2554459
Refinement of optical map assemblies
MOTIVATION
Genomic mutations and variations provide insightful information about the functionality of sequence elements and their association with human diseases. Traditionally, variations are identified through analysis of short DNA sequences, usually shorter than 1000 bp per fragment. Optical maps provide both faster and more cost-efficient means for detecting such differences, because a single map can span over 1 million bp. Optical maps are assembled to cover the whole genome, and the accuracy of assembly is critical.


RESULTS
We present a computationally efficient model-based method for improving quality of such assemblies. Our method provides very high accuracy even with moderate coverage (<20 x). We utilize a hidden Markov model to represent the consensus map and use the expectation-Maximization algorithm to drive the refinement process. We also provide quality scores to assess the quality of the finished map.


AVAILABILITY
Code is available from www.cmb.usc.edu/people/valouev/
21795877
An Eulerian Path Approach to Global Multiple Alignment for DNA Sequences
With the rapid increase in the dataset of genome sequences, the multiple sequence alignment problem is increasingly important and frequently involves the alignment of a large number of sequences. Many heuristic algorithms have been proposed to improve the speed of computation and the quality of alignment. We introduce a novel approach that is fundamentally different from all currently available methods. Our motivation comes from the Eulerian method for fragment assembly in DNA sequencing that transforms all DNA fragments into a de Bruijn graph and then reduces sequence assembly to a Eulerian path problem. The paper focuses on global multiple alignment of DNA sequences, where entire sequences are aligned into one configuration. Our main result is an algorithm with almost linear computational speed with respect to the total size (number of letters) of sequences to be aligned. Five hundred simulated sequences (averaging 500 bases per sequence and as low as 70% pairwise identity) have been aligned within three minutes on a personal computer, and the quality of alignment is satisfactory. As a result, accurate and simultaneous alignment of thousands of long sequences within a reasonable amount of time becomes possible. Data from an Arabidopsis sequencing project is used to demonstrate the performance.
12263549
Tree-guided Bayesian inference of population structures
MOTIVATION
Inferring population structures using genetic data sampled from a group of individuals is a challenging task. Many methods either consider a fixed population number or ignore the correlation between populations. As a result, they can lose sensitivity and specificity in detecting subtle stratifications. In addition, when a large number of genetic markers are used, many existing algorithms perform rather inefficiently.


RESULT
We propose a new Bayesian method to infer population structures using multiple unlinked single nucleotide polymorphisms (SNPs). Our approach explicitly considers the population correlation through a tree hierarchy, and treat the population number as a random variable. Using both simulated and real datasets of worldwide samples, we demonstrate that an incorporated tree can consistently improve the power in detecting subtle population stratifications. A tree-based model often involves a large number of unknown parameters, and the corresponding estimation procedure can be highly inefficient. We further implement a partition method to analytically integrate out all nuisance parameters in the tree. As a result, our method can analyze large SNP datasets with significantly improved convergence rate.


AVAILABILITY
http://www.stat.psu.edu/~yuzhang/tips.tar.
32462907
Alignment of Optical Maps
We introduce a new scoring method for calculation of alignments of optical maps. Missing cuts, false cuts, and sizing errors present in optical maps are addressed by our alignment score through calculation of corresponding likelihoods. The size error model is derived through the application of Central Limit Theorem and validated by residual plots collected from real data. Missing cuts and false cuts are modeled as Bernoulli and Poisson events, respectively, as suggested by previous studies. Likelihoods are used to derive an alignment score through calculation of likelihood ratios for a certain hypothesis test. This allows us to achieve maximal descriminative power for the alignment score. Our scoring method is naturally embedded within a well known DP framework for finding optimal alignments.
29942985
Poisson approximation for significance in genome-wide ChIP-chip tiling arrays
MOTIVATION
A genome-wide ChIP-chip tiling array study requires millions of simultaneous comparisons of hybridization for significance. Controlling the false positive rate in genome-wide tiling array studies is very important, because the number of computationally identified regions can easily go beyond the capability of experimental verification. No accurate and efficient method exists for evaluating statistical significance in tiling arrays. The Bonferroni method is overly conservative and the permutation test is time consuming for genome-wide studies.


RESULT
Motivated by the Poisson clumping heuristic, we propose an accurate and efficient method for evaluating statistical significance in genome-wide ChIP-chip tiling arrays. The method works accurately for any large number of multiple comparisons, and the computational cost for evaluating P-values does not increase with the total number of tests. Based on a moving window approach, we demonstrate how to combine results using various window sizes to increase the detection power while maintaining a specified type I error rate. We further introduce a new false discovery rate control that is more appropriate in measuring the false proportion of binding intervals in tiling array analysis. Our method is general and can be applied to many large-scale genomic and genetic studies.


AVAILABILITY
http://www.stat.psu.edu/~yuzhang/pass.tar
172003
Reconstructing the Evolutionary History of Complex Human Gene Clusters
Clusters of genes that evolved from single progenitors via repeated segmental duplications present significant challenges to the generation of a truly complete human genome sequence. Such clusters can confound both accurate sequence assembly and downstream computational analysis, yet they represent a hotbed of functional innovation, making them of extreme interest. We have developed an algorithm for reconstructing the evolutionary history of gene clusters using only human genomic sequence data. This method allows the tempo of large-scale evolutionary events in human gene clusters to be estimated, which in turn will facilitate primate comparative sequencing studies that will aim to reconstruct their evolutionary history more fully.
21919804
Logical Relations for Dynamic Name Creation
Pitts and Stark’s nu-calculus is a typed lambda-calculus which forms a basis for the study of interaction between higher-order functions and dynamically created names. A similar approach has received renewed attention recently through Sumii and Pierce’s cryptographic lambda-calculus, which deals with security protocols. Logical relations are a powerful tool to prove properties of such a calculus, notably observational equivalence. While Pitts and Stark construct a logical relation for the nu-calculus, it rests heavily on operational aspects of the calculus and is hard to be extended. We propose an alternative Kripke logical relation for the nu-calculus, which is derived naturally from the categorical model of the nu-calculus and the general notion of Kripke logical relation. This is also related to the Kripke logical relation for the name creation monad by Goubault-Larrecq et al. (CSL’2002), which the authors claimed had similarities with Pitts and Stark’s logical relation. We show that their Kripke logical relation for names is strictly weaker than Pitts and Stark’s. We also show that our Kripke logical relation, which extends the definition of Goubault-Larrecq et al., is equivalent to Pitts and Stark’s up to first-order types; our definition rests on purely semantic constituents, and dispenses with the detours through operational semantics that Pitts and Stark use.
18794722
Word Sense Language Model for Information Retrieval
This paper proposes a word sense language model based method for information retrieval. This method, differing from most of traditional ones, combines word senses defined in a thesaurus with a classic statistical model. The word sense language model regards the word sense as a form of linguistic knowledge, which is helpful in handling mismatch caused by synonym and data sparseness due to data limit. Experimental results based on TREC-Mandarin corpus show that this method gains 12.5% improvement on MAP over traditional tf-idf retrieval method but 5.82% decrease on MAP compared to a classic language model. A combination result of this method and the language model yields 8.92% and 7.93% increases over either respectively. We present analysis and discussions on the not-so-exciting results and conclude that a higher performance of word sense language model will owe to high accurate of word sense labeling. We believe that linguistic knowledge such as word sense of a thesaurus will help IR improve ultimately in many ways.
35510017
A Link-Based Rank of Postings in Newsgroup
Discussion systems such as Usenet, BBS, Forum are important resources for information sharing, view exchanging, problem solving and product feedback, etc. on Internet. The postings in newsgroups on Usenet represents the judgments and choices of participators. The structure of postings could provide helpful information for the users. In this paper, we present a method called PostRank to rank the postings based on the structure of newsgroup. Its results correspond to the eigenvectors of the transition probability matrix and the stationary vectors of the Markov chains. It could provide useful global information for the newsgroup and it can be used to help the users access information in it more effectively and efficiently. This method can be also applied on other discussion systems. Some experimental results and discussions on real data sets collected by us are also provided.
58833855
EDFCES: a new example-driven 3D face construction and editing system
This paper presents an automatic runtime system for generating varied, realistic face models by synthesizing a global face shape and local facial features according to intuitive, high-level control parameters. Our method takes as examples 3D face scans in order to exploit the parameter-to-geometry correlations present in real faces. In order to establish the correspondences among the scanned models, we use a three-step model fitting approach to conform a generic head mesh onto each scanned model. We transform the obtained data sets of global face shapes and local feature shapes into vector space representations by applying a principal component analysis (PCA). We compute a set of face anthropometric measurements to parameterize the exemplary shapes in the measurement spaces. Using PCA coefficients as a compact shape representation, we approach the shape synthesis problem by forming scattered data interpolation functions designed to generate the desired face shape by taking anthropometric parameters as input. At runtime, the interpolation functions are evaluated for the input parameter values to produce new face geometries at an interactive rate. The correspondence among all exemplary face textures is obtained by parameterizing the 3D generic mesh over a 2D image domain. The new feature texture with the desired attributes is synthesized by interpolating the example textures. The resulting system is intuitive to control and fine-grained. We demonstrate our method by applying different parameters to generate a wide range of face models.
40211124
Real-time physically-based facial expression animation using mass-spring system
We propose a physically-based approach based on anatomical knowledge for real-time facial expression animation. The facial model incorporates a physically-based approximation to facial skin and a set of anatomically-motivated facial muscles. The skin is modeled by a mass-spring system with nonlinear springs which have a biphasic stress-strain relationship to simulate the elastic dynamics of real facial skin. Facial muscles are modeled as forces deforming the spring mesh. Based on the action units (AUs) of the facial action coding system (FACS), various expressions can be generated by a combination of contractions of a set of facial muscles. Lagrangian mechanics governs the dynamics, dictating the deformation of the facial surface in response to muscle forces. Simulation results show real-time facial deformation as well as realistic expression animation.