Course director: Dr. Steven S. Shen

Course meeting times and location: The course will meet twice a week. Each topic consists of two lectures/QA discussion (total 150 minutes) and is followed by laboratory data analysis practices (100 minutes) for students who have registered for HINF 8440. 

Office hours: There will be 1 week scheduled 1hr time slot for office hours; additional office hours will be by appointment.

Description: Translational bioinformatics deals with the assaying, computational analysis and knowledge-based interpretation of complex molecular data to better understand, prevent, diagnose and treat disease. This course emphasizes deep DNA sequencing methods that have a persistent impact on research related to disease diagnosis and treatment. The course covers sequence analysis, applications to genome sequences, and sequence-function analysis, analysis of modern genomic data, sequence analysis for gene expression/functional genomics analysis, and gene mapping/applied population genetics.

Prerequisites:
Graduate students in M.H.I, M.S., Ph.D. or M.D./Ph.D. interested in translational bioinformatics.

Required knowledge and software: Students should be familiar with or ready to learn more basic genome biology, the next generation sequencing technology and short reads sequencing applications, such as RNA-seq, Chip-seq, ATAC-seq, etc.

Final project: The students will design and execute analysis of a genome DNA similarity search and alignment, short reads sequencing data preprocessing and alignment. By Week 4 of the class the students will identify the dataset and submit a 1-page proposal for the final project using available short reads sequencing datasets. The students will submit a final project report (2-5 pages) and present the design of the study, research questions, analysis and results in a short presentation.

Homework: There will be 5 homework assignments throughout the course distributed at least 1 week before the due date. Late homework will be graded but will not count toward class grade.

Course Assessment: The grade for HINF 5440 will be determined according to the following:

1. Homework 20%
2. Project proposal 25%
3. Project report and presentation 30%
4. Final examination 25%

• Topic I
  • Introduction to Genome, Epigenome and Gene expression control (I)
  • Introduction to Genome, Epigenome and Gene expression control (II)
  • Lab: General introduction to lab course
• Topic II
  • introduction to NGS and sequencing bioinformatics 
  • Sequencing reads alignment methods including Illumnia, Pacbio and Oxford nanopore sequencing platforms.
  • Lab: Sequence reads preprocessing and alignment
• Topic III
  • Introduce Microbiome genomics 
  • Introduce computational methods for microbiome data analysis
  • Lab: Microbiome data analysis
• Topic IV
  • Introduction to Gene expression profiling 
  • Introduce computational methods for gene expression profiling
  • Lab: RNA-seq data analysis
• Topic V
  • Introducing Single cell sequencing 
  • Introduce research application and computational methods 
  • Lab: Single cell sequencing data analysis
• Topic VI 
  • Introduction to high throughput technology, genomics and genomics center at U
  • Project planning and designing 
  • Lab: DNA methylation data analysis
• Topic VII
  • Introduce Sequence mutation, SNP, genetic variation and population genomics 
  • Introduce computation methods for mutation analysis 
  • Lab: Genetic variation and SNP calling
• Topic VIII 
  • Introduce DNA repeats and transposons 
  • Introduce research applications in the field and computational methods 
  • Lab: Dealing with DNA repeats
• Topic IX
  • Introducing De novo sequencing, genome assembly and annotation 
  • Introducing genome assembly and annotation methods 
  • Lab: De novo genome assembly
• Topic X 
  • Translational bioinformatics applications (1) 
  • The application related research and computational methods 
  • Lab: Comprehensive genomics data analysis for mental related disease
• Topic XI 
  • Translational bioinformatics applications (2) - integrative cancer genomics 
  • The application related computational methods 
  • Lab: Comprehensive integrated data analysis for cancer genomics
• Topic XII 
  • Introduce causal discovery and machine learning 
  • Discuss related research application and methods 
  • Lab: Machine learning and causal discovery technology (I)
• Topic XIII
  • Translational bioinformatics applications (3) – machine learning related 
  • Discuss related research application and methods 
  • Machine learning and causal discovery technology (II)
• Project presentation
• Final exam

Course director: Dr. Steven S. Shen

Course meeting times and location: The course will meet once a week for laboratory data analysis practices (100 minutes). 

Office hours: There will be 1 week scheduled 1hr time slot for office hours; additional office hours will be by appointment.

Description: Translational bioinformatics deals with the assaying, computational analysis and knowledge-based interpretation of complex molecular data to better understand, prevent, diagnose and treat disease. This course emphasizes deep DNA sequencing methods that have a persistent impact on research related to disease diagnosis and treatment. The course covers sequence analysis, applications to genome sequences, and sequence-function analysis, analysis of modern genomic data, sequence analysis for gene expression/functional genomics analysis, and gene mapping/applied population genetics.

Prerequisites:
Graduate students in M.H.I, M.S., Ph.D. or M.D./Ph.D. interested in translational bioinformatics. 

Required knowledge and software: Students will need to have some basic knowledge about R or Python or Perl programming and/or MSI super cluster computation environment. Understanding short reads alignment and R/Bioconductor packages will be a big plus but not required

The grade for HINF 8440 will be determined 100% by lab assignments.

Laboratory Topics by Week for HINF 8440

1. Lab1 – General introduction to lab course
2. Lab2 – Sequencing reads preprocessing and alignment
3. Lab3 – Microbiome data analysis
4. Lab4 – RNA-seq data analysis
5. Lab5 – Single cell sequencing data analysis
6. Lab6 – DNA methylation and Chip-seq data analysis
7. Lab7 – Genetic variation and SNP calling
8. Lab8 – De novo genome assembly 
9. Lab9 – Dealing with DNA repeats
10. Lab10 – Comprehensive genomics data exploration and analysis for mental related disease
11. Lab11 – Comprehensive integrated data analysis for cancer genomics
12. Lab12 – Machine learning and causal discovery technology (I)
13. Lab13 – Machine learning and causal discovery technology (II)
14. Lab14 – Project report