E-mail: natasha [at] imperial [dot] ac [dot] uk

Office: 407 C Huxley

Lecture time and location: *Thursdays, 11-13h in LT 144, and Fridays 16-18h in LT 311*

Office hours: *Fridays after the class, 407 C Huxley*

The course will cover basic biological concepts related to the issues of sequence analysis and alignment, microarray data analysis, biological networks, fundamental graph theoretic algorithms, computational complexity and challenges in network analysis, existing post-genomic approaches for analyzing, modeling, and comparing biological networks, and applications of these approaches to understanding biological function, disease, and evolution. For more details, see the course syllabus below.

Practical work will be part of the coursework assignment and will include individual work on either: (a) development of code for a bioinformatics algorithm, or (b) data analysis. You will choose which option you will do. Completing only one of these two options will result in full marks. If you do both, both will be marked and you will be given the higher grade of the two.

For information about course goals, topics, organization, grading scheme, textbooks and readings, etc., please see the course introduction.

- Lecture 1: Sequencing and Genomics (Dr. Rice). (in pdf)
- Lecture 2: Sequence Analysis (Dr. Rice). (in pdf)
- Lecture 3: Sequence Analysis (Dr. Rice). (in pdf)
- Tutorial 1 questions; Tutorial 1 answers.
- Lecture 4: Functional Genomics and Microarray Analysis (Dr. Rice). (in pdf)
- Tutorial 2 questions; Tutorial 2 answers and data.
- Lecture 5: Functional Genomics and Microarray Analysis continued (Dr. Rice). (in pdf)
- Tutorial 3 questions; Tutorial 3 answers.
- Tutorial 4a: NCBI, Uniprot, BLAST; Tutorial 4b: Cytoscape. Tutorial 4c: Questions and Answers.
- Tutorial 5a questions; Tutorial 5a answers. Tutorial 5b: GraphCrunch Tutorial.
- Tutorial 6 questions; Tutorial 6 answers.
- Tutorial 7 questions and answers.
- Tutorial 8: Coursework model answers.

- Lecture 1: Course overview and Introduction to Biology. (in pdf)
- Lectures 2 and 3: Introduction to biological networks. (in pdf)
- Lectures 4 and 5: Introduction to graph theory. (in pdf)
- (OPTIONAL: Review of more graph algorithms; after slide 11 is optional reading and will not be assessed.)
- Lectures 6, 7 and 8: Network properties. (in pdf)
- Coursework – given out on Friday, Feb 13, 2015, due on Wednesday, March 4, 2015 by 2pm.
- Lectures 9 and 10: Network models. (in pdf)
- Lectures 11 and 12: Network comparisons and alignments. (in pdf)
- Lectures 13 and 14: Protein 3D structure (Dr. Noel Malod-Dognin)
- Lecture 15: Computational Methods for Network Integration; Course review.
- Optional Lectures 16 and 17: Graph clustering; interplay of network topology and biological function. (in pdf)

- Survey: “Graph-theoretic approaches for studying biological networks“, Tijana Milenkovic, PhD advancement, Univeristy of California, Irvine, 2008.
- Natasa Przulj and Tijana Milenkovic, “Computational Methods for Analyzing and Modeling Biological Networks“, a chapter in “
**Biological Data Mining**”, edited by Jake Chen and Stefano Lonardi, Chapman & Hall/CRC; 1 edition, September 1, 2009. - Sequence alignment (pdf, doc)

- Coursework (Given out on February 13, 2015. Due on March 4, 2015 by 2pm.)
- Sample solution to coursework.
- Exam (Date to be determined.)