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CSCI-4964/6964: Bioinformatics & Computational Biology, Spring 2013


Class: 10-11:50AM, MR, Low 3130
Instructor Office Hours: 12-1PM, MR



Announcements

  • Apr 10: Assignment 5 has been posted. Due date is 25th April, just before midnight
  • Mar 22: Assignment 4 has been posted. Due date is 9th April, just before midnight
  • Mar 15: Assignment 3 has been posted. Due date is 25th March, just before midnight
  • Feb 18: Assignment 2 has been posted. Due date is 1st March, just before midnight
  • Jan 31: Assignment 1 has been posted. Due date is 11th Feb, just before midnight
  • Jan 24: Check out the neat video Cellular Visions: Inner Life of a Cell and the other animations at Virtual Cell.
  • Jan 21: Course website is up, with the calendar and syllabus.



Calendar

A tentative sequence of topics to be covered in the classes; changes are likely as the course progresses.

Day: Date Topic Readings Lecture Notes
M: Jan 21 NO CLASS (Martin Luther King Holiday)
R: Jan 24 Introduction R1, R2 intro.ppt
M: Jan 28 Sequence Alignment R3 Lecture1.pdf
R: Jan 31 Alignment & Scoring Matrices R4, R5 Lecture2.pdf
M: Feb 4 More Scoring Matrices Lecture3.pdf
R: Feb 7 Database Search and Significance R6, R7 Lecture4.pdf
M: Feb 11 HMMs ( Guest Lecture: Prof. Chris Bystroff ) R8 HHM_1.pdf
R: Feb 14 Profile HMMs ( Guest Lecture: Prof. Chris Bystroff ) R9
T (Tuesday): Feb 19 Motif Discovery R10 Lecture5.pdf
R: Feb 21 Motif Discovery Lecture6.pdf
M: Feb 25 EXAM I
R: Feb 28 Suffix Trees, Genome Scale Alignment R11, R12 Lecture7.pdf
M: Mar 4 Suffix Arrays, Phylogenetic Trees R13, R14 Lecture8.pdf
R: Mar 7 Phylogenetic Trees R15 Lecture9.pdf
M: Mar 11 NO CLASS (spring break)
R: Mar 14 NO CLASS (spring break)
M: Mar 18 Genome Rearrangements R16, R17 Lecture10.pdf
R: Mar 21 Protein Structure Alignment R18, R19, R20 Lecture11.pdf
M: Mar 25 Structure Alignment R21 Lecture12.pdf
R: Mar 28 Protein Structure Prediction R22 Lecture13.pdf
M: Apr 1 EXAM II
R: Apr 4 Protein Threading Lecture14.pdf
M: Apr 8 Gene Expression Analysis R23 Lecture15.pdf
R: Apr 11 Gene Expression Clustering R24, R25 Lecture16.pdf
M: Apr 15 Biclustering Methods Lecture17.pdf
R: Apr 18 Network Biology R26, R27 Lecture18.pdf
M: Apr 22 Network Motifs R28 Lecture19.pdf
R: Apr 25 Network Motifs R29 Lecture20.pdf
M: Apr 29 Kernel Methods for Bioinfo R30, R31 Lecture21.pdf
R: May 2 Kernel Methods for Bioinfo Lecture22.pdf
M: May 6 EXAM III
R: May 9 NO CLASS (reading day)




Syllabus

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 next generation sequencing, 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.