Bioinformatics of geneticists and biologists (training course) 19th – 23rd October 2015 

There are still some places available on this course.

The course is being delivered by Dr. Nic Blouin and Dr. Ian Misner

This 5 day course will be held at SCENE (Scottish Centre for Ecology and the Natural Environment), Glasgow, United Kingdom from 19th – 23rd October 2015

The handling of large datasets has become intractable without some level of bioinformatic literacy. Many biologists, especially in the field of genetics, find that there is a steep learning curve to develop the confidence required to explore their genomics datasets effectively. This bioinformatics short course includes a rich collection of hands-on instruction and lectures specifically intended to help novice users become comfortable with a range of tools currently used to analyse next-generation data.  There is no prerequisite for this course other than a willingness to learn and to work hard throughout the week. 

Course timetable:
Day 1: Linux
Linux is taught on the first day, this takes the entire day. Once you get through this portion you will be on your way to completing your own NGS analysis. A workbook has been created for this portion of the course. This is a step by step, or in the case, command-by-command, Linux guide. We complete each command as a class and discuss and review issues along the way. 
Day 2: RNAseq
We will cover two of the more popular tools in this workshop, The Tuxedo package & Trinity. Outcomes: confidence to design effective RNAseq experiments; knowledge of NGS sequencing platforms and their differing applications, ability to analyze Illumina data for quality and contamination; proficiency to implement the Tuxedo package to analyze an RNAseq dataset; create publication ready graphics with cummeRbund and EdgeR.
Day 3: Assembly
Whether you have a reference genome or are working with de novo samples there are some basic tools and practices that we cover to help assist you in your genome project. In this module we will cover the basic metrics you should review when doing assembly as well as best practices to consider in your own project.  Outcomes: take raw reads through a complete assembly process; working knowledge of different assembly issues/challenges; the effect of assembly settings on assembly outcomes.
Day 4: Annotation
We will use MAKER and Blast2GO and annotate the genome we assembled in the assembly module.
Outcomes: understand the differences between functional and structural annotations; train MAKER to improve structural annotations; understand how MAKER improves with more evidence and training; visualize structural annotations; apply functional annotations with Blast2GO.
Day 5: Python
Why Python? In truth it doesn’t matter what coding language you learn but you should learn one. Python has a very straightforward syntax that is easy to understand. In this module we will utilize the clearly explained training examples from Python for Biologists. Outcomes: understand Python language syntax; create scripts to answer biological problems & parse and analyze BLAST outputs using custom Python code.

Fees:
Cost is £540 for the 5 days including lunches and refreshments or £715 for an all-inclusive option which includes the addition of accommodation, breakfast, lunch, dinner and refreshments.

Intended audience: 
Research postgraduates, practicing academics, and environmental professionals in government and industry. 

Teaching format: 
This is principally a hands-on course. Participants will conduct all analyses in this course using their own laptops. Introductory lectures on the concepts will be given as necessary. Participants need to bring their own laptops and should come willing to work diligently to complete all analyses during meeting times. The level of comfort of the class as a whole will determine the pace of the class.

For further details or questions or to register please email [log in to unmask] or visit www.prstatistics.co.uk 
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Additional upcoming courses; GENETIC DATA ANALYSIS USING R; APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS; SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R; ADVANCING IN R; STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR;