CS 59000: Bioinformatics Algorithms (Fall 2020)

Course Information

When: Monday/Wednesday 4:30 pm – 5:45 pm.
Where: LWSN B134.
Instructor: Jianzhu Ma, majianzhu@purdue.edu.
Office Hour: Thursday. 4:30 pm – 5:45 pm.or by appointment (send e-mail) Where: TBD
Online discussion is available at Blackboard (mycourses.purdue.edu).

Course description:

This course covers almost all the major research directions in bioinformatics and computational biology. It is a type of “all-you-need” class if you want to develop algorithms to process and understand biological data. By completing this course, students will encounter a handful of fundamental algorithmic approaches deriving straight from very widely cited primary literature, much of which has been published in recent years. The course also introduces basic concepts in statistics, mathematics, and machine learning including deep learning, which are all necessary to understand these approaches. Classes consist of both instructor and student presentations of their final projects (see Syllabus). It goes beyond just invoking existing popular programs and packages. Instead, the aim is for you to understand the underlying principles of the most successful techniques that are currently in use, and provide you with the capacity to design and implement the next generation of tools. Another goal of the course is to tackle the research frontiers of computational biology, and give you a glimpse of how research works, expose you to current bioinformatics research directions and guide you to find the problems most interesting to you.

Prerequisites:

This course is intended to be a first course in Bioinformatics algorithms for graduate students in computer science, computer engineering, mathematics, statistics, computational life sciences, and related fields. Students from computer science, the engineering disciplines, and mathematical sciences may be concerned about how much previous exposure to biological sciences they need to study bioinformatics. The answer is, not much—students do not need any previous knowledge of biology beyond high school. But you will need a willingness to learn biological concepts needed to understand the computational problems we study. We will begin with a quick review of some of the biological concepts needed for bioinformatics, and will learn more as needed.
Students are expected to have the following background:

• Basic programming skills to write a reasonably non-trivial computer program in Python or C (e.g., CS 38003 or equivalent are recommended).

• At least an undergraduate course in algorithms (a graduate course in algorithms is preferred).

• Undergraduate or graduate level machine learning courses (e.g., CS 37300 and CS 578 are sufficient).

• Students without this background should discuss their preparation with the instructor. The introduction session in the first week of the class will give an overview of the expected background.

Target Students

This course is intended to be a first course in Bioinformatics algorithms for graduate students in computer science, computer engineering, mathematics, statistics, computational life sciences, and related fields. This is suitable as a foundational course for students planning to do research in bioinformatics with a focus on algorithms; it is also suitable for those who wish to acquire some breadth in algorithms during graduate study and learn about important recent developments in bioinformatics.

Optional textbooks

“Biological sequence analysis” by Durbin, Eddy, Krogh, Mitchison
“An introduction to bioinformatics algorithms” by Jones, Pevzner
“Network Biology:” section in Topology of molecular interaction networks
“Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Online book “Bioinformatics Algorithms” [link], Phillip Compeau and Pavel Pevzner

Grading

• Homework (50%)

• Final projects (50%)

Assignments and exams

There will be FIVE homework assignments including both theory and programming problems. Assignments should be submitted online in Brightspace, piazza or Blackboard systems. Programming projects can be written in both R and Python. Deep learning final projects should be written in either Tensorflow and PyTorch.

Late policy

Assignments are to be submitted by the due date listed. Assignments will NOT BE accepted if they are submitted late. Additional extensions will be granted only due to serious and documented medical or family emergencies.

Final projects

Students are encouraged to work as teams of two or three. Each team can either choose the provided projects on the course website or a separate project related to Biology. If you choose to do your own project, please contact me beforehand to discuss it. Here are the bioinformatics projects you might consider:

• Easy projects:

  • Using deep neural networks to predict protein secondary structures.

  • Using deep neural networks to predict protein contact/distance map.

  • Implement a RNA secondary structure prediction algorithm.

  • Implement a single cell analysis pipeline.

  • Predicting protein functions prediction using multiple protein interaction networks.

  • Implement an algorithm to correct the batch effect in single cell data.

  • Predicting enhancers using genomics features.

• Advanced projects:

  • Implement a (hierarchical) network community detection algorithms to recover Gene Ontology.

  • Using Contralign algorithm to improve the consistency of multiple network alignment.

  • Using network and deep learning to predict disease related genes.

  • Using deep learning to predict drug response for cancer.

  • Protein function prediction from interaction network using graph.

  • Comparison and analysis of methods in 3D genome reconstruction from Hi-C data.

  • Translation of genotype to phenotype by using the molecular networks.

Syllabus (tentative)

Sequence analysis in Bioinformatics (9 classes)

Structure analysis in Bioinformatics (4 classes)

Machine Learning in Bioinformatics (5 classes)

Matrix analysis in Bioinformatics (4 classes)

Network analysis in Bioinformatics (4 classes)

Project presentations (2 classes)