BME110, Fall 2013, Section 01: Syllabus

BME 110/BIOL 181 Syllabus

BME 110/BIOL181:  Computational Tools for Biologists

Fall 2013
MWF 12:30PM - 1:40PM     Cowell Rm131


Please note:  All course information and material will be maintained and updated on the eCommons website.  


Karen H. Miga


Rm 507A, E2

Office Hours: Th 3-5pm (or by appointment) 


David Bernick 

Teaching Assistant

Kiley Graim

Office Hours: to be announced 


Discussion Sections

01A     DIS     20578   W       05:00PM-06:10PM Staff   J Baskin Engr 165
01B     DIS     20579   W       06:30PM-07:40PM Staff   J Baskin Engr 165

Lectures are supplemented weekly with 1hr10min sections led by the teaching assistant, Kiley Graim. Sections provide you with opportunities to explore the course’s material in a more intimate environment, go through assigned problem sets, as well as to dive into hands-on activities. 

Course Description

This course provides an introduction to sequence informatics and workflows relevant to high-throughput computational genomics.  In addition to introducing core topics of sequence alignment and database mining (historically, central to BME110), the curriculum this quarter is designed to give students experience with advanced topics and applications of next-generational sequencing technologies, including: genome-wide alignments of experimental datasets (RNA sequence and chromatin immunoprecipitation sequencing (ChIP-seq)), short-read assembly, and metagenomics.  Biological concepts and online tools presented in this course are expected to be highly valuable to any student interested in developing skills expected of contemporary computational biologists.  

No prior experience with scripting languages or knowledge of unix environment is required.  Class problem sets will utilize the Galaxy web-based platform ( -- an online analysis framework common to life science research --  to analyze and integrate publicly available software and genomic datasets.  Additionally, the use of Galaxy is intended to introduce the importance of reproducibility and transparency in data management through the use of published workflows.

The course is open to all science students with basic biochemistry and/or genetics or permission of the instructor as a prerequisite.


You must bring your own laptop to class every day.   If you do not have access to a laptop computer that you can use for this class, please contact the instructors as soon as possible.

You are expected to submit eight problem sets, online quizzes, a midterm, and a final project.

Required Text

** PLEASE NOTE **:  The class textbook is currently back ordered through CSHL.  You are advised to place your order in the near future to ensure that it arrives in a timely manner.  Alternatively, you could consider purchasing individual chapters relative to course discussions (available for instant download @ $7/chapter with links provided below):

Next-Generation DNA Sequencing Informatics

Edited by Stuart M. Brown, New York University School of Medicine



1. Introduction to DNA Sequencing

Stuart M. Brown

2. History of Sequencing Informatics

3. Visualization of Next-Generation Sequencing Data

Phillip Ross Smith, Kranti Konganti, and Stuart M. Brown

4. DNA Sequence Alignment

Efstratios Efstathiadis

5. Genome Assembly Using Generalized de Bruijn Digraphs

D. Frank Hsu

6. De Novo Assembly of Bacterial Genomes from Short Sequence Reads

Silvia Argimón and Stuart M. Brown

7. Genome Annotation

Steven Shen

8. Using NGS to Detect Sequence Variants

Jinhua Wang, Zuojian Tang, and Stuart M. Brown

9. ChIP-seq

Zuojian Tang, Christina Schweikert, D. Frank Hsu, and Stuart M. Brown

10. RNA Sequencing with NGS

Stuart M. Brown, Jeremy Goecks, and James Taylor

11. Metagenomics

Alexander Alekseyenko and Stuart M. Brown

12 High-Performance Computing in DNA Sequencing Informatics

Efstratios Efstathiadis and Eric R. Peskin


Galaxy Screencast and Vimeo tutorials


Additional reading material will be provided throughout the class relative to topics presented that week. 


Problem Sets/Homework: 35%

Midterm: 25%

Final Exam: 30%

Participation: 5%

On-line Exercises: 5%

Academic Honesty and Academic Integrity:

In recent years, there have been an increased number of cheating incidents in many UC campuses, and unfortunately, UCSC is no exception. The School of Engineering has a zero tolerance policy for any incident of academic dishonesty. If cheating occurs, there may be consequences within the context of the course, and in addition, every case of academic dishonesty is referred to the students' college Provost, who then sets the disciplinary process in motion. Cheating in any part of the course may lead to failing the course and suspension or dismissal from the university.

What is cheating? In short, it is presenting someone else's work as your own. Examples would include copying another student's written or electronic homework assignment, or allowing your own work to be copied. Although you may discuss problems with fellow students, your collaboration must be at the level of ideas only. Legitimate collaboration ends when you "lend", "borrow", or "trade" written or electronic solutions to problems, or in any way share in the act of writing or electronically sharing your answers. If you do collaborate (legitimately) or receive help from anyone, you must credit him or her by placing his or her name(s) at the top of your paper.  

What is Academic Integrity? This question is better answered with how we violate academic integrity. One prime example is fabrication.


  • In any academic exercise, submitting falsified data including bibliographic resources and experimental data, or altering graded coursework/exams and resubmitting to the instructor for a higher score.

Another example of violating academic integrity is Facilitating Academic Dishonesty:

  • One form of this is answering questions on someone else's exam or doing someone else's homework for them.
  • Another form is helping another student take a test (allowing them to cheat from you).