ONLINE COURSE - Genome Assembly and Annotation (GAAA01)

https://www.prstats.org/course/genome-assembly-and-annotation-gaaa01/

4th - 6th November 2024

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COURSE OVERVIEW - Genome assembly is the process of piecing together
fragments of DNA to reconstruct the original genome. The genome provides
crucial information for understanding genetic structure, function and
variation.

In recent years, long-read sequencing technologies have revolutionized
genome assembly. These long reads can span repetitive sequences and
structural variations making genome assembly simpler but also reducing
gaps and fragments in the genome, resolve repeats, help with the detection
of structural variation as well as improved haplotype phasing.

During this course we will look at data generated using PacBio and Oxford
Nanopore, discuss the pros and cons of both sequencing technologies and
the effect they might have on genome assembly. During the course we will
look at different tools available to generate assemblies, focussing
on de novo genome assembly. Polishing using short or long reads and
the introduction of Hi-C sequencing can increase completeness of the
genomes. At the difference steps during the assembly process we will look
at the contiguity, completeness and correctness of the generated genomes,
thereby evaluation the status of the genome.

Once a genome has been assembled the next step is annotation. Genome
annotation involves identifying and mapping locations of genes and other
functional elements within the sequenced genome. We will take a look at
the differences between prokaryote and eukaryote genomes and the tools
available for annotation. We will talk about steps to improve annotation
once the automatic annotation has been made.

By the end of the course, participants should:

Know the difference between Nanopore and PacBio data
Be able to assembly genomes
Be able to assess the generated genomes
Assemble genomes integrating Hi-C data
Know how to annotate a genome
Please email oliverhooker@prstatistics.com with any questions.

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Oliver Hooker PhD.
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Oliver Hooker <oliverhooker@prstatistics.com>

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