Biomedical subjects

Jun S Liu

Publications and source records attributed to Jun S Liu.

At least 19 recordsLinked to original sources

PubMed2007

Monte Carlo sampling of near-native structures of proteins with applications.

Since a protein's dynamic fluctuation inside cells affects the protein's biological properties, we present a novel method to study the ensemble of near-native structures (NNS) of proteins, namely, the conformations that are very similar to the experimentally determined native structure. We show that this method enables us to (i) quantify the difficulty of predicting a protein's structure, (ii) choose appropriate simplified representations of protein structures, and (iii) assess the effectiveness of knowledge-based potential functions. We found that well-designed simple representations of protein structures are likely as accurate as those more complex ones for certain potential functions. We also found that the widely used contact potential functions stabilize NNS poorly, whereas potential functions incorporating local structure information significantly increase the stability of NNS.

Computer Simulation↗

PubMed2006

On side-chain conformational entropy of proteins.

The role of side-chain entropy (SCE) in protein folding has long been speculated about but is still not fully understood. Utilizing a newly developed Monte Carlo method, we conducted a systematic investigation of how the SCE relates to the size of the protein and how it differs among a protein's X-ray, NMR, and decoy structures. We estimated the SCE for a set of 675 nonhomologous proteins, and observed that there is a significant SCE for both exposed and buried residues for all these proteins-the contribution of buried residues approaches approximately 40% of the overall SCE. Furthermore, the SCE can be quite different for structures with similar compactness or even similar conformations. As a striking example, we found that proteins' X-ray structures appear to pack more "cleverly" than their NMR or decoy counterparts in the sense of retaining higher SCE while achieving comparable compactness, which suggests that the SCE plays an important role in favouring native protein structures. By including a SCE term in a simple free energy function, we can significantly improve the discrimination of native protein structures from decoys.

Algorithms↗

PubMed2006

Selective promoter recognition by chlamydial sigma28 holoenzyme.

The sigma transcription factor confers the promoter recognition specificity of RNA polymerase (RNAP) in eubacteria. Chlamydia trachomatis has three known sigma factors, sigma(66), sigma(54), and sigma(28). We developed two methods to facilitate the characterization of promoter sequences recognized by C. trachomatis sigma(28) (sigma(28)(Ct)). One involved the arabinose-induced expression of plasmid-encoded sigma(28)(Ct) in a strain of Escherichia coli defective in the sigma(28) structural gene, fliA. The second was an analysis of transcription in vitro with a hybrid holoenzyme reconstituted with E. coli RNAP core and recombinant sigma(28)(Ct). These approaches were used to investigate the interactions of sigma(28)(Ct) with the sigma(28)(Ct)-dependent hctB promoter and selected E. coli sigma(28) (sigma(28)(Ec))-dependent promoters, in parallel, compared with the promoter recognition properties of sigma(28)(EC). Our results indicate that RNAP containing sigma(28)(Ct) has at least three characteristics: (i) it is capable of recognizing some but not all sigma(28)(EC)-dependent promoters; (ii) it can distinguish different promoter structures, preferentially activating promoters with upstream AT-rich sequences; and (iii) it possesses a greater flexibility than sigma(28)(EC) in recognizing variants with different spacing lengths separating the -35 and -10 elements of the core promoter.

Artificial Gene Fusion↗

PubMed2006

Statistical assessment of the global regulatory role of histone acetylation in Saccharomyces cerevisiae.

BACKGROUND: Histone acetylation plays important but incompletely understood roles in gene regulation. A comprehensive understanding of the regulatory role of histone acetylation is difficult because many different histone acetylation patterns exist and their effects are confounded by other factors, such as the transcription factor binding sequence motif information and nucleosome occupancy. RESULTS: We analyzed recent genomewide histone acetylation data using a few complementary statistical models and tested the validity of a cumulative model in approximating the global regulatory effect of histone acetylation. Confounding effects due to transcription factor binding sequence information were estimated by using two independent motif-based algorithms followed by a variable selection method. We found that the sequence information has a significant role in regulating transcription, and we also found a clear additional histone acetylation effect. Our model fits well with observed genome-wide data. Strikingly, including more complicated combinatorial effects does not improve the model's performance. Through a statistical analysis of conditional independence, we found that H4 acetylation may not have significant direct impact on global gene expression. CONCLUSION: Decoding the combinatorial complexity of histone modification requires not only new data but also new methods to analyze the data. Our statistical analysis confirms that histone acetylation has a significant effect on gene transcription rates in addition to that attributable to upstream sequence motifs. Our analysis also suggests that a cumulative effect model for global histone acetylation is justified, although a more complex histone code may be important at specific gene loci. We also found that the regulatory roles among different histone acetylation sites have important differences.

Acetylation↗

PubMed2006

A coalescence-guided hierarchical Bayesian method for haplotype inference.

Haplotype inference from phase-ambiguous multilocus genotype data is an important task for both disease-gene mapping and studies of human evolution. We report a novel haplotype-inference method based on a coalescence-guided hierarchical Bayes model. In this model, a hierarchical structure is imposed on the prior haplotype frequency distributions to capture the similarities among modern-day haplotypes attributable to their common ancestry. As a consequence, the model both allows distinct haplotypes to have different a priori probabilities according to the inferred hierarchical ancestral structure and results in a proper joint posterior distribution for all the parameters of interest. A Markov chain-Monte Carlo scheme is designed to draw from this posterior distribution. By using coalescence-based simulation and empirically generated data sets (Whitehead Institute's inflammatory bowel disease data sets and HapMap data sets), we demonstrate the merits of the new method in comparison with HAPLOTYPER and PHASE, with or without the presence of recombination hotspots and missing genotypes.

Algorithms↗

PubMed2006

Bayesian models for pooling microarray studies with multiple sources of replications.

BACKGROUND: Biologists often conduct multiple but different cDNA microarray studies that all target the same biological system or pathway. Within each study, replicate slides within repeated identical experiments are often produced. Pooling information across studies can help more accurately identify true target genes. Here, we introduce a method to integrate multiple independent studies efficiently. RESULTS: We introduce a Bayesian hierarchical model to pool cDNA microarray data across multiple independent studies to identify highly expressed genes. Each study has multiple sources of variation, i.e. replicate slides within repeated identical experiments. Our model produces the gene-specific posterior probability of differential expression, which provides a direct method for ranking genes, and provides Bayesian estimates of false discovery rates (FDR). In simulations combining two and five independent studies, with fixed FDR levels, we observed large increases in the number of discovered genes in pooled versus individual analyses. When the number of output genes is fixed (e.g., top 100), the pooled model found appreciably more truly differentially expressed genes than the individual studies. We were also able to identify more differentially expressed genes from pooling two independent studies in Bacillus subtilis than from each individual data set. Finally, we observed that in our simulation studies our Bayesian FDR estimates tracked the true FDRs very well. CONCLUSION: Our method provides a cohesive framework for combining multiple but not identical microarray studies with several sources of replication, with data produced from the same platform. We assume that each study contains only two conditions: an experimental and a control sample. We demonstrated our model's suitability for a small number of studies that have been either pre-scaled or have no outliers.

Bacillus subtilis↗

PubMed2006

Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data.

BACKGROUND: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. RESULTS: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5%- approximately 20% improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. CONCLUSION: The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features.

Algorithms↗

PubMed2006

A data-driven clustering method for time course gene expression data.

Gene expression over time is, biologically, a continuous process and can thus be represented by a continuous function, i.e. a curve. Individual genes often share similar expression patterns (functional forms). However, the shape of each function, the number of such functions, and the genes that share similar functional forms are typically unknown. Here we introduce an approach that allows direct discovery of related patterns of gene expression and their underlying functions (curves) from data without a priori specification of either cluster number or functional form. Smoothing spline clustering (SSC) models natural properties of gene expression over time, taking into account natural differences in gene expression within a cluster of similarly expressed genes, the effects of experimental measurement error, and missing data. Furthermore, SSC provides a visual summary of each cluster's gene expression function and goodness-of-fit by way of a 'mean curve' construct and its associated confidence bands. We apply this method to gene expression data over the life-cycle of Drosophila melanogaster and Caenorhabditis elegans to discover 17 and 16 unique patterns of gene expression in each species, respectively. New and previously described expression patterns in both species are discovered, the majority of which are biologically meaningful and exhibit statistically significant gene function enrichment. Software and source code implementing the algorithm, SSClust, is freely available (http://genemerge.bioteam.net/SSClust.html).

Algorithms↗

PubMed2005

RSIR: regularized sliced inverse regression for motif discovery.

MOTIVATION: Identification of transcription factor binding motifs (TFBMs) is a crucial first step towards the understanding of regulatory circuitries controlling the expression of genes. In this paper, we propose a novel procedure called regularized sliced inverse regression (RSIR) for identifying TFBMs. RSIR follows a recent trend to combine information contained in both gene expression measurements and genes' promoter sequences. Compared with existing methods, RSIR is efficient in computation, very stable for data with high dimensionality and high collinearity, and improves motif detection sensitivities and specificities by avoiding inappropriate model specification. RESULTS: We compare RSIR with SIR and stepwise regression based on simulated data and find that RSIR has a lower false positive rate. We also demonstrate an excellent performance of RSIR by applying it to the yeast amino acid starvation data and cell cycle data. AVAILABILITY: Matlab programs are available upon request from the authors.

Algorithms↗

PubMed2005

Combining phylogenetic motif discovery and motif clustering to predict co-regulated genes.

MOTIVATION: We present a sequence-based framework and algorithm PHYLOCLUS for predicting co-regulated genes. In our approach, de novo discovery methods are used to find motifs conserved by evolution and then a Bayesian hierarchical clustering model is used to cluster these motifs, thereby grouping together genes that are putatively co-regulated. Our clustering procedure allows both the number of clusters and the motif width within each cluster to be unknown. RESULTS: We use our framework to predict co-regulated genes in the bacterium Bacillus subtilis using six other closely related bacterial species. Our predicted motifs and gene clusters are validated using several external sources and significant clusters are examined in detail. An extension to the discovery and clustering of two-block motifs can be used for inference about synergistic binding relationships between transcription factors. AVAILABILITY: Software and Supplementary Materials can be downloaded at http://stat.wharton.upenn.edu/~stjensen/research/phyloclus.html or http://www.fas.harvard.edu/~junliu/phyloclus.html CONTACT: stjensen@wharton.upenn.edu.

Algorithms↗

PubMed2005

De novo cis-regulatory module elicitation for eukaryotic genomes.

Transcription regulation is controlled by coordinated binding of one or more transcription factors in the promoter regions of genes. In many species, especially higher eukaryotes, transcription factor binding sites tend to occur as homotypic or heterotypic clusters, also known as cis-regulatory modules. The number of sites and distances between the sites, however, vary greatly in a module. We propose a statistical model to describe the underlying cluster structure as well as individual motif conservation and develop a Monte Carlo motif screening strategy for predicting novel regulatory modules in upstream sequences of coregulated genes. We demonstrate the power of the method with examples ranging from bacterial to insect and human genomes.

Base Sequence↗

PubMed2005

A boosting approach for motif modeling using ChIP-chip data.

MOTIVATION: Building an accurate binding model for a transcription factor (TF) is essential to differentiate its true binding targets from those spurious ones. This is an important step toward understanding gene regulation. RESULTS: This paper describes a boosting approach to modeling TF-DNA binding. Different from the widely used weight matrix model, which predicts TF-DNA binding based on a linear combination of position-specific contributions, our approach builds a TF binding classifier by combining a set of weight matrix based classifiers, thus yielding a non-linear binding decision rule. The proposed approach was applied to the ChIP-chip data of Saccharomyces cerevisiae. When compared with the weight matrix method, our new approach showed significant improvements on the specificity in a majority of cases.

Algorithms↗

PubMed2005

BEST: binding-site estimation suite of tools.

SUMMARY: The purpose of our Binding-site Estimation Suite of Tools (BEST) is two-fold: to provide a platform for using and comparing different motif-finding programs for transcription factor binding site prediction, and to improve the accuracy of these predictions by further optimization. Our software package BEST includes four commonly used motif-finding programs: AlignACE, BioProspector, CONSENSUS and MEME, as well as the optimization program BioOptimizer. BEST allows the user to run programs either separately or sequentially and manages all programs by automating the common inputs and the optimization procedure. The BEST system was implemented in Qt, a C++ application development framework, and was compiled and executed on Linux operating systems. AVAILABILITY: BEST is available for download at http://www.cs.uga.edu/~che/BEST and http://www.fas.harvard.edu/~junliu/BEST CONTACT: dsche@uga.edu, jliu@stat.harvard.edu.

Algorithms↗

PubMed2005

Defining a centromere-like element in Bacillus subtilis by Identifying the binding sites for the chromosome-anchoring protein RacA.

Chromosome segregation during sporulation in Bacillus subtilis involves the anchoring of sister chromosomes to opposite ends of the cell. Anchoring is mediated by RacA, which acts as a bridge between a centromere-like element in the vicinity of the origin of replication and the cell pole. To define this element we mapped RacA binding sites by performing chromatin immunoprecipitation in conjunction with gene microarray analysis. RacA preferentially bound to 25 regions spread over 612 kb across the origin portion of the chromosome. Computational and biochemical analysis identified a GC-rich, inverted 14 bp repeat as the recognition sequence. Experiments with single molecules of DNA demonstrated that RacA can condense nonspecific DNA dramatically against appreciable forces to form a highly stable protein-DNA complex. We propose that interactions between DNA bound RacA molecules cause the centromere-like element to fold up into a higher order complex that fastens the chromosome to the cell pole.

Bacillus subtilis↗

PubMed2005

Genetic variation in XPD, sun exposure, and risk of skin cancer.

The XPD gene is involved in the nucleotide excision repair pathway removing DNA photoproducts induced by UV radiation. Genetic variation in XPD may exert a subtle effect on DNA repair capacity. We assessed the associations between two common nonsynonymous polymorphisms (Asp312Asn and Lys751Gln) with skin cancer risk in a nested case-control study within the Nurses' Health Study (219 melanoma, 286 squamous cell carcinoma, 300 basal cell carcinoma, and 874 controls) along with exploratory analysis on the haplotype structure of the XPD gene. There were inverse associations between the Lys751Gln and Asp312Asn polymorphisms and the risks of melanoma and squamous cell carcinoma. No association was observed between these two polymorphisms and basal cell carcinoma risk. We also observed that the association of the 751Gln allele with melanoma risk was modified by lifetime severe sunburns, cumulative sun exposure with a bathing suit, and constitutional susceptibility score (P for interaction = 0.03, 0.04, and 0.02 respectively). Similar interactions were also observed for the Asp312Asn. Our data suggest these two XPD nonsynonymous polymorphisms may be associated with skin cancer risk, especially for melanoma.

Adult↗

PubMed2004

Gapped alignment of protein sequence motifs through Monte Carlo optimization of a hidden Markov model.

BACKGROUND: Certain protein families are highly conserved across distantly related organisms and belong to large and functionally diverse superfamilies. The patterns of conservation present in these protein sequences presumably are due to selective constraints maintaining important but unknown structural mechanisms with some constraints specific to each family and others shared by a larger subset or by the entire superfamily. To exploit these patterns as a source of functional information, we recently devised a statistically based approach called contrast hierarchical alignment and interaction network (CHAIN) analysis, which infers the strengths of various categories of selective constraints from co-conserved patterns in a multiple alignment. The power of this approach strongly depends on the quality of the multiple alignments, which thus motivated development of theoretical concepts and strategies to improve alignment of conserved motifs within large sets of distantly related sequences. RESULTS: Here we describe a hidden Markov model (HMM), an algebraic system, and Markov chain Monte Carlo (MCMC) sampling strategies for alignment of multiple sequence motifs. The MCMC sampling strategies are useful both for alignment optimization and for adjusting position specific background amino acid frequencies for alignment uncertainties. Associated statistical formulations provide an objective measure of alignment quality as well as automatic gap penalty optimization. Improved alignments obtained in this way are compared with PSI-BLAST based alignments within the context of CHAIN analysis of three protein families: Gialpha subunits, prolyl oligopeptidases, and transitional endoplasmic reticulum (p97) AAA+ ATPases. CONCLUSION: While not entirely replacing PSI-BLAST based alignments, which likewise may be optimized for CHAIN analysis using this approach, these motif-based methods often more accurately align very distantly related sequences and thus can provide a better measure of selective constraints. In some instances, these new approaches also provide a better understanding of family-specific constraints, as we illustrate for p97 ATPases. Programs implementing these procedures and supplementary information are available from the authors.

Amino Acid Motifs↗

PubMed2004

The program of gene transcription for a single differentiating cell type during sporulation in Bacillus subtilis.

Asymmetric division during sporulation by Bacillus subtilis generates a mother cell that undergoes a 5-h program of differentiation. The program is governed by a hierarchical cascade consisting of the transcription factors: sigma(E), sigma(K), GerE, GerR, and SpoIIID. The program consists of the activation and repression of 383 genes. The sigma(E) factor turns on 262 genes, including those for GerR and SpoIIID. These DNA-binding proteins downregulate almost half of the genes in the sigma(E) regulon. In addition, SpoIIID turns on ten genes, including genes involved in the appearance of sigma(K). Next, sigma(K) activates 75 additional genes, including that for GerE. This DNA-binding protein, in turn, represses half of the genes that had been activated by sigma(K) while switching on a final set of 36 genes. Evidence is presented that repression and activation contribute to proper morphogenesis. The program of gene expression is driven forward by its hierarchical organization and by the repressive effects of the DNA-binding proteins. The logic of the program is that of a linked series of feed-forward loops, which generate successive pulses of gene transcription. Similar regulatory circuits could be a common feature of other systems of cellular differentiation.

Amino Acid Motifs↗

PubMed2004

HapBlock: haplotype block partitioning and tag SNP selection software using a set of dynamic programming algorithms.

UNLABELLED: Recent studies have revealed that linkage disequilibrium (LD) patterns vary across the human genome with some regions of high LD interspersed with regions of low LD. Such LD patterns make it possible to select a set of single nucleotide polymorphism (SNPs; tag SNPs) for genome-wide association studies. We have developed a suite of computer programs to analyze the block-like LD patterns and to select the corresponding tag SNPs. Compared to other programs for haplotype block partitioning and tag SNP selection, our program has several notable features. First, the dynamic programming algorithms implemented are guaranteed to find the block partition with minimum number of tag SNPs for the given criteria of blocks and tag SNPs. Second, both haplotype data and genotype data from unrelated individuals and/or from general pedigrees can be analyzed. Third, several existing measures/criteria for haplotype block partitioning and tag SNP selection have been implemented in the program. Finally, the programs provide flexibility to include specific SNPs (e.g. non-synonymous SNPs) as tag SNPs. AVAILABILITY: The HapBlock program and its supplemental documents can be downloaded from the website http://www.cmb.usc.edu/~msms/HapBlock.

Algorithms↗