Biomedical Informatics 217: Translational Bioinformatics

BIOMEDIN 217 (otherwise known as Translational Bioinformatics, or CS 275) will be taught virtually this quarter. There are no in-person lectures this year. Students who want to take the in-person version can wait until 2013 Spring Quarter, when Dr. Butte will be back live.

Stanford students can register for the class using Axess (https://axess.stanford.edu). Remote SCPD students can register at http://scpd.stanford.edu or call SCPD at 650-725-3016 and ask for student services.

The video lectures for this year will be those given during the 2009-2010 academic year (January to March 2010), when the video recording quality was at its best.

• Stanford-based students should watch the videos via https://myvideosu.stanford.edu.
• Remote SCPD students need to watch the videos via their myStanfordconnection account off the main SCPD site http://scpd.stanford.edu. SCPD keeps track of the videos watched, so it is important for SCPD students to use this link.

The TAs this year are Stephen Pan stevepan@stanford.edu and Beth Percha blpercha@stanford.edu. If you are taking the class, be sure to sign up for Piazza (http://www.piazza.com), our Piazza site (http://piazza.com/class#spring2012/biomedin217), and subscribe to our blog (http://bmi217translationalbioinformatics.blogspot.com/).

The TAs will be offering one help-session each:

TA session #1: R statistical language. Friday April 13th, 3-4PM, LKSC 102.

TA session #2: MySQL. Friday May 4th, 3-4PM, LKSC 102. Cancelled. Please see MySQL review sheet (emailed to class and on Piazza).

Both will be recorded for SCPD students.

We will still require four problem sets, one midterm exam, one final exam, and a final project. We will not have final project presentations this year, but final reports will be due by the end of the class (date to be determined).

Welcome to BMI 217! – Atul Butte

Subscribe to our RSS news feed.

• http://bmi217translationalbioinformatics.blogspot.com/2012/05/announcements-1-project-office-hours.html (2012/05/07 14:07)

  Announcements:
  1) Project office hours:
  Friday May 11th 10 - 11:30 AM at Peet's in the Clark Building, 3rd floor with Steve Pan.
  Monday May 14th 11AM - 12:30 PM in Clark S240 with Bethany Percha.
  
  Who to speak to and expertise:
  Steve - data sources, medical/biological relevance
  Bethany - statistical analysis and machine learning
  
  If you received an email saying you should come talk to us...you should make it a point to come. But all are welcome. Questions about the problem sets will only be answered after all discussions about projects.
  
  2) Problem set 3 is due this Wednesday at midnight.
  
  3) Grades from problem set 2 and the midterm will be returned later this week.
  
  - Steve and Beth

  
• http://bmi217translationalbioinformatics.blogspot.com/2012/04/just-reminder-that-midterm-is-this.html (2012/05/01 15:32)

  Just a reminder that the midterm is this Wednesday from 6 - 8 PM in Alway M114. You will have 2 hours to take the exam but it is not designed to take 2 hours. If you are a local student you will be expected to take the exam at that time unless you make arrangements with us otherwise (and it is at this point very late to be contacting us regarding that). 
  
  If you are an SCPD student the midterm will be emailed to your proctor. You may take the exam anytime Wednesday. Contact us immediately if this is not possible.
  
  The midterm will cover all course material up to and including lecture 10 (Pharmacology). It is open book/notes/computer. However you will NOT be allowed to have access to the internet. 
  
  
  
  Students enrolled for BMI218 are not expected to take the exam. 

  
• http://bmi217translationalbioinformatics.blogspot.com/2012/04/problem-set-1-grades-were-emailed-to.html (2012/04/24 21:58)

  Problem set 1 grades were emailed to your stanford email today. 
  
  
  The mean grade was 95. Standard deviation was 12. 
  
  
  Histogram
  
  I will place paper copies of the graded writeups in a folder in my mailbox (Pan/Palma) on the 2nd floor of the Medical School Office Building (MSOB).
  
  
  
  General feedback for all:
  1) For future problem sets: please please please put your name and sunet ID somewhere in all files.
  2) There were a lot of misconceptions around the idea of a p-value. I highly recommend the wikipedia page on p-value. The first 3 paragraphs say it all. For simplicity sake: p = p(result | H0) NOT p(H0 | result). Subtle but important!
  3) Several people overlooked writing a summary of their approach. Please do not forget to do this. It's 15 points!
  
  If you didn't receive your grade please email the staff. Thanks. 
  

  

Preliminary Syllabus

Current (virtual) Schedule

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: Spring Quarter 2011-2012, lectures delivered virtually online

First class: N/A

Location: N/A

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, Associate Professor of Pediatrics and, by courtesy, Medicine and Computer Science, and two time winner of the American Association for Clinical Chemistry Outstanding Speaker Award. Profile Lab

Teaching Assistants: Stephen Pan stevepan@stanford.edu and Beth Percha blpercha@stanford.edu

Contact: Most questions should be posted to the piazzza group page, so that all students can benefit from the answers. Professor Butte or the TAs will respond as soon as possible. For private matters, you can contact us at biomedin217-spr1112-staff@lists.stanford.edu. Please do not email the TAs individually unless you specifically have a question for just one of them.

Office Hours:

• To be determined

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.

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

Recommended books:

• Genomic and Personalized Medicine, Vol 1-2. Ed. Huntington Willard and Geoffrey Ginsburg. 2008. These two books cover a comprehensive look at “advances in the diagnosis, prevention and treatment of human disease.”
• 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

Students should use the piazza group page. To access it, please first subscribe to the bmi217 class in http://www.piazza.com. After that this link will direct you to the group. Use it for discussion and posting questions/answers related to the course and problem sets.

Readings are linked from each lecture on the schedule page. Expect two to three readings per video lecture 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: You must use R and/or MySQL for the problem sets.

Problem set collaboration policy: You cannot talk with others in the class about the problem sets, and you must turn in your own individual work.

Problem set due dates: Problem sets are due at or before 11:59 PM on the due date. You are allotted 4 free late days total this quarter, so use them wisely for the four problem sets. After that, 20% off your grade per day late. Not using all your late days may influence your final grade (bump up) if borderline.

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. This year, we will not be having final presentations, as the class is completely virtual.

Final project details

• Problem Set 1: Integrative genomics, part 1: Genome wide association studies
• Problem Set 2: Integrative genomics, part 2: Gene Expression, variants, and functional annotation
• Problem Set 3: Integrative clinomics: Integrating molecular measurements with clinical data.
• Problem Set 4: Whole genome sequencing.

Based on the lectures and the readings. Open book.

Based on all the lectures and the readings. Open book.

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