CSCI1820: Algorithmic Foundations of Computational Biology (aka CS182 and formerly CSCI1950-L)

Semester:

Spring 2014

Schedule:

Tuesdays and Thursdays 1:00-2:20pm (K)

Location:

CIT 241 SWIG Boardroom

People:

Office Hours:

A note on final projects/exam

  • Everyone must come to the final exam time on May 12th at 2:00pm in our class room CIT 241!

  • If you are completing a final project, this counts as your 'final exam' and 25% of your grade as in the syllabus.

  • If you are not completing a final project, you will be implementing BLAST as Homework 8. We may give an option of implementing Ukonnen's algorithm instead. Either of these homeworks which will count as the final exam as far as requirements, but be weighted as a regular homework assignment. The BLAST assignment is already uploaded but we will give full details on Tuesday including extra notes on BLAST. It will be due on May 12th giving you 2 full weeks to complete it.

View the previous class's detailed course topics for a sample of teachings for this year's course. Please note that from year-to-year the amount of lectures and homeworks dedicated to specific chapters varies. This year, the course devotes a large amount of time to the topics of (in decreasing order of time spent): (1) algorithmics and statistical theory of genome assembly, (2) BLAST statistical theory, (3) efficient algorithms and data structures for mapping sequence reads, genes, and genomes to genomes, and (4) combinatorial and statistical theory of the DNA sequence regulatory architecture of genes. This is an intensive software coding class for computational science students; as an example, you will build your own genome assembly, BLAST algorithm, HMM for finding CpG islands, and suffix trees. Life science students who are not as familiar with coding will still advance their computational experience through appropriate assignments.

View the CSCI1820 Pillar Course Summary Slides.

Who?

YOU. The class is open to biologists and computer scientists, applied mathematicians, undergrads and grad students alike. The assignments will be adapted to your skillsets and interests.

When?

Tuesdays and Thursdays, 1:00pm. Spring 2014 semester.

Where?

CIT 241 The Swig Boardroom nestled into the far corner of CIT's 2nd floor. The whiteboard and presentation amenities are superb, and you can enjoy guest speakers and refreshments on most Wednesdays.

What? Computational Biology?!?

Computation and biology is a fundamental pairing and most biological research today has a computational component. To put it concisely, computation is a structure that applies mathematical method and logic to problems. Computation benefits from rigorous proof and the capacity to solve complex problems through iteration and/or recursion.

Biology is perhaps the most suitable science for computational understanding since it consists of many discrete (they either happen or they don't) events occurring in rapid time. Biology needs good statistical approaches and inference methods for discovery and verification of its findings.

Why Computational Biology?

This one requires a small history lesson and a summary of the field today. Computational Biology takes on very practical dilemmas such as interpreting sequences, modeling molecules, and making statistical prediction. It complements the vast amount of biological data being produced today with a means to make sense of it all. The sequencing of the human genome has given Computational Biology new relevance over the past decade: all of biology is presumed to be explainable from the genetic code.

If you are a computer scientist, you get to code sequence alignment and genome assembly programs just like the biology gods — and you'll be solving deep math problems that are very rewarding. If you are a biologist in the class, you will get an appreciation of algorithms (the beautiful, fundamental unit of computer science). And you will be able to bring back new tools to the laboratory. The course focuses on these topics: probability (aka "how to lie with statistics"), sequence analysis, achievements in molecular biology, Hidden Markov Models, the assembly of the human genome, and the study of evolution and mutation.