Bioinformatics

课程简介

/ Indicative Syllabus Principles of sequencing technologies, sequence assembly, sequence alignment, variant identification and annotation, DNA foundation models, genome editing Hands-on tutorial of FastQC, Megahit, BLAST Principles of RNA sequencing, RNA read mapping, gene expression quantification, differential gene expression analysis, gene set enrichment analysis Hands-on tutorial of DESeq2, g:Profiler, MetaScape, GSEA Basic concepts of gene regulatory elements, analysis of transcription factors, DNA methylation, chromatin accessibility, histone modification, three- dimensional genome Single cell sequencing technologies, characteristics of single cell data, dimensionality reduction, cell type annotation, single cell foundation models, spatial omics techniques Hands-on tutorial of single cell RNA sequencing data analysis using Seurat -- 1 of 3 -- Basics of proteomics, basic theory of biological mass spectrometry (MS) and liquid chromatography-MS instruments, protein identification, quantitative proteomics, post-translational modifications (PTMs), data analysis and bioinformatics of MS data. Basics of metabolites and techniques in metabolomics, pre-process, process and analyse metabolomics data using univariate and multivariate data analysis, metabolomics downstream analyses for metabolic pathway and network analysis, and brief introduction of multi-omics. Hands-on tutorial of metabolomics data analysis using MetaboAnalyst

目标

The subject introduces students to basic principles of bioinformatics, paying particular attention to practical aspects. Students will be able to learn how to select and use proper software for bioinformatics analysis. In addition, the latest topics like biological AI models will also be covered.

先修要求

DNA Technology, Biochemistry, Molecular Biology, Cell Biology Co- requisite NIL

Teaching Pattern

Methodology Lectures will be used as the major content-delivery tool. Students will be able to learn relevant software and programming by practice in the tutorial sessions and assignments. A self-learning component will be included to write a term paper on an interesting application of bioinformatics. Reference materials will be distributed before and in class, from library and from Internet where software is constantly updated. Computational lab practicals will be used to learn how to perform database searching and gene/protein function analysis.