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CSCI-4967/6965: Bioinformatics & Computational Biology, Spring 2016


Class: 10-11:50AM, TF, Sage 3101
Instructor Office Hours: 12-1PM, TF





Announcements

  • Feb 3: Assign1 has been posted. Due date is Feb 12th, before midnight.
  • Feb 2: You should have got an email from Piazza to access the readings. Log into Piazza to check the post.
  • Jan 25: Check out the animations at Virtual Cell.
  • Jan 20: Course website is up, with the calendar and syllabus.



Calendar & Readings

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

Day: Date Topic Readings Lecture Notes
T: Jan 26 Introduction I R1, R2 intro.ppt
F: Jan 29 Introduction II R1, R2 lecture2.pdf
T: Feb 2 Sequence Alignment R3, R4 lecture3.pdf
F: Feb 5 Local Alignment & Scoring Matrices R3, R4 lecture4.pdf
T: Feb 9 Scoring Matrices II lecture5.pdf
F: Feb 12 NO CLASS
T: Feb 16 NO CLASS (President's Day)
F: Feb 19 Database Search
T: Feb 23 Profile HMMs
F: Feb 26 Motif Discovery
T: Mar 1 Motif Discovery
F: Mar 4 Suffix Trees & Arrays
T: Mar 8 Genome Scale Alignment
F: Mar 11 EXAM I
T: Mar 15 NO CLASS (spring break)
F: Mar 18 NO CLASS (spring break)
T: Mar 22 Phylogenetic Trees
F: Mar 25 Phylogenetic Trees, Genome Rearrangements
T: Mar 29 Protein Structure Alignment
F: Apr 1 Protein Structure Alignment
T: Apr 5 Protein Structure Prediction
F: Apr 8 Protein Threading
T: Apr 12 Gene Expression Analysis
F: Apr 15 Gene Expression Clustering
T: Apr 19 Biclustering Methods
F: Apr 22 Network Biology
T: Apr 26 Network Motifs
F: Apr 29 Network Motifs
T: May 3 Kernel Methods for Bioinfo
F: May 6 Kernel Methods for Bioinfo
T: May 10 EXAM II
F: May 13 NO CLASS (Study Review)




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.

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 (60%): The assignments are practically oriented; you'll be asked to implement algorithms and apply them to real datasets, to complement the theory. The will be 6-7 assignments during the semester.
  • Exams (40%): There will be two 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: You may use Laptops and other portable devices 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 other languages) is also not permitted to solve the exam questions, which are intended to be done by hand.

Late Assignments: Most assignments will be due just before midnight on the due date. Students can get an automatic one day extension for a 20% grade penalty. No late assignments will be accepted after the midnight following the due date.

Academic Integrity

You may consult other members of the class on the assignments, but you must submit your own work. For instance you may discuss general approaches to solving a problem, but you must implement the solution on your own (similarity detection software may be used). 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.

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Page last modified on February 09, 2016, at 02:20 PM