Module Catalogues, Xi'an Jiaotong-Liverpool University   
Module Code: BIO316
Module Title: High-Throughput Approaches and Systems Biology
Module Level: Level 3
Module Credits: 5.00
Academic Year: 2020/21
Semester: SEM2
Originating Department: Biological Sciences
Pre-requisites: BIO211BIO214
A major grand challenge in the molecular biosciences is to develop predictive models for key life processes. Accordingly, there is an increasing need for research bio-scientists to handle diverse, large-scale, quantitative datasets and to extract meaningful information from them.

This module is designed to provide the bioinformatics skills necessary to analyse and interpret genome sequence data, as well as high-throughput transcriptomic and proteomic data. The module will also provide an introduction to data integration and systems biology approaches
by emphasizing the system-related concept. The students should be able to process various datasets generated from different high-throughput technologies.

Learning outcomes 

A Apply appropriate bioinformatics tools and statistical approaches to conduct a research project in this area with high-throughput biological data generated using latest biotechnology.

B Critically analyze and process high-throughput bio-molecular data, especially next generation sequencing data, such as, RNA-Seq, ChIP-Seq, and SNPs.

C Appraise, integrate and present different types of bioinformatics data in appropriate formats, such as, FASTA, FASTQ, BAM, and GTF.

D Apply appropriate bioinformatics/statistical tools to analyze bioinformatics data on both Linux and Windows OS with R/Bioconductor.

E Integrate different datasets from multiple layers to understand biology mechanisms from systems biology perspective.

F Have a working knowledge of high performance computing systems, including bash scripting, multi-threads computing, and parallel computation.

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.

1. Data analysis and visualization (weeks 1-3)

Use of open source platforms packages to visualize complex data types and integrate them
with diverse types of attribute data. This section will specifically cover R basics, various data visualization techniques, and advanced techniques, such as, ggplot2 package.

• Lecture 1: R basics and data visualization techniques

• Lecture 2: Advanced graphics with ggplot2

• Lecture 3: Bioinformatics analysis with R/Bioconductor packages

2. Next generation sequencing basics (weeks 4-6)

Library construction, strand-specific, various sequencing protocol (Single-End, Paired-End), recent development, file format, low level processing with Samtools, quality control, Linux Bash script

• Lecture 4: Current development of next generation sequencing and third generation of sequencing.

• Lecture 5: Linux Bash scripting for biological data processing.

• Lecture 6: Raw NGS data quality control and secondary analysis.

3. RNA-Seq data analysis (weeks 7-8)

Transcriptome reconstruction and gene expression analysis (differential analysis, isoform
analysis, visualization) with Tuxedo package (Tophat, Cufflinks, Cuffdiff and CummeRbund), work with real data. Get familiar with biological replicates, differential expression, visualization of NGS data, etc. Report and presentation.

• Lecture 7: RNA-Seq and RNAseq data analysis methods

• Lecture 8: RNA-Seq data analysis with Tuxedo package

4. ChIP-Seq data analysis (weeks 9-10)

Covers methods for analyzing other sequencing approaches, such as, ChIP-Seq, BS-Seq and
MeRIP-Seq; concept of consensus sequence (motif), and differential binding analysis. Reports and presentation.
• Lecture 9: ChIP-Seq and other sequencing approaches, and their analysis methods

• Lecture 10: ChIP-Seq analysis hands-on session, with MACS, MEME, etc.

5. Proteomic and metabolomic data analyses (weeks 11-12)

Covers methods for proteomic and metabolomics data analysis with commercial and open-
source software tools

• Lecture 11: Proteomics approach and analysis methods

• Lecture 12: Metabolomics approach and analysis methods

Additional state-of-the bioinformatics contents, especially systems biology, may be delivered depending on the actual progress of teaching & learning activities in the form of workshops, including personalized medicine, genome-based disease risk prediction (highly recommended), data integration, gene network prediction from high-throughput data, etc.

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 Project 1 25.00
2 Project 2 25.00
3 Project 3 25.00
4 Project 4 25.00

Module Catalogue generated from SITS CUT-OFF: 6/7/2020 5:04:52 AM