Biomedical Informatics 217: Translational Bioinformatics

• Mar 18: Final Presentation Schedule is posted.
• Feb 20: Grading Statistics posted.
• Feb 8: Problem Set 4 posted.
• Jan 25: Problem Set 3 posted.
• Jan 18: Problem Set 2 posted.
• Jan 16: Linux cheat sheet can be found here: Linux cheat sheet
• Jan 16: MySQL reference handout can be found here.
• Jan 16: RMySQL reference handout can be found here.
• Jan 16: The outline of the SQL tutorial can be found here.
• Jan 11: Problem Set 1 posted.
• Jan 11: R reference handout can be found here.
• Jan 11: The outline of the R tutorial can be found r_tutorial_session.pdf.

• Jan 10: The handout from the first day of class is posted here.
• Jan 10: Tutorial times and locations have been posted online.

2007-2008 Edition

It is the responsibility of those of us involved in today's biomedical research enterprise to translate the remarkable scientific innovations we are witnessing into health gains for the nation… At no other time has the need for a robust, bidirectional information flow between basic and translational scientists been so necessary.

– Elias A. Zerhouni, M.D.,
Director, National Institutes of Health
New England Journal of Medicine, 353:1621, 2005

Translational Bioinformatics is the development of analytic, storage, and interpretive methods to optimize the transformation of increasingly voluminous genetic, genomic, and biological data into diagnostics and therapeutics for medicine.

Topics covered in this course:

• Access and utility of publicly available data sources
• Types of genome-scale measurements in molecular biology and genomic medicine
• Analysis of microarray data
• Analysis of polymorphisms, proteomics, and protein interactions
• Linking genome-scale data to clinical data and phenotypes
• New questions in biomedicine using bioinformatics. Case studies.

Also known as: Computer Science 275

Time: Winter Quarter 2007-2008, Monday and Wednesday 3:30 pm to 4:45 pm

First class: Wednesday, January 9, 2008

Location: Stanford Medical School, Alway M-104 (look for the Alway Building on this map)

Prerequisites: Programming ability at the level of cs106A and familiarity with statistics and biology, or approval of the instructor.

Grading: Grading will be based on four problem sets, midterm exam, final exam, and a final project.

Instructor: Atul Butte, MD, PhD, Assistant Professor of Medicine (Medical Informatics) and Pediatrics abutte@stanford.edu, and two time winner of the American Association for Clinical Chemistry Outstanding Speaker Award. Profile Lab

Teaching Assistants: Marina Sirota msirota@stanford.edu and David Chen dpchen@stanford.edu

Contact: Most questions should be posted to the wiki, on the Q&A page, so that all students can benefit from the answers. Other queries can be directed to biomedin217-win0708-staff@lists.stanford.edu biomedin217-win0708-staff@lists.stanford.edu. The professor or TA will respond as soon as possible.

Office Hours:

• Wednesday, 5:00 - 6:00 PM (Marina Sirota). Lusted Library, MSOB X-215, or surrounding student lounge
• Thursday, 3:00 - 4:00 PM (David Chen). Lusted Library, MSOB X-215, or surrounding student lounge
• Tuesday, 3:00 - 4:00 PM (Professor Butte), with some exceptions to be posted as needed. MSOB X-265
• Directions to MSOB

This course is designed for:

• Graduate students in biomedical informatics, genetics, computer science, or other related disciplines
• Medical students
• Medical, pediatric, surgical or other fellows with an interest in learning and using bioinformatics in research
• Interested undergraduates
• Auditors welcome including medical staff, medical/pediatric/surgical fellows, post-doctoral fellows, and undergraduates. Please contact us to be added to the class email list.

The posters around campus

Required books: None (the course will be taught using recent publications)

Recommended books:

• Peter Dalgaard. Introductory Statistics with R. 2002. This short book serves as a handbook and tutorial for learning the free statistical system R, used in this course. This book is not specific for biological data analysis. Link E-books at Lane
• Robert Gentleman, Vince Carey, Wolfgang Huber, Rafael Irizarry, Sandrine Dudoit. Bioinformatics and Computational Biology Solutions Using R and Bioconductor. 2005. This large book serves as a comprehensive book on using the Bioconductor libraries in R for biomedical data analysis. Link E-books at Lane
• Terry Brown. Genomes 3. 2006. A great non-overwhelming reference on molecular biology, with specific focus on novel genome-era measurement tools, such as microarrays. A previous edition of this text is available free at NCBI.]] E-books at Lane

Preliminary Syllabus

Current Schedule

Televised lectures from SCPD. Follow the BIOMEDIN217 link.

Students should use the Q&A page for discussion and posting questions/answers related to the course and to homeworks. We are experimenting with using this in lieu of a newsgroup.

Readings are linked from each lecture on the schedule page. Expect two to three readings per class as preparation.

Four problem sets: hands-on analysis of data, which start with reproducing the findings in one or two publications given their raw data, then adding a twist.

Problem set coding language policy: Please use the following languages for problem sets: R, MySQL.

Problem set collaboration policy: You can talk with others in the class about this problem set, but you must turn in your own individual work.

Problem set due dates: Problem sets are due at or before 5:00 PM on the due date.

In the second half of the quarter, the students will research, design, and implement a project of similar scope to one of the problem sets. Final presentations will be held on March 20 from 3:30 to 6:30 pm, in Wallenberg Learning Theatre.

Final project details

Problem Set 1 Due: Fri, Jan 18, 5pm.
Problem Set 2 Due: Fri, Jan 25, 5pm.
Problem Set 3 Due: Fri, Feb 1, 5pm.
Problem Set 4 Due: Fri, Feb 15, 5pm.

Based on the lectures and the readings. Open book. To be held during a special evening session, to be determined.

Based on all the lectures and the readings. Open book. To be held during a class session, to be determined.

Midterms and finals may be graded on a curve. Final grades will be calculated: