Introduction to bioinformatics using Linux (IBUL02)
16 October 2017 - 20 October 2017£250 - £525
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.
This workshop is aimed at researchers and technical workers with a background in biology who want to learn to use the Linux operating system and the command line environment. No previous experience of Linux is required.
We offer COURSE ONLY and ACCOMMODATION PACKAGES;
• COURSE ONLY – Includes lunch and refreshments.
• ACCOMMODATION PACKAGE (to be purchased in addition to the course only option) – Includes breakfast, lunch, dinner, refreshments, minibus to and from meeting point and accommodation. Accommodation is multiple occupancy (max 3 people) single sex en-suite rooms. Arrival Sunday 15th October and departure Friday 20th October PM.
To book ‘COURSE ONLY’ with the option to add the additional ‘ACCOMMODATION PACKAGE’ please scroll to the bottom of this page.
Other payment options are available please email firstname.lastname@example.org
Cancellation policy: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact email@example.com Failure to attend will result in the full cost of the course being charged. In the unfortunate event that PRinformatics must cancel this course due to unforeseen circumstances a full refund for the course will be credited. However PRinformatics cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.
The workshop is delivered over nine half-day sessions (see the detailed curriculum below). Each session consists of roughly a one hour lecture followed by two hours of practical exercises, with breaks at the organizer’s discretion. There will also be plenty of time for students to discuss their own problems and data.
Assumed quantitative knowledge
Students should have enough biological background to appreciate the examples and exercise problems, and have at least some interest in working with DNA sequence data.
Assumed computer background
No previous computer skills are necessary, as we will introduce Linux starting with the very basics.
Equipment and software requirements
A laptop/personal computer with a Linux virtual machine installed (we will distribute instructions for downloading and installing the virtual machine before the course starts).
It is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) internet access may not always be available.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK firstname.lastname@example.org
Meet at the Tullie Inn, Balloch at approximately 18:30 before being taken by minibus to SCENE (Download directions PDF).
Monday 16th – Classes from 09:00 to 17:00
Module 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 file system 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.
Module 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
Module 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.
Module 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
Module 5: 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.
Module 6: – 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
Module 7: 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.
Module 18: 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
Module 9: 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 19th is reserved for finishing off the next-gen workflow exercise, working on your own datasets, or leaving early for travel.