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Biomedical subjects

B A Shapiro

Publications and source records attributed to B A Shapiro.

At least 19 recordsLinked to original sources
PubMed2001

Prediction of DNA single-strand conformation polymorphism: analysis by capillary electrophoresis and computerized DNA modeling.

We have analyzed previously three representative p53 single-point mutations by capillary-electrophoresis single-strand conformation polymorphism (CE-SSCP). In the current study, we compared our CE-SSCP results with the potential secondary structures predicted by an RNA/DNA-folding algorithm with DNA energy rules, used in conjunction with a computer analysis workbench called STRUCTURELAB. Each of these mutations produces measurable shifts in CE migration times relative to wild type. Using computerized folding analysis, each of the mutations was found to have a conformational difference relative to wild type, which accounts for the observed differences in CE migration. Additional properties exhibited in the CE electropherograms were also explained using the computerized analysis. These include the appearance of secondary peaks and the temperature dependence of the electrophoretic patterns. The results yield insight into the mechanism of SSCP and how the conditions of this measurement, especially temperature, may be optimized to improve the sensitivity of the SSCP method. The results may also impact other diagnostic methods, which would benefit by a better understanding of DNA single-strand conformation polymorphisms to optimize conditions for enzymatic cleavage and DNA hybridization reactions.

Base Sequence↗
PubMed2001

RNA folding pathway functional intermediates: their prediction and analysis.

The massively parallel genetic algorithm (GA) for RNA structure prediction uses the concepts of mutation, recombination, and survival of the fittest to evolve a population of thousands of possible RNA structures toward a solution structure. As described below, the properties of the algorithm are ideally suited to use in the prediction of possible folding pathways and functional intermediates of RNA molecules given their sequences. Utilizing Stem Trace, an interactive visualization tool for RNA structure comparison, analysis of not only the solution ensembles developed by the algorithm, but also the stages of development of each of these solutions, can give strong insight into these folding pathways. The GA allows the incorporation of information from biological experiments, making it possible to test the influence of particular interactions between structural elements on the dynamics of the folding pathway. These methods are used to reveal the folding pathways of the potato spindle tuber viroid (PSTVd) and the host killing mechanism of Escherichia coli plasmid R1, both of which are successfully explored through the combination of the GA and Stem Trace. We also present novel intermediate folds of each molecule, which appear to be phylogenetically supported, as determined by use of the methods described below.

Algorithms↗
PubMed2001

The massively parallel genetic algorithm for RNA folding: MIMD implementation and population variation.

A massively parallel Genetic Algorithm (GA) has been applied to RNA sequence folding on three different computer architectures. The GA, an evolution-like algorithm that is applied to a large population of RNA structures based on a pool of helical stems derived from an RNA sequence, evolves this population in parallel. The algorithm was originally designed and developed for a 16384 processor SIMD (Single Instruction Multiple Data) MasPar MP-2. More recently it has been adapted to a 64 processor MIMD (Multiple Instruction Multiple Data) SGI ORIGIN 2000, and a 512 processor MIMD CRAY T3E. The MIMD version of the algorithm raises issues concerning RNA structure data-layout and processor communication. In addition, the effects of population variation on the predicted results are discussed. Also presented are the scaling properties of the algorithm from the perspective of the number of physical processors utilized and the number of virtual processors (RNA structures) operated upon.

Algorithms↗
PubMed1999

A Boltzmann filter improves the prediction of RNA folding pathways in a massively parallel genetic algorithm.

RNA folding using the massively parallel genetic algorithm (GA) has been enhanced by the addition of a Boltzmann filter. The filter uses the Boltzmann probability distribution in conjunction with Metropolis' relaxation algorithm. The combination of these two concepts within the GA's massively parallel computational environment helps guide the genetic algorithm to more accurately reflect RNA folding pathways and thus final solution structures. Helical regions (base-paired stems) now form in the structures based upon the stochastic properties of the thermodynamic parameters that have been determined from experiments. Thus, structural changes occur based upon the relative energetic impact that the change causes rather than just geometric conflicts alone. As a result, when comparing the predictions to phylogenetically determined structures, over multiple runs, fewer false-positive stems (predicted incorrectly) and more true-positive stems (predicted correctly) are generated, and the total number of predicted stems representing a solution is diminished. In addition, the significance (rate of occurrence) of the true-positive stems is increased. Thus, the predicted results more accurately reflect phylogenetically determined structures.

Algorithms↗
PubMed1999

New techniques for DNA sequence classification.

DNA sequence classification is the activity of determining whether or not an unlabeled sequence S belongs to an existing class C. This paper proposes two new techniques for DNA sequence classification. The first technique works by comparing the unlabeled sequence S with a group of active motifs discovered from the elements of C and by distinction with elements outside of C. The second technique generates and matches gapped fingerprints of S with elements of C. Experimental results obtained by running these algorithms on long and well conserved Alu sequences demonstrate the good performance of the presented methods compared with FASTA. When applied to less conserved and relatively short functional sites such as splice-junctions, a variation of the second technique combining fingerprinting with consensus sequence analysis gives better results than the current classifiers employing text compression and machine learning algorithms.

Algorithms↗
PubMed1997

Secondary structure computer prediction of the poliovirus 5' non-coding region is improved by a genetic algorithm.

Comparison of the secondary structure of the 5' non-coding region of poliovirus 3 RNA derived from the genetic algorithm with the model of Skinner et al. (J. Mol. Biol., 207, 379-392, 1989) demonstrates many of the confirmed structural elements. The genetic algorithm (Shapiro and Navetta, J. Supercomput., 8, 195-201, 1994) generates a population of all possible stems, then mixes, combines, and recombines these stems in multiple iterations on a massively parallel computer, ultimately selecting a most fit structure based on its energy. The secondary structure of the region containing the determinants of neurovirulence was better predicted using the genetic algorithm, whereas the dynamic programming algorithm (Zuker, Science, 244, 48-52, 1989) required phylogenetic comparative sequence analysis to arrive at the correct conclusion. In addition, artificial mutations were introduced throughout this region of the genome and although rearrangements in structure may occur, many structures persisted, suggesting that the given structures thus selected may have evolved to withstand isolated mutations. The genetic algorithm-derived structure for the 5' non-coding region compares favorably with the biological data and functions previously described, and contains all of the 'persistent' structures, suggesting also that the persistence factor may be an aid to validating structures.

Algorithms↗
PubMed1997

Predicting RNA H-type pseudoknots with the massively parallel genetic algorithm.

MOTIVATION: Using the genetic algorithm (GA) for RNA folding on a massively parallel supercomputer, MasPar MP-2 with 16,384 processors, we successfully predicted the existence of H-type pseudoknots in several sequences. RESULTS: The GA is applied to folding the tRNA-like 3' end of turnip yellow mosaic virus (TYMV) RNA sequence with 82 nucleotides, the 3' UTRs of satellite tobacco necrosis virus (STNV)-2 RNA sequence with 619 nucleotides and STNV-I RNA sequence with 622 nucleotides, and the bacteriophage T2, T4 and T6 gene 32 mRNA sequences with 946, 1340 and 946 nucleotides, respectively. The GA's results match the phylogenetically supported tertiary structures of these sequences.

Algorithms↗
PubMed1996

STRUCTURELAB: a heterogeneous bioinformatics system for RNA structure analysis.

STRUCTURELAB is a computational system that has been developed to permit the use of a broad array of approaches for the analysis of the structure of RNA. The goal of the development is to provide a large set of tools that can be well integrated with experimental biology to aid in the process of the determination of the underlying structure of RNA sequences. The approach taken views the structure determination problem as one of dealing with a database of many computationally generated structures and provides the capability to analyze this data set from different perspectives. Many algorithms are integrated into one system that also utilizes a heterogeneous computing approach permitting the use of several computer architectures to help solve the posed problems. These different computational platforms make it relatively easy to incorporate currently existing programs as well as newly developed algorithms and to best match these algorithms to the appropriate hardware. The system has been written in Common Lisp running on SUN or SGI Unix workstations, and it utilizes a network of participating machines defined in reconfigurable tables. A window-based interface makes this heterogeneous environment as transparent to the user as possible.

Algorithms↗
PubMed1996

An annealing mutation operator in the genetic algorithms for RNA folding.

An annealing mutation operator in the genetic algorithms (GA) for RNA folding on a MasPar MP-2 has been designed. The mutation probability descends along a hyperbola with respect to the size of the secondary structure, hence the total number of mutations at each generation drops linearly. Especially for long sequences with thousands of nucleotides as opposed to hundreds of nucleotides, the new mutation operator can make the distribution of free energies over all processors on MasPar MP-2 converge only after hundreds of generations. Based upon this new mutation operator, a technique to terminate the GA is also developed. The new mutation operator runs very efficiently. Some variations of the annealing mutation operator are also discussed.

Algorithms↗
PubMed1996

Complementary classification approaches for protein sequences.

We have studied five methods of protein classification and have applied them to the 768 groups of related proteins in the PROSITE catalog. Four of these methods are based on searching a database of blocks, and the other uses the frequently occurring motifs found in the protein families combined with a fingerprint technique. Our experimental results show that the block-based methods perform well when taking into account the probability of amino acids occurring in a block. Furthermore, the five methods give information that is complementary to each other. Thus, using the five methods together, one can obtain high confidence classifications (if the results agree) or suggest alternative hypotheses (if the results disagree). We also list those proteins whose current families documented in the PROSITE catalog differ from those suggested by our results. There are remarkably few of them, which is a testimony to the quality of PROSITE.

Amino Acid Sequence↗