Course code BIO326

BIO326 Genome sequencing; tools and analysis

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Norsk emneinformasjon

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Showing course contents for the educational year starting in 2021 .

Course responsible: Matthew Peter Kent, Phillip Byron Pope
Teachers: Mariann Arnyasi, Torfinn Nome, Tan Thi Nguyen, Kristina Severine Rudskjær Stenløkk, Marie Odile Charlotte Andree Baudement, Marie Saitou, Arturo VeraPonceDeLeon, Ianina Altshuler, Live Heldal Hagen, Dag Inge Våge
ECTS credits: 10
Faculty: Faculty of Biosciences
Teaching language: EN, NO
(NO=norsk, EN=Engelsk)
Limits of class size:
20
Teaching exam periods:
This course starts in Spring parallel. This course has teaching/evaluation in Spring parallel.
Course frequency: Annually
First time: 2020H
Preferential right:
Priority is given to students admitted to the Master program Genome Science (M-GS) because this is an obligatory course for them.
Course contents:
Our ability to translate an organisms genetic blueprint (it’s genome) from chemical nucleotides to electronic sequence information that can be computationally analyzed has become a critical tool in many life science fields. Improving animal and plant health, ensuring  sustainable primary production, preserving genetic diversity, and better understanding the links between genome and biology are all research areas that rely heavily on decoding and analyzing genetic code. The same applies for microbes and microbiomes, which play essential roles regulating the many biogeochemical cycles that are essential to life on earth. Using the latest technologies and computational strategies, this course will explore the different approaches used to recover and reconstruct genome sequences from various biological sources including prokaryotic and eukaryotic cells, as well as complex microbial communities (i.e. microbiomes). You will learn about how the dominant sequence technologies work, different ways that DNA and RNA can be prepared to answer specific questions. Computational analysis will include a variety of algorithms used for DNA analysis AND genome curation and annotation and how they best suit a particular biological sample. From this, students should be able to design and execute both wetlab (e.g. DNA preparation) and drylab (computational biology) experiments and select the appropriate methods and software. 
Learning outcome:
  • Knowledge

Each student should:

  • be able to describe the principles behind nanopore DNA sequencing and explain the critical issues that can affect the success of a sequencing experiment, and assess raw data quality.
  • be able to describe the use of and theory behind at least two specialized library preparation methodologies (e.g. HiC, ATAC).
  • be able to clarify which processes must be performed to align DNA or RNA read data to a reference genome, identify SNPs, perform differential gene expression etc using the Galaxy infrastructure
  • be able to critically assess literature and present an experimental plan for sequencing and assembling Prokaryotic genomes.
  • be able to explain the key aspects behind metagenomic assembly and binning.
  • demonstrate data sharing pipelines and describe the content and purpose of core online data repositories.
  • be able to utilize what they have learnt to analyse a scientific question closely related to this courses content and contribute to discussion.
  • Skills

Each student should:

  • Be able to perform end-to-end (sample to sequence) nanopore sequencing in the wet lab with limited support.
  • Be able to login to Galaxy and Orion infrastructure and use appropriate bioinformatic tools for read alignment, genome assembly and annotation.
  • Be able to critically assess the results from wet lab sequencing and dry lab bioinformatics and determine if they are suitable to answer the scientific question for which they were used.
  • Be able to use R-studio for data visualization.
  • General competence

Each student should:

  • Be able to analyse a scientific question related to what they have been taught and propose a strategy for answering it.
  • Be able to communicate the results from wet or dry lab experiments to other scientists and to the general public.
  • Be able to contribute to a Masters level project in terms of planning and experimental design, and selection of appropriate methodologies and analysis.
Learning activities:

The course will involve lectures and group teaching as well as (wet and dry) lab work. Lectures, group discussions and practical bioinformatic exercises will be integrated to enable students to work on assigned tasks, which will include generating raw data, analyzing datasets and reporting their findings. 

The wet-lab work is intended to give students practical experience with extracting DNA, assessing its quality, preparing it for sequencing (library production) and sequencing; the sequencing technology of choice will be long-read technology from Oxford Nanopore. Dry-lab work will entail running different bioinformatic software that are designed for genome assembly, binning, annotation and quality assessment. Students will work with both genome datasets from eukaryotes and prokaryotes as well as metagenomes from microbiome samples.

Teaching support:
Canvas
Syllabus:
Can be found at the BIO326-Canvas pages.
Prerequisites:
Familiarity with a programming language (preferably Python and/or R), as well as a basic knowledge in molecular biology, microbiology and/or cell biology is required.
Recommended prerequisites:
Mandatory activity:
Your presence and active participation in the entire course and in the group work are prerequisites for participating in the exam. 
Assessment:
Portefolio assessment: The examination is a combination of oral presentations and a project notebook that will follow the course’s experimental exercises and must be submitted 3 weeks after the course. The assessments will be marked as passed/not passed.
Nominal workload:
250 hours.
Entrance requirements:
Special requirements in Science. Students need to sign up at StudentWeb
Reduction of credits:
-
Note:
-
Examiner:
The reports will be approved by an external examiner.
Examination details: Portfolio: Passed / Failed