Education

Graduate courses

1. Plant Genomics by Dr. Xiangfeng Wang

(Every Spring semester, 2nd to 9th weeks, Monday and Wednesday, 3 & 4 classes)

  • 1) Introduction of Plant Genomics download ppt
  • 2) Next-generation of sequencing (NGS) technology download ppt
  • 3) Application of NGS in crop genomics research download ppt
  • 4) NGS data processing and visualization download ppt
  • 5) Gene expression and RNA-Seq data analysis download ppt
  • 6) Epigenetics and ChIP-Seq data analysis download ppt
  • 7) Genome sequencing, assembly and annotation download ppt
  • 8) Re-sequencing and GWAS analysis download ppt
  • 9) Genomic selection-assisted breeding model download ppt
  • 10) Introduction and bioinformatics tools download ppt

We also invite 3 to 5 bioinformatics and genomics expert outside CAU to give a talk regarding microRNAs, Metagenomics and other interesting topics

2. Scientific English for Genetic Breeding by Dr. Xiangfeng Wang

(Every Autumn semester)
In this class, we teach students

  • 1) Introduce graduate programs in American Universities
  • 2) How to write C.V., Personal Statement
  • 3) How to write manuscripts
  • 4) How to read papers
  • 5) How to formulate hypothesis for scientific projects
  • 6) Mentoring students for public speech in English

Upcoming undergraduate courses

Since Crop Genomics has been elected as a core course for CAU undergraduate student education, we are organizing a series of class for undergraduate students. Most classes will be launched in 2018 or 2019. They are:

1. Crop Genomics(24 hours, 1.5 credits)

  • Chapter 1. Introduction
    • Introduce the content of this class
  • Chapter 2. Genome sequencing technology
    • Introduce genome sequencing projects of model organisms
    • History of sequencing technology development and principles
    • Introduce Illumina high-throughput sequencing technology
  • Chapter 3. Comparative Genomics
    • Features of plant genomes
    • Concepts of Paralogs, orthologs, synteny, SNPs, phylogenetics
  • Chapter 4. Origins of crops and domestication
    • The story of crop origins, domestication and evolution
    • Methods and tools for genome evolution studies
  • Chapter 5. Population Genomics
    • Basic concepts of population genetics and mechanism of genetic improve of agricultural traits
    • Principles and methods for GWAS
  • Chapter 6. Genomic selection breeding
    • Introduce whole genomic selection-assisted breeding and other relevant methods
  • Chapter 7. Epigenomics and non-coding RNAs
    • Concepts of DNA methylation, histone modifications
    • Function and biosynthesis of non-coding RNAs
  • Chapter 8. Bioinformatics databases and tools
    • Introduce databases of NCBI, Ensembl, KEGGs etc
    • Bioinformatics tools for sequence analysis and annotation

2. Cutting-edge topics in plant genomics (24 hours, 1.5 credits)

The class will invite 8 experts in the cutting-edge plant genomics research, to introduce the hot topics involving plant genomics, NGS sequencing, epigenetics, non-coding RNAs, Big Data, genome-wide association analysis, molecular breeding, proteomics and etc. The goal of this class is to extend the view of the students in Agronomy and be preparative for their future research in crop genomics.

  • Class 1: Topics on gene transcriptional regulation in plants;
  • Class 2: Topics on next-generation sequencing in plant genomics;
  • Class 3: Topics on epigenetics in plants;
  • Class 4: Topics on non-coding RNAs in plants;
  • Class 5: Topics on GWAS and molecular breeding
  • Class 6: Topics on Big Data in plant genomics;
  • Class 7: Topics on proteomics in plants;
  • Class 8: Topics on analytical methods in plant genomics

3. Crop Bioinformatics (24 hours, 1.5 credits by Dr. Weilong Guo etc)

Bioinformatics is an interdisciplinary field to understand biological data combining computational programming, algorithm design, statistics, machine learning. In the past decade, the high-throughput sequencing technology is developed rapidly, and biological data has been accumulated amazingly, bringing opportunities for the field of bioinformatics. This course will follow the key questions in studying crop genome, and introduce the bioinformatics questions, methodology and achievements in aspects of genomics, transcriptomes, proteomics and system biology, respectively. For each topic, we will cover the question definition, data generation, analysis pipeline, computational algorithm and statistic modeling. The aim of this course is to guide student to systematically understand this field, and build a logical scientific thinking. The students would be able to learn the cut-edge research of crop bioinformatics, and gain knowledges and experience for their future relevant career.

  • Chapter 1: Introduction to bioinformatics (2h)
  • Chapter 2: Bioinformatics issue in genomic study (6h)
  • Chapter 3: Bioinformatics issue in transcriptome study (6h)
  • Chapter 4: Bioinformatics issue in proteomic study (2h)
  • Chapter 5: System biology (2h)
  • Chapter 6: Bioinformatics issue in genomic study (2h)
  • Chapter 7: Others aspects of bioinformatics (2h)
  • Chapter 8: Summary in angels of computater scientist and statistician (2h)

4. Bioinformatics Lab (16 hours, 1 credit)

This course will provide chances for undergraduate student to practice and gain experiences in bioinformatics. This course includes two main experiments. The first one is to build up a Shell pipeline for the alignment of high-throughput sequencing data and the following analysis. The second experiment require student to analysis multiple transcriptomes by normalization, differentially expression analysis, WGCNA analysis and visualization using R script. The two experiments cover both basic raw data analysis, and advance bioinformatics analysis, and will train student to build bioinformatics pipeline, understand parameters and visualize their data. Additionally, this course will provide chance to visit the top bioinformatics company in China, to perform experiment on bioinformatics cloud platform. The leader experts in this field will be invited to share their bioinformatics experiences and career, and introduce the market prospect of bioinformatics.

  • 1) Build bioinformatics analysis pipeline for alignment of NGS sequencing data (4h).
  • 2) Learning linux and basic shell commands, install bioinformatics software on computers, and write shell pipeline to align the raw sequencing data to reference genome (4h).
  • 3) Bioinformatics analysis for “big data” of transcriptomes to build co-expression gene network and data visualization (4h)
  • 4) By learning R script, carry out statistic and bioinformatics analysis on multiple transcriptome data. In the experiment, students need to normalize the data across samples, generate un-supervised clustering, and finish a WGCNA analysis. Visualize the results using R script, and finish an experimental report (4h).

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