Biomedical subjects

Shane T Jensen

Publications and source records attributed to Shane T Jensen.

9 recordsLinked to original sources

PubMed2007

Clustering of genes into regulons using integrated modeling-COGRIM.

We present a Bayesian hierarchical model and Gibbs Sampling implementation that integrates gene expression, ChIP binding, and transcription factor motif data in a principled and robust fashion. COGRIM was applied to both unicellular and mammalian organisms under different scenarios of available data. In these applications, we demonstrate the ability to predict gene-transcription factor interactions with reduced numbers of false-positive findings and to make predictions beyond what is obtained when single types of data are considered.

CCAAT-Enhancer-Binding Protein-beta↗

PubMed2006

MicroRNA promoter element discovery in Arabidopsis.

In this study we present a method of identifying Arabidopsis miRNA promoter elements using known transcription factor binding motifs. We provide a comparative analysis of the representation of these elements in miRNA promoters, protein-coding gene promoters, and random genomic sequences. We report five transcription factor (TF) binding motifs that show evidence of overrepresentation in miRNA promoter regions relative to the promoter regions of protein-coding genes. This investigation is based on the analysis of 800-nucleotide regions upstream of 63 experimentally verified Transcription Start Sites (TSS) for miRNA primary transcripts in Arabidopsis. While the TATA-box binding motif was also previously reported by Xie and colleagues, the transcription factors AtMYC2, ARF, SORLREP3, and LFY are identified for the first time as overrepresented binding motifs in miRNA promoters.

Arabidopsis↗

PubMed2006

GAME: detecting cis-regulatory elements using a genetic algorithm.

MOTIVATION: Identification of a transcription factor binding sites is an important aspect of the analysis of genetic regulation. Many programs have been developed for the de novo discovery of a binding motif (collection of binding sites). Recently, a scoring function formulation was derived that allows for the comparison of discovered motifs from different programs [S.T. Jensen, X.S. Liu, Q. Zhou and J.S. Liu (2004) Stat. Sci., 19, 188-204.] A simple program, BioOptimizer, was proposed in [S.T. Jensen and J.S. Liu (2004) Bioinformatics, 20, 1557-1564.] that improved discovered motifs by optimizing a scoring function. However, BioOptimizer is a very simple algorithm that can only make local improvements upon an already discovered motif and so BioOptimizer can only be used in conjunction with other motif-finding software. RESULTS: We introduce software, GAME, which utilizes a genetic algorithm to find optimal motifs in DNA sequences. GAME evolves motifs with high fitness from a population of randomly generated starting motifs, which eliminate the reliance on additional motif-finding programs. In addition to using standard genetic operations, GAME also incorporates two additional operators that are specific to the motif discovery problem. We demonstrate the superior performance of GAME compared with MEME, BioProspector and BioOptimizer in simulation studies as well as several real data applications where we use an extended version of the GAME algorithm that allows the motif width to be unknown.

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↗

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

BioOptimizer: a Bayesian scoring function approach to motif discovery.

MOTIVATION: Transcription factors (TFs) bind directly to short segments on the genome, often within hundreds to thousands of base pairs upstream of gene transcription start sites, to regulate gene expression. The experimental determination of TFs binding sites is expensive and time-consuming. Many motif-finding programs have been developed, but no program is clearly superior in all situations. Practitioners often find it difficult to judge which of the motifs predicted by these algorithms are more likely to be biologically relevant. RESULTS: We derive a comprehensive scoring function based on a full Bayesian model that can handle unknown site abundance, unknown motif width and two-block motifs with variable-length gaps. An algorithm called BioOptimizer is proposed to optimize this scoring function so as to reduce noise in the motif signal found by any motif-finding program. The accuracy of BioOptimizer, which can be used in conjunction with several existing programs, is shown to be superior to using any of these motif-finding programs alone when evaluated by both simulation studies and application to sets of co-regulated genes in bacteria. In addition, this scoring function formulation enables us to compare objectively different predicted motifs and select the optimal ones, effectively combining the strengths of existing programs. AVAILABILITY: BioOptimizer is available for download at www.fas.harvard.edu/~junliu/BioOptimizer/

Algorithms↗

PubMed2003

The sigmaE regulon and the identification of additional sporulation genes in Bacillus subtilis.

We report the identification and characterization on a genome-wide basis of genes under the control of the developmental transcription factor sigma(E) in Bacillus subtilis. The sigma(E) factor governs gene expression in the larger of the two cellular compartments (the mother cell) created by polar division during the developmental process of sporulation. Using transcriptional profiling and bioinformatics we show that 253 genes (organized in 157 operons) appear to be controlled by sigma(E). Among these, 181 genes (organized in 121 operons) had not been previously described as members of this regulon. Promoters for many of the newly identified genes were located by transcription start site mapping. To assess the role of these genes in sporulation, we created null mutations in 98 of the newly identified genes and operons. Of the resulting mutants, 12 (in prkA, ybaN, yhbH, ykvV, ylbJ, ypjB, yqfC, yqfD, ytrH, ytrI, ytvI and yunB) exhibited defects in spore formation. In addition, subcellular localization studies were carried out using in-frame fusions of several of the genes to the coding sequence for GFP. A majority of the fusion proteins localized either to the membrane surrounding the developing spore or to specific layers of the spore coat, although some fusions showed a uniform distribution in the mother cell cytoplasm. Finally, we used comparative genomics to determine that 46 of the sigma(E)-controlled genes in B.subtilis were present in all of the Gram-positive endospore-forming bacteria whose genome has been sequenced, but absent from the genome of the closely related but not endospore-forming bacterium Listeria monocytogenes, thereby defining a core of conserved sporulation genes of probable common ancestral origin. Our findings set the stage for a comprehensive understanding of the contribution of a cell-specific transcription factor to development and morphogenesis.

Bacillus subtilis↗

PubMed2003

Finding the "genuine" schizotype: a model and method for resolving heterogeneity in performance on laboratory measures in experimental psychopathology research.

Heterogeneity in the performance of persons affected with schizophrenia or schizotypic psychopathology on various laboratory tasks has long been recognized, both for its consistency across tasks and studies and for the massive methodological and substantive challenges it poses for experimental psychopathology, genetic, and other investigations. Traditional multivariate techniques, such as factor analysis, discriminant function analysis, and cluster analysis, have all been deemed inadequate for resolving heterogeneity, because of one or another statistical limitation. Here, an objective statistical approach based on a formal statistical model that uses the ubiquitous and well-developed expectation-maximization (EM) algorithm (A. P. Dempster, N. M. Laird, & D. B. Rubin, 1977) is presented, which enables one effectively to partition a group of experimental subjects, in this case identified initially using the well-known Perceptual Aberration Scale (L. J. Chapman, J. P. Chapman, & M. L. Raulin, 1978), in a manner that reduces heterogeneity and allows for the separation of what are termed genuine and false-positive schizotypes. The validity of the parsing strategy was supported by reference to other laboratory indexes of relevance to schizophrenia and schizotypy that were not included in the initial EM-based analyses. The potential utility of this approach is discussed with reference to future schizophrenia and schizotypy research.

Adolescent↗

PubMed2003

The Spo0A regulon of Bacillus subtilis.

The master regulator for entry into sporulation in Bacillus subtilis is the DNA-binding protein Spo0A, which has been found to influence, directly or indirectly, the expression of over 500 genes during the early stages of development. To search on a genome-wide basis for genes under the direct control of Spo0A, we used chromatin immunoprecipitation in combination with gene microarray analysis to identify regions of the chromosome at which an activated form of Spo0A binds in vivo. This information in combination with transcriptional profiling using gene microarrays, gel electrophoretic mobility shift assays, using the DNA-binding domain of Spo0A, and bioinformatics enabled us to assign 103 genes to the Spo0A regulon in addition to 18 previously known members. Thus, in total, 121 genes, which are organized as 30 single-gene units and 24 operons, are likely to be under the direct control of Spo0A. Forty of these genes are under the positive control of Spo0A, and 81 are under its negative control. Among newly identified members of the regulon with transcription that was stimulated by Spo0A are genes for metabolic enzymes and genes for efflux pumps. Among members with transcription that was in-hibited by Spo0A are genes encoding components of the DNA replication machinery and genes that govern flagellum biosynthesis and chemotaxis. Also in-cluded in the regulon are many (25) genes with products that are direct or indirect regulators of gene transcription. Spo0A is a master regulator for sporulation, but many of its effects on the global pattern of gene transcription are likely to be mediated indirectly by regulatory genes under its control.

Bacillus subtilis↗