Timothy Vaughan: Catalogue data in Autumn Semester 2023

Name Dr. Timothy Vaughan
Name variantsTimothy G. Vaughan
Tim Vaughan
Timothy Vaughan
Address
Computational Evolution
ETH Zürich, BSS J 2.1
Klingelbergstrasse 48
4056 Basel
SWITZERLAND
E-mailtimothy.vaughan@bsse.ethz.ch
DepartmentBiosystems Science and Engineering
RelationshipLecturer

NumberTitleECTSHoursLecturers
636-0017-00LComputational Biology6 credits3G + 2AT. Vaughan, C. Magnus, T. Stadler
AbstractThe aim of the course is to provide up-to-date knowledge on how we can study biological processes using genetic sequencing data. Computational algorithms extracting biological information from genetic sequence data are discussed, and statistical tools to understand this information in detail are introduced.
Learning objectiveAttendees will learn which information is contained in genetic sequencing data and how to extract information from this data using computational tools. The main concepts introduced are:
* stochastic models in molecular evolution
* phylogenetic & phylodynamic inference
* maximum likelihood and Bayesian statistics
Attendees will apply these concepts to a number of applications yielding biological insight into:
* epidemiology
* pathogen evolution
* macroevolution of species
ContentThe course consists of four parts. We first introduce modern genetic sequencing technology, and algorithms to obtain sequence alignments from the output of the sequencers. We then present methods for direct alignment analysis using approaches such as BLAST and GWAS. Second, we introduce mechanisms and concepts of molecular evolution, i.e. we discuss how genetic sequences change over time. Third, we employ evolutionary concepts to infer ancestral relationships between organisms based on their genetic sequences, i.e. we discuss methods to infer genealogies and phylogenies. Lastly, we introduce the field of phylodynamics, the aim of which is to understand and quantify population dynamic processes (such as transmission in epidemiology or speciation & extinction in macroevolution) based on a phylogeny. Throughout the class, the models and methods are illustrated on different datasets giving insight into the epidemiology and evolution of a range of infectious diseases (e.g. HIV, HCV, influenza, Ebola). Applications of the methods to the field of macroevolution provide insight into the evolution and ecology of different species clades. Students will be trained in the algorithms and their application both on paper and in silico as part of the exercises.
Lecture notesLecture slides will be available on moodle.
LiteratureThe course is not based on any of the textbooks below, but they are excellent choices as accompanying material:
* Yang, Z. 2006. Computational Molecular Evolution.
* Felsenstein, J. 2004. Inferring Phylogenies.
* Semple, C. & Steel, M. 2003. Phylogenetics.
* Drummond, A. & Bouckaert, R. 2015. Bayesian evolutionary analysis with BEAST.
Prerequisites / NoticeBasic knowledge in linear algebra, analysis, and statistics will be helpful. Programming in R will be required for the project work (compulsory continuous performance assessments). In case you do not have any previous experience with R, we strongly recommend to get familiar with R prior to the semester start. For the D-BSSE students, we highly recommend the voluntary course „Introduction to Programming“, which takes place in Basel before the start of the semester.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingassessed
Problem-solvingassessed
Personal CompetenciesCreative Thinkingassessed
Critical Thinkingassessed
636-0120-00LIntroduction to Programming
This is a voluntary programming course BEFORE the start of the semester, open for students of the MSc Biotechnology master's programme.
Lecture dates: 4-15 September 2023.
0 credits1GA. Gupta, T. Vaughan
AbstractIntroduction to Programming provides an overview of the basic programming blocks needed to translate a problem, stated in textual form, into an algorithm that solves such problem. The course provides an introduction to the MATLAB programming language and covers Bash scripting and other programming languages such as R and Python.
Learning objectiveThe goal of this course is to give students, who have no prior programming background, a solid introduction to algorithm development and its successive implementation in a programming language. For students with previously acquired programming skills, the course will serve as a reinforcement of key aspects of structured programming in addition to providing a well-rounded introduction to MATLAB, R and Python.
ContentThe course is structured in four main pillars:
• Logical thinking: Translating a problem into a conceptual sequence of computational steps. For example:
[Problem] What is the GC content of a given DNA string?
[Logical steps]
i) Iterate through all nucleotides in the DNA string, one by one
ii) Count the Cs or Gs
iii) Divide the count of Cs or Gs by the length of the DNA string
iv) Report the result.
• The basics of programming: Variables, functions and arrays. Control flow and recursion. Top-down algorithm design. Computational complexity of an algorithm.
• Writing code: Full introduction to the MATLAB programming languages (R and Python will also be covered). Solutions to all exercises will be provided in MATLAB, R and Python. Creation of programming projects with an integrated development environment (IDE).
• Primer of Unix commands: Command-line examples on how to access servers and computing resources at the D-BSSE. Submission of jobs to the EULER cluster.
Lecture notesAvailable on course website (Moodle)
LiteraturePublicly available material (links will be posted on the course website)