02-715 Advanced Topics in Computational Genomics

02-715 Advanced Topics in Computational Genomics, Fall 2011

Course Information

Instructor: Seyoung Kim (Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University)
Location: 9115 Gates Hillman Complex (for the first two weeks until 9/8), 4303 Gates Hillman Complex (after 9/22)
Time: Tuesday & Thursday 1:30-2:50pm
Office hours: Tuesday 3:00-4:00pm

Course Description

Research in biology and medicine is undergoing a revolution due to the availability of high-throughput technology for probing various aspects of a cell at a genome-wide scale. The next-generation sequencing technology is allowing researchers to inexpensively generate a large volume of genome sequence data. In combination with various other high-throughput techniques for epigenome, transcriptome, and proteome, we have unprecedented opportunities to answer fundamental questions in cell biology and understand the disease processes with the goal of finding treatments in medicine. The challenge in this new genomic era is to develop computational methods for integrating different data types and extracting complex patterns accurately and efficiently from a large volume of data. This course will discuss computational issues arising from high-throughput techniques recently introduced in biology, and cover very recent developments in computational genomics and population genetics, including genome structural variant discovery, association mapping, epigenome analysis, cancer genomics, and transcriptome analysis. The course material will be drawn from very recent literature. Class sessions will consist of lectures and discussions of recent papers led by the instructor. This course assumes a basic knowledge of machine learning and computational genomics (equivalent to 15-781 and 02-710).

Grading

The requirements of this course consists of short write-ups of summary/critique of the required readings for each class session, class participation, paper presentation, and final projects. The grading breakdown is as follows:

Write-ups for required reading (30%)
Class participation (20%)
Paper presentation (20%)
Final project (30%)

Write-ups for Required Reading: We expect students to read the papers marked as required readings (R) in the course syllabus before the class meets. Students are required to submit a short write-up (less than 300 words for each paper) of summary/critique of the required readings by 12am on the day before each class. The write-ups should be submitted to a dropbox available through the course blackboard.

Class Participation: We expect students to participate in discussions of the papers in each class session.

Paper Presentation: The class sessions during Week 13-15 will consist of student presentations of recent papers in computational genomics.

Final Project: In a class project, students will develop new computational methods and test them on genomic data. Also, students can take existing methods (or extend the existing methods) and apply them to genomic data. Students can work on a project either on their own or as a team of up to two students. A one-page project proposal is due by Tuesday, November 1, in class, and we will have a poster session on the last day of the course. The final project report is due by Wednesday (December 14th) (dropbox in the course blackboard will be made available).

Syllabus and Course Schedule (tentative)

(Note: the papers marked with (R) are required readings.)

Date	Topics	Lectures/Readings	Additional Readings
Tue 8/30	Introduction [slides]	Course overview and introduction
Population Genetics
Thur 9/1	Population Structure [slides]	(R) Analysis of population structure: a unifying framework and novel methods based on sparse factor analysis. PLoS Genetics, 2010. (B. Engelhardt and M. Stephens) (R) Inference of population structure using multilocus genotype data. Genetics, 2000. (J.K. Pritchard et al.)
Genome-wide Association Study (GWAS)
Tue 9/6	GWAS Overview [slides]	Lecture: Overview of genetic association studies (R) Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nature Genetics, 2008. (J. Zhu et al.)	A tutorial on statistical methods for population association studies. Nature Reviews Genetics, 2006. (D.J. Balding)
Thur 9/8	GWAS and Population Structure [slides]	(R) Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics, 2006. (A. Price et al.) (R) Association mapping in structured populations. AJHG 2000. (J.K. Pritchard et al.)
Tue 9/13	eQTL Mapping [slides]	Lecture: Sparse regression methods (R) Learning a prior on regulatory potential from eQTL data. PLoS Genetics, 2009. (S-I. Lee et al.)
Thur 9/15	Epistasis [slides]	(R) Bayesian inference of epistatic interactions in case-control studies. Nature Genetics, 2007. (Y. Zhang and J. Liu) Screen and clean: a tool for identifying interactions in genome-wide association studies. Genetic Epidemiology, 2010. (J. Wu et al.)
Tue 9/20	Structured Genome-Phenome Association I [slides]	(R) Statistical estimation of correlated genome associations to a quantitative trait network. PLoS Genetics, 2009. (S. Kim and E.P. Xing) Network-constrained regularization and variable selection for analysis of genomic data. Bioinformatics, 2008. (C. Li and H. Li)
Thur 9/22	Structured Genome-Phenome Association II [slides]	Lecture: Structured association
Evolution, Natural Selection
Tue 9/27	Natural Selection Overview [slides]	(R) Positive Natural Selection in the Human Lineage. Science, 2006. (P. Sabeti et al.) A Draft Sequence of the Neandertal Genome. Science, 2010. (R.E. Green et al.)
Thur 9/29	Detecting Selective Sweeps [slides]	(R) Genomic scans for selective sweeps using SNP data. Genome Research, 2005. (R. Nielsen et al.) Genome-wide detection and characterization of positive selection in human populations. Nature, 2007. (P. C. Sabeti et al.)
Tue 10/4	Comparative Genomics [slides]	(R) Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Research, 2005. (A. Siepel et al.) Phylogenomics of primates and their ancestral populations. Genome Research, 2009. (A. Siepel)
Structural Variations in Genomes
Thur 10/6	Structural Variation Overview (Guest lecturer: Dr. Marcel Schulz) [slides]	Lecture: Structural variations Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics, 2009. (K. Ye et al.)	Computational methods for discovering structural variation with next-generation sequencing. Nature Methods, 2009. (P. Medvedev, M. Stanciu, M. Brudno)
Tue 10/11	Structural Variant Detection [slides]	(R) MoDIL: detecting small indels from clone-end sequencing with mixtures of distributions. Nature Methods, 2009. (S. Lee et al.) (R) Breakdancer: an algorithm for high-resolution mapping of genomic structural variation. Nature Methods, 2009. (K. Chen et al.)
Thur 10/13	Copy Number Variations and Associations [slides]	(R) Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs. Nature Genetics, 2008. (J. Korn et al.) Copy-number variation and association studies of human disease. Nature Genetics, 2007. (S. A. McCarroll and D. M. Altshuler)
Epigenetics
Tue 10/18	Epigenetics Overview, Chromatin State Modeling [slides]	(R) Discovery and characterization of chromatin states for systematic annotation of the human genome. Nature Biotechnology, 2010. (J. Ernst and M. Kellis) Comprehensive analysis of the chromatin landscape in Drosophila melanogaster. Nature, 2010. (P. Kharchenko et al.)
Thur 10/20	Nucleosome Positioning [slides]	(R) Learning a weighted sequence model of the nucleosome core and linker yields more accurate predictions in Saccharomyces cerevisiae and Homo sapiens. PLoS Computational Biology, 2010. (S. Reynolds et al.) Predicting nucleosome positioning using multiple evidence tracks. RECOMB 2010. (S. Reynolds et al.)
Tue 10/25	Epigenome and Gene Expressions [slides]	(R) Histone modification levels are predictive for gene expression. PNAS, 2010. (R. Karlic et al.)
Cancer Genomics
Thur 10/27	Cancer Genomics Overview [slides]	Lecture: Cancer genomics overview (R) Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations. Cancer Research, 2009. (H. Carter et al.)	The Hallmarks of Cancer. Cell, 2000. (D. Hanahan and R. Weinberg) Hallmarks of Cancer: The Next Generation. Cell, 2011. (D. Hanahan and R. Weinberg)
Tue 11/1	Detecting Driver Mutations [slides]	(R) An integrated approach to uncover drivers of cancer. Cell, 2010. (U. Akavia et al.) Network modeling of the transcriptional effects of copy number aberrations in glioblastoma. Molecular Systems Biology, 2011. (R. Jornsten et al.)
Thur 11/3	Detecting Driver Mutations [slides]	(R) Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics, 2010. (C. J. Vaske et al.)
Gene Interaction Networks
Tue 11/8	Gene Interaction Networks and Transcriptomics I [slides]	(R) High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions. PLoS Computational Biology, 2010. (P. Agius et al.) Mismatch string kernels for discriminative protein classification. Bioinformatics, 2004. (C. Leslie et al.)
Thur 11/10	Gene Interaction Networks and Transcriptomics II [slides]	(R) A feature-based approach to modeling protein−DNA interactions. PLoS Computational Biology, 2008. (E. Sharon, S. Lubliner, E. Segal) (R) Automated identification of pathways from quantitative genetic interaction data. Molecular Systems Biology, 2010. (A. Battle et al.)
Tue 11/15	PPI Network Alignment [slides]	(R) Global alignment of multiple protein interaction networks with application to functional orthology detection. PNAS, 2008. (R. Singh, J. Xu, B. Berger) IsoRankN: spectral methods for global alignment of multiple protein networks. Bioinformatics, 2009. (C. Liao et al.)
RNA-Seq, Alternative Splicing
Thur 11/17	RNA-Seq, Alternative Splicing [slides]	(R) Model-based detection of alternative splicing signals. Bioinformatics, 2010. (Y. Barash et al.) Transcriptome genetics using second generation sequencing in a Caucasian population. Nature, 2010. (S. Montgomery et al.)
Metagenomics
Tue 11/22	Metagenomics [slides]	(R) Genovo: de novo assembly for metagenomes. RECOMB, 2010. (J. Laserson, V. Jojic, D. Koller) A human gut microbial gene catalogue established by metagenomic sequencing. Nature, 2010. (J. Qin et al.)
Thur 11/24	Thanksgiving holiday, no class
Tue 11/29	Student presentations
Thur 12/1	Student presentations
Tue 12/6	Student presentations
Thur 12/8	Poster session

Resources

International HapMap Project
Model organism ENCyclopedia Of DNA Elements (modENCODE) Project
1000 Genomes Project: a deep catalog of human genetic variation
Sage Bionetworks: a repository of various genomic datasets
The Cancer Genome Atlas (TCGA)