Course Description
Rapid developments in biotechnology and computing are changing the way that biomedical scientists interact with data. Traditionally, data were the end result of laborious experimentation, and their interpretation mostly involved careful thought and background knowledge. Today, data are increasingly generated much earlier in the scientific workflow and are much larger in scale. Also, before the data can be interpreted, extensive computational processing is often necessary. Thus, the data deluge now requires the mining and modeling of biomedical data at a large scale - ie biomedical data science.
This course aims to equip students with some of the concepts and skills relevant to biomedical data science, with an emphasis on bioinformatics, a sub-discipline of this broader field, through examples of mining and modeling of genomic and proteomic data. More specifically, bioinformatics encompasses the analysis of gene sequences, macromolecular structures, and functional genomics data on a large scale. It represents a major practical application for modern techniques in data mining and simulation. Specific topics to be covered include sequence alignment, large-scale processing, next-generation sequencing data, comparative genomics, phylogenetics, biological database design, geometric analysis of protein structure, molecular-dynamics simulation, biological networks, mining of functional genomics data sets, and machine learning approaches for data integration.
Overall Flow of the Class:
(Module = Group of Lectures)
Introduction
Module on "the Data" (Genomic, Proteomic & Structural Data), introducing the main data sources (their properties, where you access, &c)
Module on Databases & Data Science Issues (Knowledge Representation incl. Sem. Web & Privacy, Provenance & Standards)
Module on Mining (Alignment & Variant Calling, Supervised & Unsupervised Approaches, Networks)
Module on Cell Modeling
Module on Molecular Modeling
Lectures:
MW 1:00 - 2:15 PM, Bass 305
Discussion Section:
Bass 405 (subject to change)
Different headings for this class (4 variants)
CB&B752/CPSC752 - Grad. w/ programming
This graduate-level version of the course consists of lectures, in-class tests, discussion section, programming assignments, and a final programming project.
MB&B752/MCDB752 - Grad. w/o programming
This graduate-level version of the course consists of lectures, in-class tests,
discussion section, written problem sets, and a final (semi-computational section and a literature survey) project. Unlike CBB752, there is no programming required.
MB&B 753b3/MB&B 754b4 - Modules
For graduate students the course can be broken up into two "modules" (each counting 0.5 credit towards MB&B course requirement):
753 - Biomedical Data Science: Mining (1st half of term)
754 - Biomedical Data Science: Modeling (2nd half of term)
Each module consists of lectures, in-class tests, written problem sets, and a final, graduate level written project that is half the length of the full course's final project.
MB&B452/MCDB452/S&DS352 - Undergrad.
This undergraduate version of the course consists of lectures, in-class tests, discussion section, written problem sets, and a final (semi-computational section and a literature survey) project. The programming assignments from CB752 can be substituted for the written work by permission of instructor.
Auditing
This is allowed. We would strongly prefer if you would register for the class.
Prerequisites
The course is keyed towards CBB graduate students as well as advanced undergraduates and graduate students wishing to learn about types of large-scale quantitative analysis that whole-genome sequencing and forms of large-scale biological data will make possible. It would also be suitable for students from other fields such as computer science, statistics or physics wanting to learn about an important new biological application for computation.
Students should have:
A basic knowledge of biochemistry and molecular biology.
A knowledge of basic quantitative concepts, such as single variable calculus, basic probability & statistics, and basic programming skills.
These can be fulfilled by: MBB 200 and Mathematics 115 or permission of the instructor.
Class Requirements
Discussion Section / Readings
Papers will be assigned throughout the course. These papers will be presented and discussed in weekly 60-minute sections with the TFs. A brief summary (a half-page per article) should be submitted at the beginning of the discussion session.
There will be a midterm covering the 1st half of the course.
There will be a quiz covering 2nd half of the course comprising simple questions that you should be able to answer from the lectures plus the main readings.
For references, please refer the previous quizzes and answer keys from Fall 2012
There will be four homework assignments including assignment 0. We will try to promote the idea of reproducible research and using version control system, specifically GitHub, in facilitating the process of homework submission.
Non-programming Assignments
There will be equivalent four homework assignments (including assignment 0), particularly for MB&B and MCDB students without a programming background. The programming part will be replaced with assignments involving the use of web-based tools or essay questions.
The course syllabus as a single PDF can be found HERE
Pages from previous years
2018 is the 20th time Bioinformatics has been taught at Yale. Pages for the 19 previous iterations of the class are available. Look at how things evolve!
2009 and earlier (12 years of classes, staring in '98) (Note the pre-2010 course was Genomics & Bioinformatics; after 2010, the course contains all of the "Bioinformatics" of previous years and then more (!) with less "Genomics".)