MaCH-Admix: genotype imputation for admixed populations, by Eric Yi Liu, Mingyao Li, Wei Wang, and Yun Li, Genetic Epidemiology, 2012.

Inferring Ancestry in Admixed Populations using Microarray Probe Intensities, by Chen-Ping Fu, Catherine E. Welsh, Leonard McMillan, and Fernando Pardo-Manuel de Villena, ACM-BCB, 2012.

Discovery of novel variants in genotyping arrays improves genotype retention and reduces ascertainment bias, by John P Didion, Hyuna Yang, Keith Sheppard, Chen-Ping Fu, Leonard McMillan, Fernando Pardo-Manuel de Villena, and Gary A Churchill, BMC Genomics 2012, 13:34

Status and access to the Collaborative Cross Publication, by Catherine E. Welsh, Darla R. Miller, Kenneth F. Manly, Jeremy Wang, Leonard McMillan, Grant Morahan, Richard Mott, Fuad A. Iraqi, David W. Threadgill, and Fernando Pardo-Manuel de Villena, Mammalian Genome 2012: 10.1007/s00335-012-9410-6.

Accelerating the Inbreeding of Multi-Parental Recombinant Inbred Lines Generated By Sibling Matings, by Catherine E. Welsh and Leonard McMillan, G3 2012: 10.1534/g3.111.001784.

High resolution genetic mapping using the mouse Diversity Outbred population, by Svenson, Karen. L., Daniel. M. Gatti, William. Valdar, Catherine. E. Welsh, Riyan. Cheng, Elissa J. Chessler, Abraham A. Palmer, Leonard McMillan, and Gary A. Churchill, Genetics, 2012 190: 437-447.

WikiGWA: an open platform for collaborative utilization of genome-wide association (GWA) findings, by Jie Huang, Eric Yi Liu, Ryan Welch, Cristen Willer, Lucia A. Hindorff, and Yun Li, European Journal of Human Genetics, 2012.

Genotype imputation of Metabochip SNPs using a study specific reference panel, by Eric Yi Liu et al. Genetic Epidemiology, 36(2), 2012.

Single Nucleotide Polymorphism (SNP) Detection and Genotype Calling from Massively Parallel Sequencing (MPS) Data, by Yun Li, Wei Chen, Eric Yi Liu, and Yi-Hui Zhou, Statistics in Biosciences, 2012.

Hierarchical Co-Clustering Based on Entropy Splitting, by Wei Cheng, Xiang Zhang, Feng Pan, and Wei Wang, Proceedings of the ACM Conference on Information and Knowledge Management (CIKM), 2012.

Inferring Novel Associations between SNP Sets and Gene Sets in eQTL Study using Sparse Graphical Model, by Wei Cheng, Xiang Zhang, Yubao Wu, Xiaolin Yin, Jing Li, David Heckerman, and Wei Wang, Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM-BCB), 2012.

Dual Transfer Learning, by Mingsheng Long, Jianmin Wang, Guiguang Ding, Wei Cheng, Xiang Zhang, and Wei Wang, Proceedings of the SIAM International Conference on Data Mining (SDM), 540-551, 2012.

Learning Transcriptional Regulatory Relationships Using Sparse Graphical Models, by Xiang Zhang, Wei Cheng, Jennifer Listgarten, Carl Kadie, Shunping Huang, Wei Wang, and David Heckerman, PLoS One, 7(5): e35762, 2012.

Imputation of SNPs in inbred mice using local phylogeny, by Jeremy R Wang, Fernando Pardo-Manuel de Villena, Heather A Lawson, James M Cheverud, Gary A Churchill, and Leonard McMillan, Genetics, February 2012 190:449-458.

The Genome Architecture of the Collaborative Cross Mouse Genetic Reference Population, by the Collaborative Cross Consortium, Genetics, February 2012 190:389-401.

Transcriptome Atlases Of Mouse Brain Reveals Differential Expression Across Brain Regions And Genetic Backgrounds, by Wei Sun, Seunggeun Lee, Vasyl Zhabotynsky, Fei Zou, Fred Wright, Jim Crowley, Zaining Yun, Ryan Buus, Darla Miller, Jeremy Wang, Leonard McMillan, Fernando Pardo-Manuel de Villena, and Patrick F Sullivan, G3 February 2012 2(2):203-211.

Comparative analysis and visualization of multiple collinear genomes, by Jeremy Wang, Fernando Pardo-Manuel de Villena, and Leonard McMillan, BMC Bioinformatics, 2012 13(Suppl 3):S13.

Measuring Opinion Relevance in Latent Topic Space, by Wei Cheng, Xiaochuan Ni, Jian-Tao Sun, Xiaoming Jin, Hye-Chung Kum,Xiang Zhang, and Wei Wang, Proceedings of the IEEE International Conference on Social Computing (SocialCom),323-330, 2011.

Dynamic Visualization and Comparative Analysis of Multiple Collinear Genomic Data, by Jeremy Wang, Fernando Pardo-Manuel de Villena, and Leonard McMillan, ACM Bioinformatics and Computational Biology, 2011.

Clustering with relative constraints, by Eric Yi Liu, Zhaojun Zhang, and Wei Wang, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD), 2011.

Genetic analysis of complex traits in the emerging collaborative cross, by David L Aylor, William Valdar, Wendy Foulds-Mathes, Ryan J Buus, Ricardo A Verdugo, Ralph S Baric, Martin T Ferris, Jeff A Frelinger, Mark Heise, Matt B Frieman, Lisa E Gralinski, Timothy A Bell, John D Didion, Kunjie Hua, Derrick L Nehrenberg, Christine L Powell, Jill Steigerwalt, Yuying Xie, Samir NP Kelada, Francis S Collins, Ivana V Yang, David A Schwartz, Lisa A Branstetter, Elissa J Chesler, Darla R Miller, Jason Spence, Eric Yi Liu, Leonard McMillan, Abhishek Sarkar, Jeremy Wang, Wei Wang, Qi Zhang, Karl W Broman, Ron Korstanje, Caroline Durrant, Richard Mott, Fuad A Iraqi, Daniel Pomp, David Threadgill, Fernando Pardo-Manuel de Villena, and Gary A Churchill, Genome Research, 2011.

Subspecific origin and haplotype diversity in the laboratory mouse, by Hyuna Yang, Jeremy R Wang, John P Didion, Ryan J Buus, Timothy A Bell, Catherine E Welsh, Francois Bonhomme, Alex Hon-Tsen Yu, Michael W Nachman, Jaroslav Pialek, Priscilla Tucker, Pierre Boursot, Leonard McMillan, Gary A Churchill, and Fernando Pardo-Manuel de Villena, Nature Genetics, 2011 43 (7), 648-655.

Efficient genome ancestry inference in complex pedigrees with inbreeding, by Eric Yi Liu, Qi Zhang, Leonard McMillan, Fernando Pardo-Manuel de Villena,  and Wei Wang, Proceedings of the 18th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB), Bioinformatics, 26(12), 2010.

Genome-wide compatible SNP intervals and their properties, by Jeremy Wang, Kyle J Moore, Qi Zhang, Fernando Pardo-Manuel de Villena, Wei Wang, and Leonard McMillan, ACM Bioinformatics and Computational Biology, 2010.

A fast approximation to multidimensional scaling, by Tynia Yang, Jinze Liu, Leonard McMillan, and Wei Wang, Proceedings of the ECCV Workshop on Computation Intensive Methods for Computer Vision (CIMCV), 2006.

Poclustering: lossless clustering of dissimilarity data, by Jinze Liu, Qi Zhang, Wei Wang, Leonard McMillan, and Jan Prins, Proceedings of 2007 SIAM International Conference on Data Mining (SDM2007), 2007.

Inferring missing genotypes in large SNP panels using fast nearest-neighbor searches over sliding windows, by Adam Roberts, Leonard McMillan, Wei Wang, Joel Parker, Ivan Rusyn, and David Threadgill, Proceedings of the 15th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB), 2007.

On the subspecific origin of the laboratory mouse, by Hyuna Yang, Timothy Bell, Gary Churchill, and Fernando Pardo-Manuel de Villena, Nature Genetics Jul 22, 2007.

The polymorphism architecture of mouse genetic resources elucidated using genome-wide resequencing data: implications for QTL discovery and systems genetics, by Adam Roberts, Fernando Pardo-Manuel de Villena, Wei Wang, Leonard McMillan, and David Threadgill, Mammalian Genome, Aug 3, 2007.

Sample selection for maximal diversity, by Feng Pan, Adam Roberts, Leonard McMillan, Fernando Pardo Manuel de Villena, David Threadgill, and Wei Wang, 2007 IEEE International Conference on Data Mining (ICDM’07)

An imputed genotype resource for the laboratory mouse, by Jin P. Szatkiewicz, Glen L. Beane, Yueming Ding, Lucie Hutchins, Fernando Pardo Manuel de Villena, and Gary Churchill, Mammalian Genome, 19,3, 199-208.

CARE: Finding Local Linear Correlations in High Dimensional Data, by Xiang Zhang, Feng Pan, and Wei Wang, Proceedings of 2008 International Conference on Data Engineering (ICDE’08).

CRD: Fast Co-clustering on Large Datasets Utilizing Smapling-Based Matrix Decomposition, by Feng Pan, Xiang Zhang and Wei Wang, Proceedings of  2008 SIGMOD/PODS Conference (SIGMOD’08).

FastANOVA: an efficient algorithm for genome-wide association study, by Xiang Zhang, Fei Zou, and Wei Wang. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD’08).

Mining non-redundant high order correlations in binary data, by Xiang Zhang, Feng Pan, Wei Wang, and Andrew Nobel. Proceedings of the 34th International Conference on Very Large Data Bases (VLDB’08).

Genotype Sequence Segmentation: Handling Constraints and Noise, by Qi Zhang, Wei Wang, Leonard McMillan, Jan Prins, Fernando Pardo-Manuel de Villena, and David Threadgill, Proceedings of 8th Workshop on Algorithms in Bioinformatics (WABI’08), 2008.

REDUS: finding reducible subspaces in high dimensional data, by Xiang Zhang, Feng Pan, and Wei Wang. Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM’08).

TreeQA: Quantitative Genome Wide Association Mapping Using Local Perfect Phylogeny Trees, by Feng Pan, Leonard McMillan, Fernando Pardo-Manuel de Villena, David Threadgill and Wei Wang. Proceedings of the the 14th Pacific Symposium on Biocomputing (PSB’ 09) .

Inferring Genome-Wide Mosaic Structure, by Qi Zhang, Wei Wang, Leonard McMillan, Fernando Pardo-Manuel de Villena, and David Threadgill. Proceedings of the the 14th Pacific Symposium on Biocomputing (PSB’ 09) .

FastChi: an efficient algorithm for analyzing gene-gene interactions, by Xiang Zhang, Fei Zou, and Wei Wang. Proceedings of the the 14th Pacific Symposium on Biocomputing (PSB’ 09) .

COE: a general approach for efficient genome-wide two-locus epistasis test in disease association study, by Xiang Zhang, Feng Pan, Yuying Xie, Fei Zou, and Wei Wang. Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology (RECOMB), pp. 253-269, 2009.

Structure-based function inference using protein family-specific fingerprints, by Deepak Bandyopadhyay, Jun Huan, Jinze Liu, Jan Prins, Jack Snoeyink, Wei Wang, and Alexander Tropsha, Protein Science, v.15, 2006, p. 1537

Benchmarking the effectiveness of sequential pattern mining methods, by Hye-Chung Kum, J. H. Chang, and Wei Wang, Data and Knowledge Engineering, v.60, 2007, p. 30.

Sequential pattern mining in multi-databases via multiple alignment, by Hye-Chung Kum, Joong-Hyuk Chang, and Wei Wang, Data Mining and Knowledge Discovery (DMKD), v.12, 2006, p. 151

CRD: fast co-clustering on large datasets utilizing sample-based matrix decomposition, by Feng Pan, Xiang Zhang, and Wei Wang, Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), 2008, p. 173.

Accelerating Profile Queries in Elevation Maps, by Pan Feng, Wei Wang, and Leonard McMillan, International Conference on Data Engineering (ICDE 2007), 2007.

Mining approximate order preserving clusters in the presence of noise, by Mengsheng Zhang, Wei Wang, and Jinze Liu, Proceedings of the 24th IEEE International Conference on Data Engineering (ICDE), 2008, p. 160

Split-order distance for clustering and classification hierarchies, by Zhang, Q., Liu, E. Y., Sarkar, A., and Wang, W., Proceedings of the 21st International Conference on Scientific and Statistical Database Management (SSDBM), 2009, p. 517.

We are a multidisciplinary research group focused on a new field of integrated biomedical research called systems genetics. Systems genetics is a non-reductionist field that was simply not practical even a few years ago: it relies on diverse multiscale and multiorgan phenotype data sets obtained from large segregating populations. System genetics is a biological science that relies on statistical methods, advanced computational algorithms, visualization, and high-performance computing. Systems genetics has the goal and potential to dissect and reassemble complex molecular and phenotypic networks in the context of natural genetic variation. Our group is a collaboration between members of the Departments of Biostatistics, Computer Science, Environmental Science and Engineering, and Genetics.

Meetings

During the fall semester, the UNC Computational Genetics Working Group will hold its regular weekly group meetings on Tuesday afternoons from 4:00-5:30 pm in FB141 in Brooks Hall.

News

September, 2012

Completed major site maintenance (it hadn’t been changed in over 3 years)

March 4, 2009

UNC Compgen has begun construction on a pool of computational resources to support bioinformatics research and the collaborative cross. Over the next month we’ll be bringing online a large storage array of NAS disks to store large, terabyte-size datasets as well as a high performance compute cluster to support the tools developed at UNC for bioinformatics research.

December 5, 2008:

In January, the U.S. Department of Energy’s (DOE) Oak Ridge National Laboratory (ORNL) is moving its unique colony of 8,000 mice, known as the Collaborative Cross, to the University of North Carolina at Chapel Hill. (More information)

October 31, 2008:

Compgen.unc.edu has been replaced with a new server. Databases and websites should be functional again. Please contact hulbert@email.unc.edu if you are having trouble accessing any resources.

May 15, 2007:

CompGen member Prof. Wei Wang, 2007-2008 recipient of the Phillip & Ruth Hettleman Prize for Artistic and Scholarly Achievement, will be presenting her award lecture on Surfing the Data Flood at The Carolina Club from 2pm-4pm. Refreshments will be served.

Research Sponsors

EPA STAR RD832720: “Environmental Bioinformatics Research Center to Support Computational Toxicology Applications”

NSF IIS 0534580: “Visualizing and Exploring High-dimensional Data”

NSF IIS 0448392: “CAREER: Mining Salient Localized Patterns in Complex Data”

NSF IIS 0812464: ” III-Core: Discovering and Exploring Patterns in Subspaces”

NIH U01 CA105417: “Integrative Genetics of Cancer Susceptibility”

NIH U01 CA134240: ” Systems Genetics Research Consortium”

NIH GM 076468: “The Center for Genome Dynamics at Jackson Laboratory:
An NIGMS National Center of Systems Biology”

UCRF: “University Cancer Research Fund”

Microsoft Research Grant

Microsoft New Faculty Fellows

Join us!

If you have a problem related to systems biology or genetics, are in need of an analysis or visualization tool for your data, or you would like to join our research group, please contact Leonard McMillan via email ( ) or Wei Wang via email (weiwang@cs.unc.edu).

The Mouse Phylogeny Viewer (MPV) is a custom genome browser designed to provide the user with reliable and detailed answers to questions on the haplotype diversity and phylogenetic origin of the genetic variation underlying any genomic region of most laboratory strains (both classical and wild-derived). A detailed explanation of the methods used and meaning of the data has been reported in “Subspecific origin and haplotype diversity in the laboratory mouse” (Yang et al. 2011).

MPV allows the user to select a region of the genome and a set of laboratory strains and/or wild caught mice (i.e, samples). The region is selected by specifying the start (e.g. 31200000 or 31200K or 31.2M), and end of the interval and the chromosome (i.e, autosome number and X chromosome). Samples can be selected by name or by entire set.

Data sets

SNPs on the Mouse Diversity Array

This display provides local density of SNPs on the Mouse Diversity Array. The browser provides a histogram in which the height of the bar is proportional to the local SNP density.

Subspecific Origin

In windows representing large genomic regions, this section depicts each sample as a solid line.
Blue bars represent M. m. domesticus haplotypes
Red lines represent M. m. musculus haplotypes
Green lines represent M. m. castaneus haplotypes
White lines represent regions of haplotypes of undetermined origin
Purple lines represent heterozygous regions with M. m. domesticus and M. m. musculus haplotypes
Cyan lines represent heterozygous regions with M. m. domesticus and M. m. castaneus haplotypes
Brown lines represent heterozygous regions with M. m. musculus and M. m. castaneus haplotypes
After zooming to a smaller region, each sample is depicted by the same horizontal line described above and a variable number of vertical bars. These bars denote the presence of diagnostic alleles in that sample at that marker (SNP or VINO). The height of each bar represents the diagnostic score for that allele while the color represents the subspecies (M. m. domesticus (blue), M. m. musculus (red) and M. m. castaneus (green)).

Heterozygosity Regions

Each sample is depicted as a line. White represents homozygous regions and black bars represent heterozygous regions. The height of the vertical bars represents the local level of heterozygosity.

For classical inbred strains, MPV provides access to four additional types of analysis.

Compatibility Intervals

These are overlapping intervals that show no evidence of historical recombination. The browser provides a histogram representing the local density of intervals. Under this track there are two additional tracks in which each interval is shown as a blue bar. The user needs to zoom in to regions with a high density of intervals to be able to see individual intervals. For each such compatible interval there is a phylogenetic tree, in which each node corresponds to a haplotype and each edge to SNPs with the same strain distribution pattern (see below).

IBD

The browser identifies regions of identity by descent among all selected classical strains for a given region. IBD regions are shown as pink bars.

Haplotype Coloring

This section depicts each sample as a colored line using eight pastel colors. The order of the samples can be changed at will by the user. Samples can be recolored based on the strain order in the display.

Phylogeny Trees

Trees are available for each compatible interval by clicking on that interval. The browser provides the distance tree, the distance matrix and the strains belonging to each haplotype. Selected strains are shown in bold. Colors represent subspecific origin. For each haplotype we provide a score and the number of SNP used to support it. Asterisks, denote haplotypes for which IBD among all samples is not strongly supported (high score and/or low number of SNPs). The user can take into consideration the trees in neighboring intervals to weight the evidence supporting a given haplotype.

Further details are explained in our paper.

Tool suite developed to monitor the progress and status of the Collaborative Cross and provide a bunch of useful analysis tools over the in-progress and complete CC lines.

Mouse Phylogeny Viewer

Visualization and analysis tool for the subspecific origin and haplotype diversity among a set of over 100 classical laboratory mice.

HiDimViewer

HiDimViewer is a visualization tool we are developing for high-dimensional datasets. It is designed to be used as an interactive data exploration tool to aid scientists in selecting and observing clusters in high-dimensional data.

NPUTE

NPUTE is an efficient data structure we have developed for finding pair-wise haplotype similarity. Its simplicity can lead to benefits in speed and exhaustive searches over multiple parameters.

Genetic Diversity of Mus musculus Laboratory Strains

The most commonly used resources harbor only a fraction of Mus musculus genetic diversity, which is not uniformly distributed resulting in many blind spots. Only resources that include wild-derived inbred strains from subspecies other than M. m. domesticus have no blind spots and uniform distribution of the variation. Unlike other resources that are primarily suited for gene discovery, the CC is the only resource that can support genome-wide network analysis, which is the foundation of systems genetics.

XBox Science

In XBox Science, we are exploring the potential of employing game interfaces, game-design principles, and game production approaches for constructing bioinformatics tools.

Tree-based Genome-wide Association Mapping

In this project, we developed TreeQA, a quantitative genome wide association (GWA) mapping algorithm. TreeQA utilizes local perfect phylogenies constructed in genomic regions exhibiting no evidence of historical recombination. By efficient algorithm design and implementation, TreeQA can efficiently conduct quantitative genom-wide association analysis and is more effective than the previous methods.

Collaborative Cross Simulator

The Collaborative Cross Simulator will provide both data and visual simulations for the collaborative cross experiment. The simulator will provide a powerful tool for the community by allowing them to generate synthetic lines and populations. Using these synthetic mice, researchers can compare actual mouse data against statistically neutral and random data.

Genotype Sequence Segmentation

In this project, we study the problem of segmenting the genotype sequences into the minimum number of segments attributable to the founder sequences. Our algorithms incorporate biological constraints to greatly reduce the computation, and guarantee that only minimum segmentation solutions with comparable numbers of segments on both haplotypes of the genotype sequence are computed. Our algorithms can also work on noisy data including genotyping errors, point mutations, gene conversions, and missing values.

Inferring Genome-wide Mosaic Structure

In this project, we study the Minimum Mosaic Problem: given a set of genome sequences from individuals within a population, compute a mosaic structure containing the minimum number of breakpoints. This mosaic structure provides a good estimation of the minimum number of recombination events (and their location) required to generate the existing haplotypes in the population. We solve this problem by finding the shortest path in a directed graph. Our algorithm’s efficiency permits genome-wide analysis.

FastANOVA: an Efficient Algorithm for Genome-Wide Association Study

In this project, we studied the problem of finding SNP-pairs that have significant associations with a given quantitative phenotype. We propose an efficient algorithm, FastANOVA, for performing ANOVA tests on SNP-pairs in a batch mode, which also supports large permutation test. FastANOVA only needs to perform the ANOVA test on a small number of candidate SNP-pairs without the risk of missing any significant ones.

Gene Expression Extract: Tool for extraction of subsets from gene expression data

This web tool allows one to extract subset of gene expression data by specifying subsets of genes,probes and strains. Clustering analysis can also be done on extracted data. An algorithm called, SAFE, is also integrated so that enrichment of biological pathways can be tested. The tool is available at http://compgen.unc.edu/GeneExprExtract

GBrowse for Mouse Genome

GBrowse is an open source genome viewer, which combines both databases and interactive Web pages for manipulating and displaying annotations on genomes. We utilize GBrowse to visualize genome-wide datasets as tracks, of which the order and the appearance are customizable by administrators or end-users. GBrowse supports simultaneous overviews, regional views, and detailed views.

In this project, we propose two dynamic programming algorithms to compute the minimum segmentations for genotype sequences. Our algorithms run in polynomial time and consider biological constraints of the genotype segmentation problem, i.e., the number of segments in both haplotypes are comparable. Moreover, our algorithms account for the potential noise sources in the data including point mutations, gene conversions, genotyping errors, and missing values. [paper]

Research Sponsor

NSF IIS 0448392: “CAREER: Mining Salient Localized Patterns in Complex Data”
NSF IIS 0812464: “III-Core: Discovering and Exploring Patterns in Subspaces”

What if solving nature’s puzzles was entertaining as well as fulfilling? Would you rather play a first-person shooter, or be the first person to discover a gene’s function? Is it possible to do both? This is the challenge that we address in our Xbox Science project. We are exploring the potential of employing game interfaces, game-design principles, and game production approaches for constructing bioinformatics tools. You might ask why?

1. Set-top Supercomputers. The most powerful computer in most homes today is a video-game console. Today’s machines boast multiple cores and 100+ MFlop performance with high-end graphics. Moreover, at $299, they represent one of the best MFlop per dollar ratios in history.
2. Most bioinformatics applications stink. Typical bioinformatics tools require their user to be literate in statistics, computer science, and biology. Imagine if, in order to drive a car, you had to simultaneously be a test-driver, mechanic, and combustion engineer. This is what is expected of today’s biologists. Lab software focuses on function and features rather than usability. In contrast, video game manuals are seldom read. Is it possible to build scientific tools that are usable by anyone? Can we make them fun?
3. Leverage an insatiable resource. Can we harness the minds and reflexes of the billion-plus gamers worldwide to find cures for disease with incentives of being a high scorer rather than securing drug-patent rights? Many of the tasks confronted by biologists amount to combinatorial puzzles, not unlike the game “Bejeweled”. A biologist may spend years searching for patterns within a gene expression array. What if hundreds of gamers joined in, and explored their datasets in parallel?

In these pages we share our experiences in writing video games with a real-world purpose. This includes discussions of the underlying biology, as well as downloadable games that can be played on the XBox-360.

Research Sponsors

NSF IIS 0534580: “Visualizing and Exploring High-dimensional Data”

Microsoft Research Grant

Biomedical Engineering

• Shawn Gomez

Biostatistics

• Fred Wright
• Fei Zou

Computer Science

• Sandra Batista
• Surojit Biswas
• Wei Cheng
• Chen-Ping Fu
• Elwin Gao
• Matt Holt
• Shunping Huang
• Vladimir Jojic
• Katy Kao
• Yi Liu
• Jingjing Ma
• Leonard McMillan
• Isa-Kemal Pakatci
• Jan Prins
• Sean Sanders
• Darshan Singh
• Jeremy Wang
• Wei Wang
• Weibo Wang
• Catie Welsh
• Alex Yang
• Zhaojun Zhang

Environmental Science and Engineering

• Daniel Gatti
• Ivan Rusyn

Genetics

• David Aylor
• John Calaway
• John Didion
• Fernando Pardo-Manuel de Villena
• Darla Miller
• Daniel Pomp
• Jason Spence
• David Threadgill
• Alex Vu
• Kirk C Wilhelmsen
• Yuying Xie

Lineberger Comprehensive Cancer Center

• Todd Taft

Alumni

• Andrew Hulbert
• Daniel Kumar
• Jinze Liu
• Kyle Moore
• Feng Pan
• Joel Parker
• Adam Roberts
• Abhishek Sarkar
• Lynda Yang
• Tynia Yang
• Qi Zhang
• Xiang Zhang

In this project, we are interested in inferring the possible mosaic structure of a given set of related haplotypes. This is accomplished by finding a set of recombination breakpoints that divide the haplotypes into compatible blocks according to the Four-Gamete Test (FGT)2. The FGT states that, under the infinite-sites assumption2, all pairs of polymorphisms should co-occur in only three out of their four possible configurations. Thus, when four configurations are observed in a pair of markers, it implies that either a recombination or a homoplastic event has occurred between them. We propose an efficient algorithm to solve the “Minimum Mosaic Problem”, which finds the mosaic with the minimum number of breakpoints. The algorithm is suitable for genome-wide study. [paper]

Please try this tool online.

Research Sponsor

NSF IIS 0448392: “CAREER: Mining Salient Localized Patterns in Complex Data”
NSF IIS 0534580: “Visualizing and Exploring High-dimensional Data”
NSF IIS 0812464: ” III-Core: Discovering and Exploring Patterns in Subspaces”