A Unified Dynamic Programming Framework for the Analysis of Interacting Nucleic Acid Strands: Enhanced Models, Scalability, and Speed.

Dynamic programming algorithms within the NUPACK software suite enable analysis of nucleic acid sequences over complex and test tube ensembles containing arbitrary numbers of interacting strand species, serving the needs of researchers in molecular programming, nucleic acid nanotechnology, synthetic biology, and across the life sciences. Here, to enhance the underlying physical model, ensure scalability for large calculations, and achieve dramatic speedups when calculating diverse physical quantities over complex and test tube ensembles, we introduce a unified dynamic programming framework that combines three ingredients: (1) recursions that specify the dependencies between subproblems and incorporate the details of the structural ensemble and the free energy model, (2) evaluation algebras that define the mathematical form of each subproblem, (3) operation orders that specify the computational trajectory through the dependency graph of subproblems. The physical model is enhanced using new recursions that operate over the complex ensemble including coaxial and dangle stacking subensembles. The recursions are coded generically and then compiled with a quantity-specific evaluation algebra and operation order to generate an executable for each physical quantity: partition function, equilibrium base-pairing probabilities, MFE energy and proxy structure, suboptimal proxy structures, and Boltzmann sampled structures. For large complexes (e.g., 30 000 nt), scalability is achieved for partition function calculations using an overflow-safe evaluation algebra, and for equilibrium base-pairing probabilities using a backtrack-free operation order. A new blockwise operation order that treats subcomplex blocks for the complex species in a test tube ensemble enables dramatic speedups (e.g., 20-120× ) using vectorization and caching. With these performance enhancements, equilibrium analysis of substantial test tube ensembles can be performed in ≤ 1 min on a single computational core (e.g., partition function and equilibrium concentration for all complex species of up to six strands formed from two strand species of 300 nt each, or for all complex species of up to two strands formed from 80 strand species of 100 nt each). A new sampling algorithm simultaneously samples multiple structures from the complex ensemble to yield speedups of an order of magnitude or more as the number of structures increases above ≈103. These advances are available within the NUPACK 4.0 code base (www.nupack.org) which can be flexibly scripted using the all-new NUPACK Python module.

Large-Scale Sequence Comparison.

There are millions of sequences deposited in genomic databases, and it is an important task to categorize them according to their structural and functional roles. Sequence comparison is a prerequisite for proper categorization of both DNA and protein sequences, and helps in assigning a putative or hypothetical structure and function to a given sequence. There are various methods available for comparing sequences, alignment being first and foremost for sequences with a small number of base pairs as well as for large-scale genome comparison. Various tools are available for performing pairwise large sequence comparison. The best known tools either perform global alignment or generate local alignments between the two sequences. In this chapter we first provide basic information regarding sequence comparison. This is followed by the description of the PAM and BLOSUM matrices that form the basis of sequence comparison. We also give a practical overview of currently available methods such as BLAST and FASTA, followed by a description and overview of tools available for genome comparison including LAGAN, MumMER, BLASTZ, and AVID.

Prediction study on signal transduction pathways of integrin adhesome / 医用生物力学

Objective To predict signal transduction pathways of the integrin adhesome via bioinformatics method, so as to provide references for experimental study on the mechanism of integrin-related signal transduction pathways. Methods First, the interaction network between the interactive integrin adhesome was constructed, and the confidence probability of each interaction was used as its link weight, respectively. Secondly, the pathways of the minimum weight were identified via a standard dynamic programming algorithm. Finally, all pathways calculated by the algorithm were aggregated into some probable networks. Results Seven signal transduction pathways were obtained from the integrin adhesome interaction network that contained 147 components with 736 interactions. In every predicted signal transduction pathway, the coverage rate of the proteins with Gene Ontology annotation was calculated. Conclusions By research on signal transduction pathways the pathogenesis of some diseases can be explored at the molecular level. Several possible signal transduction pathways obtained in this study have some reference value for exploring disease mechanism in basic medical sciences, and also provide some useful information for such exploration under the stimulation of external signals including mechanical or chemical signals.

GENESUS: a two-step sequence design program for DNA nanostructure self-assembly.

DNA has been recognized as an ideal material for bottom-up construction of nanometer scale structures by self-assembly. The generation of sequences optimized for unique self-assembly (GENESUS) program reported here is a straightforward method for generating sets of strand sequences optimized for self-assembly of arbitrarily designed DNA nanostructures by a generate-candidates-and-choose-the-best strategy. A scalable procedure to prepare single-stranded DNA having arbitrary sequences is also presented. Strands for the assembly of various structures were designed and successfully constructed, validating both the program and the procedure.

Identifying multiple change points in a linear mixed effects model.

Although change-point analysis methods for longitudinal data have been developed, it is often of interest to detect multiple change points in longitudinal data. In this paper, we propose a linear mixed effects modeling framework for identifying multiple change points in longitudinal Gaussian data. Specifically, we develop a novel statistical and computational framework that integrates the expectation-maximization and the dynamic programming algorithms. We conduct a comprehensive simulation study to demonstrate the performance of our method. We illustrate our method with an analysis of data from a trial evaluating a behavioral intervention for the control of type I diabetes in adolescents with HbA1c as the longitudinal response variable.