tasks. Wednesday 18th - Classes from 09:00 to 17:00 (approximately) ● Session 5 �C EMBOSS EMBOSS is a suite of bioinformatics command-line tools explicitly designed to work in the Linux paradigm. We'll get an overview of the different sequence data formats that we might expect to work with, and put what we learned about shell scripting to biological use by building a pipeline to compare codon usage across two collections of DNA sequences. ● Session 6 �C Using a Linux server Often in bioinformatics we'll be working on a Linux server rather than our own computer― typically because we need access to more computing power, or to specialized tools and datasets. In this session we'll learn how to connect to a Linux server and how to manage sessions. We'll also consider the various ways of moving data to and from a server from your own computer, and finish with a discussion of the considerations we have to make when working on a shared computer. Thursday 19th - Classes from 09:00 to 17:00 (approximately) ● Session 7 �C Combining methods In the next two sessions ― i.e. one full day ― we'll put everything we have learned together and implement a workflow for next-gen sequence analysis. In this first session we'll carry out quality control on some paired-end Illumina data and map these reads to a reference genome. We'll then look at various approaches to automating this pipeline, allowing us to quickly do the same for a second dataset. ● Session 8 �C Combining methods The second part of the next-gen workflow is to call variants to identify SNPs between our two samples and the reference genome. We'll look at the VCF file format and figure out how to filter SNPs for read coverage and quality. By counting the number of SNPs between each sample and the reference we will try to figure out something about the biology of the two samples. We'll attempt to automate this analysis in various ways so that we could easily repeat the pipeline for additional samples. Friday 20th - Classes from 09:00 to 16:00 (approximately) ● Session 9 �C Customization Part of the Linux design is that everything can be customized. This can be intimidating at first but, given that bioinformatics work is often fairly repetitive, can be used to good effect. Here we'll learn about environment variables, custom prompts, soft links, and ssh configuration ― a collection of tools with modest capabilities, but which together can make life on the command line much more pleasant. In this last session there will also be time to continue working on the next-gen sequencing pipeline. The afternoon of Friday 20th is reserved for finishing off the next-gen workflow exercise, working on your own datasets, or leaving early for travel. Please send inquiries to [email protected] or visit the website www.prstatistics.com Please feel free to distribute this information anywhere you think suitable. Upcoming courses - email for details [email protected] 1. ADVANCED PYTHON FOR BIOLOGISTS (February 2017) #APYB http://www.prstatistics.com/course/advanced-python-biologists-apyb01/ 2. STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR USING R (February 2017) #SIMM http://www.prstatistics.com/course/stable-isotope-mixing-models-using-r- simm03/ 3. NETWORK ANAYLSIS FOR ECOLOGISTS USING R (March 2017) #NTWA http://www.prstatistics.com/course/network-analysis-ecologists-ntwa01/ 4. ADVANCES IN MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA (April 2017) #MVSP http://www.prstatistics.com/course/advances-in-spatial-analysis-of- multivariate-ecological-data-theory-and-practice-mvsp02/ 5. INTRODUCTION TO STATISTICS AND R FOR BIOLOGISTS (April 2017) #IRFB http://www.prstatistics.com/course/introduction-to-statistics-and-r-for- biologists-irfb02/ 6. ADVANCING IN STATISTICAL MODELLING USING R (April 2017) #ADVR http://www.prstatistics.com/course/advancing-statistical-modelling-using-r- advr05/ 7. GEOMETRIC MORPHOMETRICS USING R (June 2017) #GMMR http://www.prstatistics.com/course/geometric-morphometrics-using-r-gmmr01/ 8. MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA (June 2017) #MASE http://www.prstatistics.com/course/multivariate-analysis-of-spatial- ecological-data-using-r-mase01/ 9. TIME SERIES MODELS FOR ECOLOGISTS USING R (JUNE 2017 (#TSME) 10. BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS (July 2017) #BIGB http://www.prstatistics.com/course/bioinformatics-for-geneticists-and- biologists-bigb02/ 11. SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R (August 2017) #SPAE http://www.prstatistics.com/course/spatial-analysis-ecological-data-using-r- spae05/ 12. ECOLOGICAL NICHE MODELLING (October 2017) #ENMR http://www.prstatistics.com/course/ecological-niche-modelling-using-r- enmr01/ 13. INTRODUCTION TO BIOINFORMATICS USING LINUX (October 2017) #IBUL http://www.prstatistics.com/course/introduction-to-bioinformatics-using- linux-ibul02/ 14. STRUCTURAL EQUATION MODELLING FOR ECOLOGISTS AND EVOLUTIONARY BIOLOGISTS (October 2017) #SEMR 15. GENETIC DATA ANALYSIS USING R (October 2017 TBC) #GDAR 16. LANDSCAPE (POPULATION) GENETIC DATA ANALYSIS USING R (November 2017 TBC) #LNDG http://www.prstatistics.com/course/landscape-genetic-data-analysis-using-r- lndg02/ 17. APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS (November 2017) #ABME http://www.prstatistics.com/course/applied-bayesian-modelling-ecologists- epidemiologists-abme03/ 18. INTRODUCTION TO METHODS FOR REMOTE SENSING (November 2017) #IRMS 19. INTRODUCTION TO PYTHON FOR BIOLOGISTS (November 2017) #IPYB http://www.prstatistics.com/course/introduction-to-python-for-biologists- ipyb04/ 20. DATA VISUALISATION AND MANIPULATION USING PYTHON (December 2017) #DVMP http://www.prstatistics.com/course/data-visualisation-and-manipulation- using-python-dvmp01/ 21. ADVANCING IN STATISTICAL MODELLING USING R (December 2017) #ADVR http://www.prstatistics.com/course/advancing-statistical-modelling-using-r- advr07/ 22. INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING (January 2018) #IBHM http://www.prstatistics.com/course/introduction-to-bayesian-hierarchical- modelling-using-r-ibhm02/ 23. PHYLOGENETIC DATA ANALYSIS USING R (TBC) #PHYL Oliver Hooker PhD. PR statistics 3/1 128 Brunswick Street Glasgow G1 1TF +44 (0) 7966500340 www.prstatistics.com www.prstatistics.com/organiser/oliver-hooker/
