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PubMed1994

An improved system for exon recognition and gene modeling in human DNA sequences.

A new version of the GRAIL system (Uberbacher and Mural, 1991; Mural et al., 1992; Uberbacher et al., 1993), called GRAIL II, has recently been developed (Xu et al., 1994). GRAIL II is a hybrid AI system that supports a number of DNA sequence analysis tools including protein-coding region recognition, PolyA site and transcription promoter recognition, gene model construction, translation to protein, and DNA/protein database searching capabilities. This paper presents the core of GRAIL II, the coding exon recognition and gene model construction algorithms. The exon recognition algorithm recognizes coding exons by combining coding feature analysis and edge signal (acceptor/donor/translation-start sites) detection. Unlike the original GRAIL system (Uberbacher and Mural, 1991; Mural et al., 1992), this algorithm uses variable-length windows tailored to each potential exon candidate, making its performance almost exon length-independent. In this algorithm, the recognition process is divided into four steps. Initially a large number of possible coding exon candidates are generated. Then a rule-based prescreening algorithm eliminates the majority of the improbable candidates. As the kernel of the recognition algorithm, three neural networks are trained to evaluate the remaining candidates. The outputs of the neural networks are then divided into clusters of candidates, corresponding to presumed exons. The algorithm makes its final prediction by picking the best canadidate from each cluster. The gene construction algorithm (Xu, Mural and Uberbacher, 1994) uses a dynamic programming approach to build gene models by using as input the clusters predicted by the exon recognition algorithm. Extensive testing has been done on these two algorithms.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗
PubMed1995

Multiple alignment using hidden Markov models.

A simulated annealing method is described for training hidden Markov models and producing multiple sequence alignments from initially unaligned protein or DNA sequences. Simulated annealing in turn uses a dynamic programming algorithm for correctly sampling suboptimal multiple alignments according to their probability and a Boltzmann temperature factor. The quality of simulated annealing alignments is evaluated on structural alignments of ten different protein families, and compared to the performance of other HMM training methods and the ClustalW program. Simulated annealing is better able to find near-global optima in the multiple alignment probability landscape than the other tested HMM training methods. Neither ClustalW nor simulated annealing produce consistently better alignments compared to each other. Examination of the specific cases in which ClustalW outperforms simulated annealing, and vice versa, provides insight into the strengths and weaknesses of current hidden Markov model approaches.

Algorithms↗
PubMed1995

Automatic RNA secondary structure determination with stochastic context-free grammars.

We have developed a method for predicting the common secondary structure of large RNA multiple alignments using only the information in the alignment. It uses a series of progressively more sensitive searches of the data in an iterative manner to discover regions of base pairing; the first pass examines the entire multiple alignment. The searching uses two methods to find base pairings. Mutual information is used to measure covariation between pairs of columns in the multiple alignment and a minimum length encoding method is used to detect column pairs with high potential to base pair. Dynamic programming is used to recover the optimal tree made up of the best potential base pairs and to create a stochastic context-free grammar. The information in the tree guides the next iteration of searching. The method is similar to the traditional comparative sequence analysis technique. The method correctly identifies most of the common secondary structure in 16S and 23S rRNA.

Algorithms↗
PubMed1995

3-D lookup: fast protein structure database searches at 90% reliability.

There are far fewer classes of three-dimensional protein folds than sequence families but the problem of detecting three-dimensional similarities is NP-complete. We present a novel heuristic for identifying 3-D similarities between a query structure and the database of known protein structures. Many methods for structure alignment use a bottom-up approach, identifying first local matches and then solving a combinatorial problem in building up larger clusters of matching substructures. Here, the top-down approach is to start with the global comparison and select a rough superimposition using a fast 3-D lookup of secondary structure motifs. The superimposition is then extended to an alignment of C alpha atoms by an iterative dynamic programming step. An all-against-all comparison of 385 representative proteins (150,000 pair comparisons) took 1 day of computer time on a single R8000 processor. In other words, one query structure is scanned against the database in a matter of minutes. The method is rated at 90% reliability at capturing statistically significant similarities. It is useful as a rapid preprocessor to a comprehensive protein structure database search system.

Amino Acid Sequence↗
PubMed1995

TOPITS: threading one-dimensional predictions into three-dimensional structures.

Homology modelling, currently, is the only theoretical tool which can successfully predict protein 3D structure. As 3D structure is conserved in sequence families, homology modelling allows to predict 3D structure for 20% of SWISSPROT. 20% of the proteins in PDB are remote homologues to another PDB protein. Threading techniques attempt to predict such remote homologues based on sequence information. Here, a new threading method is presented. First, for a list of PDB proteins, 3D structure was projected onto 1D strings of secondary structure and relative solvent accessibility. Then, secondary structure and accessibility were predicted by neural network systems (PHD). Finally, the predicted and observed 1D strings were aligned by dynamic programming. The resulting alignment was used to detect remote 3D homologues. Four results stand out. Firstly, even for an optimal prediction (assignment based on known structure), only about half the hits that ranked above a given threshold were correctly identified as remote homologues; only about 25% of the first hits were correct. Secondly, real predictions (PHD) were not much worse: about 20% of the first hits were correct. Thirdly, a simple filtering procedure improved prediction performance to about 30% correct first hits. The correct hit ranked among the first three for more than 23 out of 46 cases. Fourthly, the combination of the 1D threading and sequence alignments markedly improved the performance of the threading method TOPITS for some selected cases.

Amino Acid Sequence↗
PubMed1995

Automata-theoretic models of mutation and alignment.

Finite-state automata called transducers, which have both input and output, can be used to model simple mechanisms of biological mutation. We present a methodology whereby numerically-weighted versions of such specifications can be mechanically adapted to create string edit machines that are essentially equivalent to recurrence relations of the sort that characterize dynamic programming alignment algorithms. Based on this, we have developed a visual programming system for designing new alignment algorithms in a rapid-prototyping fashion.

Algorithms↗
PubMed1995

Discrete strategies of cancer post-treatment surveillance. Estimation and optimization problems.

We consider the cancer post-treatment surveillance to be represented by a discrete observation process with a non-zero false-negative rate. Using a simple stochastic model of cancer recurrence derived within the random minima framework, we obtain parametric estimates of both the time-to-recurrence distribution and the probability of false-negative diagnosis. Then assuming the false-negative rate known, we give a nonparametric maximum likelihood estimator for the tumor latency time distribution. When designing an optimal strategy of post-treatment surveillance, we proceed from the minimum of the expected delay in detecting tumor recurrence as a pertinent criterion of optimality. To solve this problem we give a dynamic programming algorithm. We illustrate the methods by analyzing data on breast cancer recurrence.

Algorithms↗
PubMed1996

A generalized hidden Markov model for the recognition of human genes in DNA.

We present a statistical model of genes in DNA. A Generalized Hidden Markov Model (GHMM) provides the framework for describing the grammar of a legal parse of a DNA sequence (Stormo & Haussler 1994). Probabilities are assigned to transitions between states in the GHMM and to the generation of each nucleotide base given a particular state. Machine learning techniques are applied to optimize these probabilities using a standardized training set. Given a new candidate sequence, the best parse is deduced from the model using a dynamic programming algorithm to identify the path through the model with maximum probability. The GHMM is flexible and modular, so new sensors and additional states can be inserted easily. In addition, it provides simple solutions for integrating cardinality constraints, reading frame constraints, "indels", and homology searching. The description and results of an implementation of such a gene-finding model, called Genie, is presented. The exon sensor is a codon frequency model conditioned on windowed nucleotide frequency and the preceding codon. Two neural networks are used, as in (Brunak, Engelbrecht, & Knudsen 1991), for splice site prediction. We show that this simple model performs quite well. For a cross-validated standard test set of 304 genes [ftp:@www-hgc.lbl.gov/pub/genesets] in human DNA, our gene-finding system identified up to 85% of protein-coding bases correctly with a specificity of 80%. 58% of exons were exactly identified with a specificity of 51%. Genie is shown to perform favorably compared with several other gene-finding systems.

Chromosomes, Human↗
PubMed1996

Compact encoding strategies for DNA sequence similarity search.

Determining whether two DNA sequences are similar is an essential component of DNA sequence analysis. Dynamic programming is the algorithm of choice if computational time is not the most important consideration. Heuristic search tools, such as BLAST, are computationally more efficient, but they may miss some of the sequence similarities (Altschul et al., 1990). These tools often use common k-tuples (words) between the two sequences to determine anchor points for the alignment, and spend most of their computational time extending the alignment beyond these anchor points. We discuss and provide a DNA sequence similarity search implementation (called SENSEI) that improves upon the performance of BLASTN by almost an order of magnitude for comparable sensitivity. This improvement is a result of using compactly encoded scoring tables for k-tuples, encoding bases with a single bit, filtering the sequence to remove the simple sequence repeats using XNUN, and masking the known species-specific repeats in the query sequence. To reduce memory requirements, especially for large genomic DNA query sequences, we recommend generating the neighborhood words from the target sequence at run-time, instead of generating them by preprocessing the query sequence.

Base Sequence↗
PubMed1997

Hierarchical protein structure superposition using both secondary structure and atomic representations.

The structural comparison of proteins has become increasingly important as a means to identify protein motifs and fold families. In this paper we present a new algorithm for the comparison of proteins based on a hierarchy of structural representations, from the secondary structure level to the atomic level. Our technique represents alpha-helices and beta-strands as vectors and uses a set of seven scoring functions to compare pairs of vectors from different proteins. The scores obtained are used in a dynamic programming algorithm that finds the best local alignment of the two sets of vectors. The second step in our algorithm is based on the atomic coordinates of the protein structures and improves the initial vector alignment by iteratively minimizing the RMSD between pairs of nearest atoms from the two proteins. We refine the final alignment by determining a core of well aligned atoms and minimizing the RMSD of this core. In a comparison of our method to Holm and Sander's DALI algorithm, our program was able to detect structural similarity at the same level as DALI. We also performed searches of a representative set of the Protein Data Bank (PDB) using our program and detected structurally similarity between several distantly related proteins.

Algorithms↗
PubMed1997

Integrating database homology in a probabilistic gene structure model.

We present an improved stochastic model of genes in DNA, and describe a method for integrating database homology into the probabilistic framework. A generalized hidden Markov model (GHMM) describes the grammar of a legal parse of a DNA sequence. Probabilities are estimated for gene features by using dynamic programming to combine information from multiple sensors. We show how matches to homologous sequences from a database can be integrated into the probability estimation by interpreting the likelihood of a sequence in terms of the bit-cost to encode a sequence given a homology match. We also demonstrate how homology matches in protein databases can be exploited to help identify splice sites. Our experiments show significant improvements in the sensitivity and specificity of gene structure identification when these new features are added to our gene-finding system, Genie. Experimental results in tests using a standard set of annotated genes showed that Genie identified 95% of coding nucleotides correctly with a specificity of 91%, and 77% of exons were identified exactly.

Algorithms↗
PubMed1998

Process models for production of beta-lactam antibiotics.

Great progress has been made in the modelling of biotechnical processes using filamentous microorganisms. This paper deals with cultivations of Penicillium chrysogenum for the production of Penicillin and of Acremonium chrysogenum for the production of Cephalosporin C. The properties of the processes and the existing models are reviewed. Models are presented for both processes that consider aspects which are important for industrial cultivation. The process model for Penicillin production is based on a detailed morphological description of growth of hyphal filaments and pellets. The model allows for simulation of the production process including the preculture and considering the inhomogenous pellet population. It opens new possibilities for understanding the complex kinetics of the process and improvement of its control. The structured segregated model for Cephalosporin C production considers soy oil as second carbon source besides sugar. The application of the model for dynamic optimization of feeding strategies by Iterative Dynamic Programming is demonstrated. As an alternative approach, modelling of the Cephalosporin production by an artificial neural network is discussed.

Acremonium↗
PubMed1998

Sequence assembly validation by multiple restriction digest fragment coverage analysis.

DNA sequence analysis depends on the accurate assembly of fragment reads for the determination of a consensus sequence. This report examines the possibility of analyzing multiple, independent restriction digests as a method for testing the fidelity of sequence assembly. A dynamic programming algorithm to determine the maximum likelihood alignment of error prone electrophoretic mobility data to the expected fragment mobilities given the consensus sequence and restriction enzymes is derived and used to assess the likelihood of detecting rearrangements in genomic sequencing projects. The method is shown to reliably detect errors in sequence fragment assembly without the necessity of making reference to an overlying physical map. An html form-based interface is available at http:/(/)www.ibc.wustl.edu/services/validate. html.

Algorithms↗
PubMed1998

Curve matching on brain surfaces using Frenet distances.

This paper describes methods for diffeomorphic matching of curves on brain surfaces. Distances between curves are defined by Frenet representation via speed, curvature, and torsion. The curvematching algorithm is based on bipartite graph matching, with weights defined by the Frenet distance over diffeomorphic maps of one curve onto the other (Sedgewick [1983]: Algorithms). We follow Khaneja ([1996]: Statistics and Geometry of Cortical Features) and define fundus curves on the brain surfaces as extremal curvature lines generated using dynamic programming. Examples are shown for fundus curve matchings on macaque brain surfaces.

Algorithms↗
PubMed1996

The new program OPAL for molecular dynamics simulations and energy refinements of biological macromolecules.

A new program for molecular dynamics (MD) simulation and energy refinement of biological macromolecules, OPAL, is introduced. Combined with the supporting program TRAJEC for the analysis of MD trajectories, OPAL affords high efficiency and flexibility for work with different force fields, and offers a user-friendly interface and extensive trajectory analysis capabilities. Salient features are computational speeds of up to 1.5 GFlops on vector supercomputers such as the NEC SX-3, ellipsoidal boundaries to reduce the system size for studies in explicit solvents, and natural treatment of the hydrostatic pressure. Practical applications of OPAL are illustrated with MD simulations of pure water, energy minimization of the NMR structure of the mixed disulfide of a mutant E. coli glutaredoxin with glutathione in different solvent models, and MD simulations of a small protein, pheromone Er-2, using either instantaneous or time-averaged NMR restraints, or no restraints.

Computer Simulation↗
PubMed2026

From immature to mature epithelium: unveiling structural dynamics and transcriptional programs in rainbow trout intestinal barrier.

The intestinal epithelium is crucial for nutrient absorption, immune defense, and barrier function in farmed fish. However, the molecular mechanisms underlying its development and maturation in salmonids remain poorly characterized, hindering our ability to address pervasive gut health challenges in aquaculture. In this study, we use the RTgutGC cell line to implement an epithelial maturation model with the aim of characterizing the global transcriptional program in rainbow trout (Oncorhynchus mykiss). We evaluated in vitro culture conditions to generate a polarized epithelial barrier with high transepithelial electrical resistance (TEER = 75.8 Ω × cm2), low permeability (6.2 × 10-6 cm/s), and well-defined apical specializations, including microvilli-like structures and clusters of these structures (brush border). Comparative transcriptomic profiling between immature (7 days post-seeding, dps) and mature (28 dps) epithelia revealed 3,817 differentially expressed genes (DEGs). Functional enrichment analysis showed that maturation was characterized by the downregulation of proliferative and ribosomal pathways and the concerted upregulation of processes critical for barrier function, including transmembrane transport, proteolysis, cell adhesion, extracellular matrix organization, and tight junction assembly. We identified a core set of 60 genes indicators of epithelial maturation, encompassing solute transporters (slc26a6, slc43a2), tight junction proteins (tjp1, cldn1, cldn3, cldn5, cldn18, among others), and structural components essential for microvilli formation and polarization (cdhr5b, pard6a). By integrating ultrastructural, functional, and transcriptomic data, this study established a framework for future mechanistic investigations into gut development and maturation in vitro. This set of mature epithelium indicators has diverse applications, such as the design of nutritional and pharmacological interventions to improve gut health and resilience in farmed fish.

Animals↗