Module Catalogues, Xi'an Jiaotong-Liverpool University   
Module Code: BIO211
Module Title: Bioinformatics I
Module Level: Level 2
Module Credits: 5.00
Academic Year: 2020/21
Semester: SEM1
Originating Department: Biological Sciences
Pre-requisites: N/A
The biological and computational modules delivered in the first two years of BSc Bioinformatics program will be integrated in this module under bioinformatics framework to address various issues related biological databases, sequence comparison, phylogenetics, and other sequence related analysis. Lab sessions in BIO211 emphasise basic programming and hands-on skills to work with biological sequences, i.e., DNA, RNA and proteins.

In BIO211, biological databases, sequence comparison, phylogenetics, and other sequence related analysis are delivered together with recent developments. The students are expected to:

1. Get familiar with biological databases, issues in sequence analysis

2. Understand the mathematical formulation of sequence comparison and phylogenetic analysis

3. To be able to use computer science approach and bioinformatics software to solve practical problems related to sequence comparison, phylogenetics.

Learning outcomes 

A Demonstrate a systematic knowledge in biological databases, sequence comparison, phylogenetics, and other sequence related analysis, and to provide the students with hands-on

B Understand most classic content within the domain of biological databases, sequence comparison, phylogenetics, and other sequence related analysis.

C Analyse datasets of relevance to biological databases, sequence comparison, phylogenetics for future research projects.

D Demonstrate information literacy skills in the discovery, evaluation and acquisition of data from public resources.

E Enable the students to choose an appropriate tool among several computational approaches.

F Have a comprehensive understanding of various biological databases from both computational and biological perspective.

G Familiarity to implement the classic sequence analysis algorithms by hand- calculation or on by computer programming.

H Use popular main-stream bioinformatics resources to address well-defined problems such as phylogenetic analysis, etc.

Method of teaching and learning 
1. Didactic component - the core of the teaching is lecture-based with Q/A and feedback.

2. Self-learning component - students are encouraged to read around the subject materials.

3. Comprehension/review exercise - two continuous assessments, following supervised discussion and Q/A sessions in the seminars.

4. Case studies will be supplied to help students place the course material in context.

5. Working in computer lab with bioinformatics tools to solve practical problems.

There are totally 50 hours public time, which is roughly divided into 13 weeks. And in each week, there should 2 hour lecture, 1 hour tutorial and 1 hour computer lab session.

Lecture 1-2. Introduction to biological databases: What Is a Database Types of Databases, Biological Databases , Pitfalls of Biological Databases , Information Retrieval from Biological Databases

Lecture 3-4. Pairwise Sequence Alignment: DNA sequencing approaches, Evolutionary Basis, Sequence Homology versus Sequence Similarity, Sequence Similarity versus Sequence Identity, Methods, Scoring Matrices, Statistical Significance of Sequence Alignment

Lecture 5-6. Database Similarity Searching: Unique Requirements of Database Searching, Heuristic Database Searching, Basic Local Alignment Search Tool (BLAST), FASTA, Comparison of FASTA and BLAST, Database Searching with the Smith–Waterman Method

Lecture 7-8. Multiple Sequence Alignment: Scoring Function, Exhaustive Algorithms, Heuristic Algorithms, Practical Issues

Lecture 9-10. Profiles and Hidden Markov Models: Position-Specific Scoring Matrices, Profiles, Markov Model and Hidden Markov Model

Lecture 11-12. Protein Motifs and Domain Prediction: Identification of Motifs and Domains in Multiple Sequence Alignment, Motif and Domain Databases Using Regular Expressions, Motif and Domain Databases Using Statistical Models, Protein Family Databases, Motif Discovery in Unaligned Sequences, Sequence Logos

Lecture 13-14. Gene Prediction: Categories of Gene Prediction Programs, Gene Prediction in Prokaryotes, Gene Prediction in Eukaryotes, Transcriptome

Lecture 15-16. Promoter and Regulatory Element Prediction: Promoter and Regulatory Elements in Prokaryotes, Promoter and Regulatory Elements in Eukaryotes, Prediction Algorithms

Lecture 17-18. Phylogenetics Basics: Molecular Evolution and Molecular Phylogenetics, Terminology, Gene Phylogeny versus Species Phylogeny, Forms of Tree Representation, Why Finding a True Tree Is Difficult, Procedure

Lecture 19-20. Phylogenetic Tree Construction Methods and Programs: Distance-Based Methods, Character-Based Methods, Phylogenetic Tree Evaluation, Phylogenetic Programs

Delivery Hours  
Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total
Hours/Semester 26    13  11    100  150 


Sequence Method % of Final Mark
1 Written Examination 70.00
2 Assignment 1 15.00
3 Assignment 2 15.00

Module Catalogue generated from SITS CUT-OFF: 6/7/2020 4:52:43 AM