02725 – Computational Methods for Proteogenomics and Metabolomics

Proteomics and metabolomics are the large scale study of proteins and metabolites, respectively. In contrast to genomes, proteomes and metabolomes vary with time and the specific stress or conditions an organism is under. Applications of proteomics and metabolomics include determination of protein and metabolite functions (including in immunology and neurobiology) and discovery of biomarkers for disease. These applications require advanced computational methods to analyze experimental measurements, create models from them, and integrate with information from diverse sources. This course specifically covers computational mass spectrometry, structural proteomics, proteogenomics, metabolomics, genome mining and metagenomics. Prerequisites: 02-250 or 02-604.

Lectures

Lecture 1. Introduction to metabolomics. Suggested reading, David Wishart, Chapter 10. Metabolomics in Humans and Other Mammals, http://onlinelibrary.wiley.com/doi/10.1002/9780470105511.ch10/summary
Lecture 2. Metabolic identification by MS/MS; Suggested reading, Juhu Rousu, Metabolite identification and molecular fingerprint prediction through machine learning.
Lecture 3, 4. Kernel learning, and its applications to chemoinformatics (powerpoint slides attached). Suggested reading, Pierre Baldi, Graph Kernels for Chemical Informatics, https://www.ncbi.nlm.nih.gov/pubmed/16157471
Lecture 5, 6. (Hidden Markov Models) and Gene Finding, Suggested reading: Richard Durbin, Biological Sequence Analysis, Chapter 3, Markov chains and hidden Markov models. GeneMark.hmm: new solutions for gene finding. Lukashin AV, Borodovsky M. https://www.ncbi.nlm.nih.gov/pubmed/9461475
Lecture 7. (Gene Structure & Gene Finding). GeneMark hmm: new solutions for gene finding, A. Lukashin & M. Borodovsky, https://www.ncbi.nlm.nih.gov/pubmed/9461475
Lecture 8,9. Suggested Reading: Introduction to Bioinformatics Algoritms, Jones and Pevzner, Chapter 10, Trees and Clustering.
Lecture 10, 11. Biclustering Algorithms: A Survey, Amos Tanay, Roded Sharan, Ron Shamir, http://www.cs.tau.as.il/~roded/articles/bicrev.pdf
Lecture 12, 13. Introduction to Proteomics, suggested reading: Mass Spectrometry based Proteomics, R. Abersold, and M. Mann, https://www.ncbi.nlm.nih.gov/pubmed/12634793
Lecture 14, 15.

Homework

Homework 1 – Due February 22nd
Homework 2 – Due March 1st
Homework 3 – Due March 20th, by 1:30pm
Homework 4 – Due April 3rd, before class
Homework 5 – Due April 17th, by 1:30pm

Rules:

1. Homework is due on the due date before start of the lecture.

2. Solution to each problem should marked appropriately.

3. All the files should be clearly and correctly named as announced (ex. “HW2_2.txt”). Please enclose all the files in a folder and compress it, and then submit the compressed folder to the instructor’s e-mail (Hosein Mohimani <hoseinm@andrew.cmu.edu>).

4. We recommend you typeset your homework using appropriate software such as LATEX. If you are writing, please make sure your homework is cleanly written up and legible. The TAs will not invest undue effort to decrypt bad handwriting.

5. Code submission: for programming questions, you must submit the complete source code of your implementation. Remember to include a small README file and a script that would help us execute your code. We recommend to use Matlab / Python / R.

6. Collaboration: It is acceptable for you to collaborate with other students, but you should write up your own solution and implement your own code. You must indicate on each homework the students with whom you collaborated.

List of Papers for Final Presentations:
(deadline to pick paper for final presentation is March 1, 2018)

· DeepNovo:
http://www.pnas.org/content/114/31/8247

· Fast Unifrac:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2797552/

· Genovo:
http://online.liebertpub.com/doi/abs/10.1089/cmb.2010.0244

· NRPSpredictor2:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125756/

· Topic modeling for untargeted metabolomics:
http://www.pnas.org/content/113/48/13738

· Percolator:
https://www.nature.com/articles/nmeth1113