Charles E. Lawrence

Comparative analyses of multi-species sequences from targeted genomic regions

The systematic comparison of genomic sequences from different organisms represents a central focus of contemporary genome analysis. Comparative analyses of vertebrate sequences can identify coding and conserved non-coding regions, including regulatory elements, and provide insight into the forces that have rendered modern-day genomes. As a complement to whole-genome sequencing efforts, we are sequencing and comparing targeted genomic regions in multiple, evolutionarily diverse vertebrates. Here we report the generation and analysis of over 12 megabases (Mb) of sequence from 12 species, all derived from the genomic region orthologous to a segment of about 1.8 Mb on human chromosome 7 containing ten genes, including the gene mutated in cystic fibrosis. These sequences show conservation reflecting both functional constraints and the neutral mutational events that shaped this genomic region. In particular, we identify substantial numbers of conserved non-coding segments beyond those previously identified experimentally, most of which are not detectable by pair-wise sequence comparisons alone. Analysis of transposable element insertions highlights the variation in genome dynamics among these species and confirms the placement of rodents as a sister group to the primates.

Human-mouse genome comparisons to locate regulatory sites

Elucidating the human transcriptional regulatory network is a challenge of the post-genomic era. Technical progress so far is impressive, including detailed understanding of regulatory mechanisms for at least a few genes in multicellular organisms, rapid and precise localization of regulatory regions within extensive regions of DNA by means of cross-species comparison, and de novo determination of transcription-factor binding specificities from large-scale yeast expression data. Here we address two problems involved in extending these results to the human genome: first, it has been unclear how many model organism genomes will be needed to delineate most regulatory regions; and second, the discovery of transcription-factor binding sites (response elements) from expression data has not yet been generalized from single-celled organisms to multicellular organisms. We found that 98% (74/75) of experimentally defined sequence-specific binding sites of skeletal-muscle-specific transcription factors are confined to the 19% of human sequences that are most conserved in the orthologous rodent sequences. Also we found that in using this restriction, the binding specificities of all three major muscle-specific transcription factors (MYF, SRF and MEF2) can be computationally identified.

SFOLD WEB SERVER FOR STATISTICAL FOLDING AND RATIONAL DESIGN OF NUCLEIC ACIDS

The Sfold web server provides user-friendly access to Sfold, a recently developed nucleic acid folding software package, via the World Wide Web (WWW). The software is based on a new statistical sampling paradigm for the prediction of RNA secondary structure. One of the main objectives of this software is to offer computational tools for the rational design of RNA-targeting nucleic acids, which include small interfering RNAs (siRNAs), antisense oligonucleotides and trans-cleaving ribozymes for gene knock-down studies. The methodology for siRNA design is based on a combination of RNA target accessibility prediction, siRNA duplex thermodynamic properties and empirical design rules. Our approach to target accessibility evaluation is an original extension of the underlying RNA folding algorithm to account for the likely existence of a population of structures for the target mRNA. In addition to the application modules Sirna, Soligo and Sribo for siRNAs, antisense oligos and ribozymes, respectively, the module Srna offers comprehensive features for statistical representation of sampled structures. Detailed output in both graphical and text formats is available for all modules. The Sfold server is available at http://sfold.wadsworth.org and http://www.bioinfo.rpi.edu/applications/sfold.

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.

Bayesian Models for Multiple Local Sequence Alignment and Gibbs Sampling Strategies

Abstract A wealth of data concerning lifes basic molecules, proteins and nucleic acids, has emerged from the biotechnology revolution. The human genome project has accelerated the growth of these data. Multiple observations of homologous protein or nucleic acid sequences from different organisms are often available. But because mutations and sequence errors misalign these data, multiple sequence alignment has become an essential and valuable tool for understanding structures and functions of these molecules. A recently developed Gibbs sampling algorithm has been applied with substantial advantage in this setting. In this article we develop a full Bayesian foundation for this algorithm and present extensions that permit relaxation of two important restrictions. We also present a rank test for the assessment of the significance of multiple sequence alignment. As an example, we study the set of dinucleotide binding proteins and predict binding segments for dozens of its members.

Natural selection of life history attributes: An analytical approach

Abstract The life history attributes which maximize fitness can be established analytically through Fishers equation for reproductive value. Maximizing the reproductive value at age zero is equivalent to maximizing the ultimate rate of increase. As an example of the usefulness of this equality it is shown that when survivorship is uniformly reduced, the corresponding optimal maternal frequency is unaltered, even though the ultimate rate of increase is lowered by a known amount. A general life history model is proposed which links these demographic determinants of rate of increase with the energy utilization alternatives (as among maintenance, growth, and reproduction) characterizing an individual organisms development. Since the energy partitioning alternatives at any age may depend on previous allocations, an organism state variable is introduced to describe the domain over which the maximization of reproductive value may take place. Further, if the reproductive value is to be a maximum at age zero, it must be maximized at every age. An optimal life history, then, is characterized by the energy allocations which maximize sequential reproductive values. Further examples of the utility of the model focus on growth vs reproduction decisions under biomass specific life history attributes. It is shown that if births per unit energy is a linear or convex function, then an organism will not simultaneously grow and reproduce. Determinant growth, biomass at first reproduction, and explicit calculation of the maximum ultimate rate of increase are also illustrated.

Estimated effect of breast self-examination and routine physician examinations on breast-cancer mortality.

We examined the effects of breast self-examination and breast examination by physicians on the stage of breast cancer at diagnosis. Clinical and pathological-staging information was compared to interview data on method of initial detection of 293 women. Tumors were detected in clinical Stage I 53.8% of the time when the detection method was routine physician examination, 37.7% when it was self-examination and only 27.0% when detection was accidental. Sixty-nine per cent of women practicing self-examination at the time of diagnosis discovered their tumor by this method. Differences were less apparent when pathological stage was considered. Tumors found during routine examination of the breast averaged 6.1 mm smaller in diameter than those discovered accidentally. We estimate that breast-cancer mortality might be reduced by 18.8% to 24.4% through self-examination or routine physician examination, respectively.

Extent and nature of contacts between protein molecules in crystal lattices and between subunits of protein oligomers.

A survey was compiled of several characteristics of the intersubunit contacts in 58 oligomeric proteins, and of the intermolecular contacts in the lattice for 223 protein crystal structures. The total number of atoms in contact and the secondary structure elements involved are similar in the two types of interfaces. Crystal contact patches are frequently smaller than patches involved in oligomer interfaces. Crystal contacts result from more numerous interactions by polar residues, compared with a tendency toward nonpolar amino acids at oligomer interfaces. Arginine is the only amino acid prominent in both types of interfaces. Potentials of mean force for residue–residue contacts at both crystal and oligomer interfaces were derived from comparison of the number of observed residue–residue interactions with the number expected by mass action. They show that hydrophobic interactions at oligomer interfaces favor aromatic amino acids and methionine over aliphatic amino acids; and that crystal contacts form in such a way as to avoid inclusion of hydrophobic interactions. They also suggest that complex salt bridges with certain amino acid compositions might be important in oligomer formation. For a protein that is recalcitrant to crystallization, substitution of lysine residues with arginine or glutamine is a recommended strategy. Proteins 28:494–514, 1997.

Bayesian inference on biopolymer models.

MOTIVATION Most existing bioinformatics methods are limited to making point estimates of one variable, e.g. the optimal alignment, with fixed input values for all other variables, e.g. gap penalties and scoring matrices. While the requirement to specify parameters remains one of the more vexing issues in bioinformatics, it is a reflection of a larger issue: the need to broaden the view on statistical inference in bioinformatics. RESULTS The assignment of probabilities for all possible values of all unknown variables in a problem in the form of a posterior distribution is the goal of Bayesian inference. Here we show how this goal can be achieved for most bioinformatics methods that use dynamic programming. Specifically, a tutorial style description of a Bayesian inference procedure for segmentation of a sequence based on the heterogeneity in its composition is given. In addition, full Bayesian inference algorithms for sequence alignment are described. AVAILABILITY Software and a set of transparencies for a tutorial describing these ideas are available at http://www.wadsworth.org/res&res/bioinfo/

Algorithms for the optimal identification of segment neighborhoods

Two algorithms for the efficient identification of segment neighborhoods are presented. A segment neighborhood is a set of contiguous residues that share common features. Two procedures are developed to efficiently find estimates for the parameters of the model that describe these features and for the residues that define the boundaries of each segment neighborhood. The algorithms can accept nearly any model of segment neighborhood, and can be applied with a broad class of best fit functions including least squares and maximum likelihood. The algorithms successively identify the most important features of the sequence. The application of one of these methods to the haemagglutinin protein of influenza virus reveals a possible mechanism for conformational change through the finding of a break in a strong heptad repeat structure.