Support The World's Smartest Network

Help the New York Academy of Sciences bring late-breaking scientific information about the COVID-19 pandemic to global audiences. Please make a tax-deductible gift today.

This site uses cookies.
Learn more.


This website uses cookies. Some of the cookies we use are essential for parts of the website to operate while others offer you a better browsing experience. You give us your permission to use cookies, by continuing to use our website after you have received the cookie notification. To find out more about cookies on this website and how to change your cookie settings, see our Privacy policy and Terms of Use.

We encourage you to learn more about cookies on our site in our Privacy policy and Terms of Use.


Windows on Genetics

Windows on Genetics
Reported by
Don Monroe

Posted January 12, 2010


Much of computational biology and bioinformatics involves transforming various types of raw data into useful information. At a May 18, 2006, symposium of the Computational Biology & Bioinformatics Discussion Group, three speakers addressed various techniques for achieving this goal. The thread going through the three talks was that they involved diverse ways of looking at genetics.

Topics discussed include the conceptual basis and practical improvement of an algorithm for partitioning genomic DNA segment copy-number data into contiguous sets with the same copy number, the use of genome-wide association data to identify a specific genetic variant connected with age-related macular degeneration, and an algorithm that successfully classifies genes into functional groups solely on the basis of their interactions.

Use the tabs above to find a meeting report and multimedia from this event.

Web Sites

The Age-Related Eye Diseases Study (AREDS)
A study sponsored by the National Eye Institute to learn more about the natural history of and risk factors for cataract and age-related macular degeneration (AMD), and to evaluate the effects of high doses of nutritional supplements on disease progression.

An open source and open development software project for the analysis and comprehension of genomic data.

The Cancer Cell Map
A selected set of human cancer focused pathways, hosted at Memorial Sloan-Kettering Cancer Center.

The Computational Biology Center
The bioinformatics core at Memorial Sloan-Kettering Cancer Center.

The International HapMap Project
The HapMap Project catalogs haplotype variations among various human populations.

Pathguide: The Pathway Resource List
Resource list containing information on about 220 biological pathway resources.

Supplementary Information for the Paper, "Modular epistasis in yeast metabolism"
Includes information about a 2005 paper Roy Kishony's lab published in Nature Genetics, including documentation on the Prism algorithm.

Journal Articles

A Faster Circular Binary Segmentation Algorithm and Its Application to Expression Data

Lai, W. R., M. D. Johnson, R. Kucherlapati & P. J. Park. 2005. Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data. Bioinformatics 21: 3763-3770.

Levin, A. M., D. Ghosh, K. R. Cho & S. L. Kardia. 2005. A model-based scan statistic for identifying extreme chromosomal regions of gene expression in human tumors. Bioinformatics 21: 2867-2874.

Levin, B. & J. Kline. 1985. The cusum test of homogeneity with an application in spontaneous abortion epidemiology. Stat. Med. 4: 469-488.

Olshen, A. B., E. S. Venkatraman, R. Lucito & M. Wigler. 2004. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5: 557-572.

Pinkel, D. & D. G. Albertson. 2005. Array comparative genomic hybridization and its applications in cancer. Nat. Genet. 37(Suppl): S11-17.

Redon, R., S. Ishikawa, K. R. Fitch, et al. 2006. Global variation in copy number in the human genome. Nature 444: 444-454.

Venkatraman, E. S. & A. B. Olshen. 2006. A faster circular binary segmentation algorithm for the analysis of array CGH. Memorial Sloan-Kettering Cancer Center Department of Epidemiology and Biostatistics Working Paper Series 9. Full Text

Willenbrock, H. & J. Fridlyand. 2005. A comparison study: applying segmentation to array CGH data for downstream analyses. Bioinformatics 21: 4084-4091.

Genome-wide Association Studies: Practice and Theory

Edwards, A. O., R. Ritter 3rd, K. J. Abel, et al. 2005. Complement factor H polymorphism and age-related macular degeneration. Science 308: 421-424.

Fisher, S. A., G. R. Abecasis, B. M. Yashar, et al. 2005. Meta-analysis of genome scans of age-related macular degeneration. Hum. Mol. Genet. 14: 2257-2264.

Hageman, G. S., D. H. Anderson L. V. Johnson, et al. 2005. A common haplotype in the complement regulatory gene factor H (HF1/CFH) predisposes individuals to age-related macular degeneration. Proc. Natl. Acad. Sci. USA 102: 7227-7232. Full Text

Haines, J. L., M. A. Hauser, S. Schmidt, et al. 2005. Complement factor H variant increases the risk of age-related macular degeneration. Science 308:419-21.

International HapMap Consortium. 2005. A haplotype map of the human genome. Nature 437: 1299-1320.

Klein, R. J., C. Zeiss, E. Y. Chew, et al. 2005. Complement factor H polymorphism in age-related macular degeneration. Science 308: 385-389. Full Text

Risch, N. & K. Merikangas. 1996. The future of genetic studies of complex human diseases. Science 273: 1516-1517.

Rivera, A., S. A. Fisher, L. G. Fritsche, et al. 2005. Hypothetical LOC387715 is a second major susceptibility gene for age-related macular degeneration, contributing independently of complement factor H to disease risk. Hum. Mol. Genet. 14:3227-36.

Zareparsi, S., K. E. Branham, M. Li, et al. 2005. Strong association of the Y402H variant in complement factor H at 1q32 with susceptibility to age-related macular degeneration. Am. J. Hum. Genet. 77: 149-153. Full Text

Epistasis Interaction Networks

Famili, I., J. Forster, J. Nielsen & B. O. Palsson. 2003. Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. Proc. Natl. Acad. Sci. USA 100: 13134-13139. Full Text

Fong, S. S. & B. O. Palsson. 2004. Metabolic gene-deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes. Nat. Genet. 36: 1056-1058.

Ibarra, R. U., J. S. Edwards & B. O. Palsson. 2002. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420: 186-189.

Ooi, S. L., D. D. Shoemaker & J. D. Boeke. 2003. DNA helicase gene interaction network defined using synthetic lethality analyzed by microarray. Nat. Genet. 35: 277-286.

Ooi, S. L., X. Pan, B. D. Peyser, et al. 2006. Global synthetic-lethality analysis and yeast functional profiling. Trends Genet. 22: 56-63.

Segre, D., A. Deluna, G. M. Church & R. Kishony. 2005. Modular epistasis in yeast metabolism. Nat. Genet. 37: 77-83.
For additional supplementary information, click here.

Tong, A. H., G. Lesage, G. D. Bader, et al. 2004. Global mapping of the yeast genetic interaction network. Science 303: 808-813.

Varma, A. & B. O. Palsson. 1994. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl. Environ. Microbiol. 60: 3724-3731.

Yeh, P., A. I. Tschumi & R. Kishony. 2006. Functional classification of drugs by properties of their pairwise interactions. Nat. Genet. 38: 489-494.


Adam Olshen, PhD

Memorial Sloan-Kettering Cancer Center
email | web site | publications

Adam Olshen is an assistant attending biostatistician at Memorial Sloan-Kettering Cancer Center. His research focuses on the intersection of statistical genomics and bioinformatics, particularly on how copy number data can be derived from microarrays. He is also working to understand differences in gene expression data among institutions over time and to use tree-structured vector quantization to cluster protein data, and is collaborating with other researchers at MSKCC to bring computational methods to cancer research. Olshen received his PhD from the University of Washington.

Robert Klein, PhD

Memorial Sloan-Kettering Cancer Center
email | web site | publications

Robert Klein recently joined the staff of Memorial Sloan-Kettering Cancer Center as an assistant member of the Research Program in Cancer Biology and Genetics. He joined MSKCC in 2006 following completion of a postdoctoral fellowship in the Laboratory of Statistical Genetics at the Rockefeller University. He is now building a laboratory that will use an integrated combination of experimental and computational approaches to identify the genetic variants that influence the onset, progression, and treatment of cancer. His goals for the lab are to improve the power of association testing, conducting genetic studies of specific cancer phenotypes, and predicting the functionality of specific SNPs.

Roy Kishony, PhD

Harvard Medical School
email | web site | publications

Roy Kishony is an assistant professor in the Department of Systems Biology at Harvard Medical School. His lab combines theoretical and experimental approaches to study epistasis networks, to understand how combinations of individual perturbations can aggravate or alleviate the effects of each other.

Don Monroe

Don Monroe is a science writer based in Murray Hill, New Jersey. After getting a PhD in physics from MIT, he spent more than fifteen years doing research in physics and electronics technology at Bell Labs. He writes on biology, physics, and technology.