Page 90 - Handbook PG 20182019

Faculty of Science Postgraduate Booklet, Session 2018/2019

SQF7003 Functional Bioinformatics

Topics include methods of large scale data analysis in order to find information on all genes and
transcripts of an organism using bioinformatics tools. Specific topics include:

(i) Reference and de novo based assembly;
(ii) Gene discovery (search and identify the molecular functions, biological processes,
biological pathways, associated disorders, chromosomal location, publications,
conserved domain given gene symbol or a DNA/amino acid sequence);
(iii) Polymorphism discovery (to understand the different type of variation, to get all variants
given a chromosomal position or a gene identifier, to verity if a variant is a novel or
known, to identify the position of the variant in gene, codon and to identify relevant
sequence variation and structural variation databases, to predict the damaging effect of
the variant, to identify the allele frequency of the variant and to identify disorders
related to the variant CNVs);
(iv) Gene expression analysis (to explain what is a fold change and what is a log ratio, to
calculate and identify up-regulated/down-regulated genes and to annotate gene
information;
(v) NGS – Exome sequencing analysis (to understand the variant call file (VCF) format, to
generate a VCF format file, to manipulate and filter the VCF file in terms of read depth,
quality score, zygosity and to annotate the VCF file).

Assessment Methods:
Continuous Assessment: 100%

Medium of Instruction:
English

Transferable Skills:
Functional analysis of large datasets derived in most cases from emerging high-throughput genomic,
transcriptomics and proteomic technologies.

Humanity Skill:
-

References:
nd
Jonathon Pevsner (2009). Bioinformatics and Functional Genomics (2 ed.). Wiley-Blackwell.

SQF7004 Application of Algorithms in Bioinformatics
This course will focus on the application of algorithms to solve bioinformatics problems such as in
pairwise and multiple sequence alignment, molecular sequence database search, scoring matrices,
phylogenetic analysis and genome assembly. A specific discussion on hidden Markov Model
application in gene prediction will also be conducted.

Assessment Methods:
Continuous Assessment: 100%

Medium of Instruction:
English

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