SUMMARY: Many online sources of gene interaction networks supply rich visual data regarding gene pathways that can aid in the study of biological processes, disease research and drug discovery. PathGen incorporates data from several sources to create transitive connections that span multiple gene interaction databases. Results are displayed in a comprehensible graphical format, showing gene interaction type and strength, database source and microarray expression data. These features make PathGen a valuable tool for in silico discovery of novel gene interaction pathways, which can be experimentally tested and verified. The usefulness of PathGen interaction analyses was validated using genes connected to the altered facial development related to Down syndrome. AVAILABILITY: http://dna.cs.byu.edu/pathgen. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Further information is available at http://dna.cs.byu.edu/pathgen/PathGenSupplemental.pdf.

We present a new algorithm, ChemAlign, that uses physicochemical properties and secondary structure elements to create biologically relevant multiple sequence alignments (MSAs). Additionally, we introduce the physicochemical property difference (PPD) score for the evaluation of MSAs. This score is the normalized difference of physicochemical property values between a calculated and a reference alignment. It takes a step beyond sequence similarity and measures characteristics of the amino acids to provide a more biologically relevant metric. ChemAlign is able to produce more biologically correct alignments and can help to identify potential drug docking sites.

The performance of maximum likelihood searches can be boosted by using the most parsimonious tree as a starting point for the search. The time spent in performing the parsimony search to find this starting tree is insignificant compared to the time spent in the maximum likelihood search, leading to an overall gain in search time. These parsimony boosted maximum likelihood searches lead to topologies with scores statistically similar to the unboosted searches, but in less time.

The relationship between changes in gene expression and physical characteristics associated with Down syndrome is not well understood. Chromosome 21 genes interact with nonchromosome 21 genes to produce Down syndrome characteristics. This indirect influence, however, is difficult to empirically define due to the number, size, and complexity of the involved gene regulatory networks. This work links chromosome 21 genes to non-chromosome 21 genes known to interact in a Down syndrome phenotype through a reachability analysis of labeled transition graphs extracted from published gene regulatory network databases. The analysis provides new relations in a recently discovered link between a specific gene and Down syndrome phenotype. This type of formal analysis helps scientists direct empirical studies to unravel chromosome 21 gene interactions with the hope for therapeutic intervention.

Abstract Motivation: Multiple sequence alignments (MSAs) are at the heart of bioinformatics analysis. Recently, a number of multiple protein sequence alignment benchmarks (i.e. BAliBASE, OXBench, PREFAB and SMART) have been released to evaluate new and existing MSA applications. These databases have been well received by researchers and help to quantitatively evaluate MSA programs on protein sequences. Unfortunately, analogous DNA benchmarks are not available, making evaluation of MSA programs difficult for DNA sequences. Results: This work presents the first known multiple DNA sequence alignment benchmarks that are (1) comprised of protein-coding portions of DNA (2) based on biological features such as the tertiary structure of encoded proteins. These reference DNA databases contain a total of 3545 alignments, comprising of 68 581 sequences. Two versions of the database are available: mdsa_100s and mdsa_all. The mdsa_100s version contains the alignments of the data sets that TBLASTN found 100% sequence identity for each sequence. The mdsa_all version includes all hits with an E-value score above the threshold of 0.001. A primary use of these databases is to benchmark the performance of MSA applications on DNA data sets. The first such case study is included in the Supplementary Material. Availability: The databases, further details and the Supplementary Material are publicly available at http://csl.cs.byu.edu/mdsas/http://csl.cs.byu.edu/mdsas/ Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

DNA sequence alignment is a critical step in identifying homology between organism. The most widely used alignment program, ClustalW is known to suffer from the local minima problem, where suboptimal guide trees produce incorrect gap insertions. The optimization alignment approach, has been shown to be effective in combining alignment and phylogenetic search in order to avoid the problems associated with poor guide trees. The optimization alignment algorithm operates at a small grain size, aligning each tree found, wasting time producing multiple sequence alignments for suboptimal trees. This research develops and analyzes a large grain size algorithm for optimization alignment that iterates through steps of alignment and phylogeny search, thus improving the quality of guide trees used for computation of multiple sequence alignments and eliminating computation of multiple sequence alignments for sub-optimal guide trees. Local minima are avoided by the use of stochastic search methods. Large Grain Size Stochastic Optimization Alignment (LGA) exploits the relationship between phylogenies and multiple sequence alignments, and in so doing achieves improved alignment accuracy. LGA is licensed under the GNU General Public License. Source code and data sets are publicly available at http://csl.cs.byu.edu/lga/

Due to the high cost of producing hardware prototypes, software simulators are typically used to determine the performance of proposed systems. To accurately represent a system with a simulator, the simulator inputs need to be representative of actual system usage. Trace-driven simulators that use logs of actual usage are generally preferred by researchers and developers to other types of simulators to determine expected performance. In this paper we explain the design and results of a trace-driven simulator for Palm OS devices capable of starting in a specified state and replaying a log of inputs originally generated on a handheld. We collect the user inputs with an acceptable amount of overhead while a device is executing real applications in normal operating environments. We based our simulator on the deterministic state machine model. The model specifies that two equivalent systems that start in the same state and have the same inputs applied, follow the same execution paths. By replaying the collected inputs we are able to collect traces and performance statistics from the simulator that are representative of actual usage with minimal perturbation. Our simulator can be used to evaluate various hardware modifications to Palm OS devices such as adding a cache. At the end of this paper we present an in-depth case study analyzing the expected memory performance from adding a cache to a Palm m515 device