Biomedical subjects

Charles E Lawrence

Publications and source records attributed to Charles E Lawrence.

6 recordsLinked to original sources

PubMed2003

Gibbs Recursive Sampler: finding transcription factor binding sites.

The Gibbs Motif Sampler is a software package for locating common elements in collections of biopolymer sequences. In this paper we describe a new variation of the Gibbs Motif Sampler, the Gibbs Recursive Sampler, which has been developed specifically for locating multiple transcription factor binding sites for multiple transcription factors simultaneously in unaligned DNA sequences that may be heterogeneous in DNA composition. Here we describe the basic operation of the web-based version of this sampler. The sampler may be acces-sed at http://bayesweb.wadsworth.org/gibbs/gibbs.html and at http://www.bioinfo.rpi.edu/applications/bayesian/gibbs/gibbs.html. An online user guide is available at http://bayesweb.wadsworth.org/gibbs/bernoulli.html and at http://www.bioinfo.rpi.edu/applications/bayesian/gibbs/manual/bernoulli.html. Solaris, Solaris.x86 and Linux versions of the sampler are available as stand-alone programs for academic and not-for-profit users. Commercial licenses are also available. The Gibbs Recursive Sampler is distributed in accordance with the ISCB level 0 guidelines and a requirement for citation of use in scientific publications.

Algorithms↗

PubMed2003

Identification of co-regulated genes through Bayesian clustering of predicted regulatory binding sites.

The identification of co-regulated genes and their transcription-factor binding sites (TFBS) are key steps toward understanding transcription regulation. In addition to effective laboratory assays, various computational approaches for the detection of TFBS in promoter regions of coexpressed genes have been developed. The availability of complete genome sequences combined with the likelihood that transcription factors and their cognate sites are often conserved during evolution has led to the development of phylogenetic footprinting. The modus operandi of this technique is to search for conserved motifs upstream of orthologous genes from closely related species. The method can identify hundreds of TFBS without prior knowledge of co-regulation or coexpression. Because many of these predicted sites are likely to be bound by the same transcription factor, motifs with similar patterns can be put into clusters so as to infer the sets of co-regulated genes, that is, the regulons. This strategy utilizes only genome sequence information and is complementary to and confirmative of gene expression data generated by microarray experiments. However, the limited data available to characterize individual binding patterns, the variation in motif alignment, motif width, and base conservation, and the lack of knowledge of the number and sizes of regulons make this inference problem difficult. We have developed a Gibbs sampling-based Bayesian motif clustering (BMC) algorithm to address these challenges. Tests on simulated data sets show that BMC produces many fewer errors than hierarchical and K-means clustering methods. The application of BMC to hundreds of predicted gamma-proteobacterial motifs correctly identified many experimentally reported regulons, inferred the existence of previously unreported members of these regulons, and suggested novel regulons.

Algorithms↗

PubMed2002

Identification of a novel class in the alpha/beta hydrolase fold superfamily: the N-myc differentiation-related proteins.

The alpha/beta hydrolases constitute a large protein superfamily that mainly consists of enzymes that catalyze a diverse range of reactions. These proteins exhibit the alpha/beta hydrolase fold, the essential features of which have recently been delineated: the presence of at least five parallel beta-strands, a catalytic triad in a specific order (nucleophile-acid-histidine), and a nucleophilic elbow. Because of the difficulties experimentally in identifying protein structures, we have used a Bayesian computational algorithm (PROBE) to identify the members of this superfamily based on distant sequence relationships. We found that the presence of five sequence motifs, which contain residues important for substrate binding and stabilization of the fold, are required for membership in this superfamily. The superfamily consists of at least 909 members, including the N-myc downstream regulated proteins, which are believed to be involved in cell differentiation. Unlike most of the other superfamily members, the N-myc downstream regulated proteins have never been proposed to possess the alpha/beta hydrolase fold and do not appear to be hydrolases.

Amino Acid Motifs↗

PubMed2002

BALSA: Bayesian algorithm for local sequence alignment.

The Smith-Waterman algorithm yields a single alignment, which, albeit optimal, can be strongly affected by the choice of the scoring matrix and the gap penalties. Additionally, the scores obtained are dependent upon the lengths of the aligned sequences, requiring a post-analysis conversion. To overcome some of these shortcomings, we developed a Bayesian algorithm for local sequence alignment (BALSA), that takes into account the uncertainty associated with all unknown variables by incorporating in its forward sums a series of scoring matrices, gap parameters and all possible alignments. The algorithm can return both the joint and the marginal optimal alignments, samples of alignments drawn from the posterior distribution and the posterior probabilities of gap penalties and scoring matrices. Furthermore, it automatically adjusts for variations in sequence lengths. BALSA was compared with SSEARCH, to date the best performing dynamic programming algorithm in the detection of structural neighbors. Using the SCOP databases PDB40D-B and PDB90D-B, BALSA detected 19.8 and 41.3% of remote homologs whereas SSEARCH detected 18.4 and 38% at an error rate of 1% errors per query over the databases, respectively.

Algorithms↗

PubMed2002

A Bayesian method for classification of images from electron micrographs.

Particle classification is an important component of multivariate statistical analysis methods that has been used extensively to extract information from electron micrographs of single particles. Here we describe a new Bayesian Gibbs sampling algorithm for the classification of such images. This algorithm, which is applied after dimension reduction by correspondence analysis or by principal components analysis, dynamically learns the parameters of the multivariate Gaussian distributions that characterize each class. These distributions describe tilted ellipsoidal clusters that adaptively adjust shape to capture differences in the variances of factors and the correlations of factors within classes. A novel Bayesian procedure to objectively select factors for inclusion in the classification models is a component of this procedure. A comparison of this algorithm with hierarchical ascendant classification of simulated data sets shows improved classification over a broad range of signal-to-noise ratios.

Algorithms↗

PubMed2002

Factors influencing the identification of transcription factor binding sites by cross-species comparison.

As the number of sequenced genomes has grown, the questions of which species are most useful and how many genomes are sufficient for comparison have become increasingly important for comparative genomics studies. We have systematically addressed these questions with respect to phylogenetic footprinting of transcription factor (TF) binding sites in the gamma-proteobacteria, and have evaluated the statistical significance of our motif predictions. We used a study set of 166 Escherichia coli genes that have experimentally identified TF binding sites upstream of the gene, with orthologous data from nine additional gamma-proteobacteria for phylogenetic footprinting. Just three species were sufficient for approximately 74.0% of the motif predictions to correspond to the experimentally reported E. coli sites, and important characteristics to consider when choosing species were phylogenetic distance, genome size, and natural habitat. We also performed simulations using randomized data to determine the critical maximum a posteriori probability (MAP) values for statistical significance of our motif predictions (P = 0.05). Approximately 60% of motif predictions containing sites from just three species had average MAP values above these critical MAP values. The inclusion of a species very closely related to E. coli increased the number of statistically significant motif predictions, despite substantially increasing the critical MAP value.

Binding Sites↗