• Jan 22: Course website is up, with the calendar and syllabus.
• Jan 25: Check the Reading section to lookup the latest reading materials.
• Jan 26: Check out the neat video Cellular Visions: Inner Life of a Cell and the other animations at Virtual Cell.
• Jan 31: Assignment 1 has been posted.

Introduction

Computational Biology and Bioinformatics are essentially interchangeable terms, referring to the science of analyzing biological data. The goal of this course is to introduce the main topics and the frontiers of computational biology. The basic topics include sequence and protein structure analysis (alignment, evolution, search, motifs, and indexing). The emerging topics include gene expression analysis, network biology, and kernel data mining methods. The emphasis will be on the application of these methods to the various "omics" within computational systems biology, i.e., genomics, proteomics, interactomics, transcriptomics, and metabolomics.

Learning Objectives

After taking this course students will be

• knowledgeable about the fundamental computational biology tasks like sequence and structure analysis and evolution, biological networks, and data mining methods in bioinformatics
• able to understand the key algorithms for the main tasks
• able to implement and apply the techniques to real world omics datasets

Prerequisites

The pre-requisites for this course include data structures and algorithms, discrete mathematics, and probability & statistics. Knowledge of basic linear algebra will serve you well too. Assignments will require the use of Python, or R. Only these two scripting languages will be permitted for the assignments.

Textbook

There is no required text for the course. Reading materials will be posted online.

Grading Policy

Your grade will be a combination of the following items.

• Assignments (40%): There will be two types of assignments: homework questions from the book, and practically oriented assignments. For the latter you'll be asked to implement algorithms and apply them to real datasets, to complement the theory. Only python, and R are permitted for the scripting language.
• Exams (60%): There will be three exams covering the main topics of the course. The tentative exam dates are posted on the class schedule table. There is no comprehensive final exam. All exams are open book.

Attendance: Students are strongly encouraged to participate in the class, and should try to attend all classes.

Laptop Policy: No laptops or other electronic devices are permitted during lectures. You may however use these during exams to access course material online, or to use the calculator functions. Browsing the web for solutions, etc. is of course not permitted. Scripting (using python or R or other languages) is also not permitted to solve the exam questions, which are intended to be done by hand.

Academic Integrity

You may consult other members of the class on the homeworks, but this must be limited to the ideas only; you must submit your own implementation and work. Anytime you borrow material from the web or elsewhere, you must acknowledge the source.

The school takes cases of academic dishonesty very seriously, resulting in an automatic "F" grade for the course. Students should familiarize themselves with the relevant portion of the Rensselaer Handbook of Student Rights and Responsibilities on this topic.