This is a practical course, which shows why programming is a powerful tool in the context of solving biological problems. During the course participants will be taught the Python programming language, a language widely considered both powerful as well as easy to use. This course might be considered as a demonstration how bioinformatics improves biologists lives.

The course is designed and aimed for people without computer science background who want to learn programming.

This course is suited for:

• Researchers dealing with biological data.
• Scientists who would like to learn how to automate everyday tasks and analyse data.
• Managers who want to learn how programming improves workflows and conducting projects.

By the end of the course, participants will be able to write short programs, which will allow them to manipulate, analyse and deal with biological data and present results in a graphical format.

Introduction to the Python programming language

• Why Python?
• Using Python to deal with biological data
• Working with the iPython shell
• Your first programme
• Writing Python scripts
• Importing modules

Working with protein and RNA/DNA sequences

• Finding motives
• Transcription and translation in silico
• Handling sequence alignments

Parsing data in different biological formats

• Parsing FASTA
• Data format conversions

Running biological analyses

• BLAST
• Accessing biological web services

Dealing with biological 3D structures using Python

Python facilitates statistical analysis

Visualizing data

• Creating bar and scatter plots
• Calculating an Area Under Curve (AUC)

Working with .xls and .csv files

• Importing data from and exporting to MS Excel / OpenOffice Calc
• Writing .xls and .csv files

Using Python to create an automated data processing pipeline

This is a practical workshop, which will introduce basics of bioinformatics. It is aimed at people with basic biological background and knowledge in molecular biology, who want to learn how to process and work with biological data using computational tools. It has been designed especially for:

• Researchers working in experimental laboratories on biological macromolecules proteins, RNA and DNA.
• Scientists whose main area of expertise is not biology, but they need to cope with biological data.
• Managers introducing new products (e.g. drugs), who want to broaden their knowledge about nowadays bioinformatics.
• Computer programmers who are going to write programs dealing with biological molecules.

The course is divided into two parts: theoretical and practical. The lecture is interwoven with simple practical exercises, quizzes and tests to make sure that attendees understand the concepts.

During the training we will take you along a guided tour that covers all the basic aspects of biological data analysis. This workshop is an ideal opportunity to get familiar with bioinformatics and its many faces! At the end, you should be able to tackle any basic bioinformatics problem: you will know where to get the required data from, how to obtain relevant information and how basic software and algorithms work. Our aim is to facilitate your understanding of biological data.

By the end of the course you will be able to work with biological data using bioinformatics tools.

• Introduction
• Sequence alignments
• BLAST and its Applications
• Translation tools
• Primer Designing
• Epitope Prediction
• Secondary structure prediction
• Phylogenetic analysis

This course aims to give researchers an understanding of the principles of statistical design and analysis and their relevance to research in a range of scientific disciplines.

It covers some probability and statistical methods, mainly through examples. This training contains around 30% of lectures, 70% of guided quizzes and labs.

In the case of closed course we can tailor the examples and materials to a specific branch (like psychology tests, public sector, biology, genetics, etc...)

In the case of public courses, mixed examples are used.

Though various software is used during this course (Microsoft Excel to SPSS, Statgraphics, etc...) its main focus is on understanding principles and processes guiding research, reasoning and conclusion.

This course can be delivered as a blended course i.e. with homework and assignments.

Scientific Method, Probability & Statistics

• Very short history of statistics
• Why can be "confident" about the conclusions
• Probability and decision making

Preparation for research (deciding "what" and "how")

• The big picture: research is a part of a process with inputs and outputs
• Gathering data
• Questioners and measurement
• What to measure
• Observational Studies
• Design of Experiments
• Analysis of Data and Graphical Methods
• Research Skills and Techniques
• Research Management

Describing Bivariate Data

• Introduction to Bivariate Data
• Values of the Pearson Correlation
• Guessing Correlations Simulation
• Properties of Pearson's r
• Computing Pearson's r
• Restriction of Range Demo
• Variance Sum Law II
• Exercises

Probability

• Introduction
• Basic Concepts
• Conditional Probability Demo
• Gamblers Fallacy Simulation
• Birthday Demonstration
• Binomial Distribution
• Binomial Demonstration
• Base Rates
• Bayes' Theorem Demonstration
• Monty Hall Problem Demonstration
• Exercises

Normal Distributions

• Introduction
• History
• Areas of Normal Distributions
• Varieties of Normal Distribution Demo
• Standard Normal
• Normal Approximation to the Binomial
• Normal Approximation Demo
• Exercises

Sampling Distributions

• Introduction
• Basic Demo
• Sample Size Demo
• Central Limit Theorem Demo
• Sampling Distribution of the Mean
• Sampling Distribution of Difference Between Means
• Sampling Distribution of Pearson's r
• Sampling Distribution of a Proportion
• Exercises

Estimation

• Introduction
• Degrees of Freedom
• Characteristics of Estimators
• Bias and Variability Simulation
• Confidence Intervals
• Exercises

Logic of Hypothesis Testing

• Introduction
• Significance Testing
• Type I and Type II Errors
• One- and Two-Tailed Tests
• Interpreting Significant Results
• Interpreting Non-Significant Results
• Steps in Hypothesis Testing
• Significance Testing and Confidence Intervals
• Misconceptions
• Exercises

Testing Means

• Single Mean
• t Distribution Demo
• Difference between Two Means (Independent Groups)
• Robustness Simulation
• All Pairwise Comparisons Among Means
• Specific Comparisons
• Difference between Two Means (Correlated Pairs)
• Correlated t Simulation
• Specific Comparisons (Correlated Observations)
• Pairwise Comparisons (Correlated Observations)
• Exercises

Power

• Introduction
• Example Calculations
• Factors Affecting Power
• Exercises

Prediction

• Introduction to Simple Linear Regression
• Linear Fit Demo
• Partitioning Sums of Squares
• Standard Error of the Estimate
• Prediction Line Demo
• Inferential Statistics for b and r
• Exercises

ANOVA

• Introduction
• ANOVA Designs
• One-Factor ANOVA (Between-Subjects)
• One-Way Demo
• Multi-Factor ANOVA (Between-Subjects)
• Unequal Sample Sizes
• Tests Supplementing ANOVA
• Within-Subjects ANOVA
• Power of Within-Subjects Designs Demo
• Exercises

Chi Square

• Chi Square Distribution
• One-Way Tables
• Testing Distributions Demo
• Contingency Tables
• 2 x 2 Table Simulation
• Exercises

Case Studies

Analysis of selected case studies