Metamorphic robots have gained the attention of many researchers due to their ability to change shape and adapt to various tasks. In order to utilize the versatility of metamorphic systems, we need to be able to find a shape-shifting (reconfiguration) plan efficiently; however, finding these plans is challenging due to the high degree of freedom of modular systems. Reconfiguration algorithms proposed so far either scale poorly with a growing number of modules, impose specific restrictions on modules, or produce plans that are unrealistic outside of zero-gravity environments. This paper presents a new approach to the reconfiguration problem of chain-type metamorphic robots. Our algorithm relies on forming tentacles and using them to transport modules, which allows us to search through a reduced state space by computing many smaller planning instances. As a result, we obtain a heuristic solution that is more scalable than optimal planners, while producing realistic plans that impose no specific module requirements.

BACKGROUND: Identifying orthologs continues to be an early and imperative step in genome analysis but remains a challenging problem. While synteny (conservation of gene order) has previously been used independently and in combination with other methods to identify orthologs, applying synteny in ortholog identification has yet to be automated in a user-friendly manner. This desire for automation and ease-of-use led us to develop OrthoRefine, a standalone program that uses synteny to refine ortholog identification. RESULTS: We developed OrthoRefine to improve the detection of orthologous genes by implementing a look-around window approach to detect synteny. We tested OrthoRefine in tandem with OrthoFinder, one of the most used software for identification of orthologs in recent years. We evaluated improvements provided by OrthoRefine in several bacterial and a eukaryotic dataset. OrthoRefine efficiently eliminates paralogs from orthologous groups detected by OrthoFinder. Using synteny increased specificity and functional ortholog identification; additionally, analysis of BLAST e-value, phylogenetics, and operon occurrence further supported using synteny for ortholog identification. A comparison of several window sizes suggested that smaller window sizes (eight genes) were generally the most suitable for identifying orthologs via synteny. However, larger windows (30 genes) performed better in datasets containing less closely related genomes. A typical run of OrthoRefine with ~ 10 bacterial genomes can be completed in a few minutes on a regular desktop PC. CONCLUSION: OrthoRefine is a simple-to-use, standalone tool that automates the application of synteny to improve ortholog detection. OrthoRefine is particularly efficient in eliminating paralogs from orthologous groups delineated by standard methods.

We present a new approach to tackle the problem of metamorphic robots' reconfiguration. Given the chain-type metamorphic robot's initial and target configuration, we compute a reconfiguration plan that is provably physically collision-free. Our solution employs a specific heuristic. The robot initially reconfigures to a shape that resembles an octopus with many tentacles. After that, the tentacles gradually reconnect to each other using inverse kinematics, separating one tentacle from the body and keeping the other one connected. This strategy eventually leads to a snake-like structure of the robot. For the target configuration, we compute the reconfiguration plan with the same procedure, however, we reverse the plan to reconfigure the robot from the snake-like structure to the target shape. According to our experimental evaluation, our newly introduced strategy for finding reconfiguration plans is successful. It efficiently finds collision-free plans even for robots consisting of hundreds of modules.

We present a new approach to tackle the problem of lattice-type metamorphic robots reconfiguration. We base our approach on a reduction to satisfiability modulo theory (SMT). Unlike the current state-of-the-art solutions, we consider the spatial limitations of the modules themselves and produce collision-free plans. We give an in-depth description of the reduction and discuss several optimizations for our technique. We also show an experimental evaluation of our approach and list possible future improvements to our technique.

We present RoFICoM, a new retractable connection device that allows for mechanical, electric, and data communication connection between separable robotic modules. The device is intentionally designed to be used in lattice-type metamorphic robots, however, its applicability is much wider. The main novelty of our solution lies primarily in a new unique flat design and spatial compactness of the connector. With a flat connector, much more space is left for the body of a robotic module in the structure. Moreover, the connector is also fully self-contained device with well defined mechanical, electrical and data interfaces, hence it can be easily embedded in various robotic solutions. Our RoFICoM connector is easy to produce, it is open-hardware and free for non-commercial use. In the paper, we give construction details and report on a couple of experiments we performed to demonstrate key features of the connection achieved with two RoFICoM devices.

Finding significant nucleotide sequence motifs in prokaryotic genomes can be divided into three types of tasks: (1) supervised motif finding, where a sample of motif sequences is used to find other similar sequences in genomes; (2) unsupervised motif finding, which typically relates to the task of finding regulatory motifs and protein binding sites and (3) exploratory motif finding, which aims to identify potential functionally significant sequence motifs as those that are unusual in some statistical sense. This article provides a conceptual overview for each type of task, a brief description of basic algorithms used in their solution, and a review of selected relevant software available online.

MOTIVATION: Genomes contain biologically significant information that extends beyond that encoded in genes. Some of this information relates to various short dispersed repeats distributed throughout the genome. The goal of this work was to combine tools for detection of statistically significant dispersed repeats in DNA sequences with tools to aid development of hypotheses regarding their possible physiological functions in an easy-to-use web-based environment. RESULTS: Ab Initio Motif Identification Environment (AIMIE) was designed to facilitate investigations of dispersed sequence motifs in prokaryotic genomes. We used AIMIE to analyze the Escherichia coli and Haemophilus influenzae genomes in order to demonstrate the utility of the new environment. AIMIE detected repeated extragenic palindrome (REP) elements, CRISPR repeats, uptake signal sequences, intergenic dyad sequences and several other over-represented sequence motifs. Distributional patterns of these motifs were analyzed using the tools included in AIMIE. AVAILABILITY: AIMIE and the related software can be accessed at our web site http://www.cmbl.uga.edu/software.html.

UNLABELLED: We present a new tool for finding local sequence patterns in long DNA sequences. The program, Pattern Locator, uses an intuitive syntax for pattern description, and provides more flexibility than existing programs by allowing combinations of specific nucleotide sequences, direct and inverted repeats, variable length tandem repeats of subpatterns, and a specified number of errors in any part of the pattern. AVAILABILITY: The program is available for download and as a web service accessible through a CGI interface at http://www.cmbl.uga.edu/software.html. The source code is written in C and distributed under the GNU General Public License.

We propose a novel, transparent and very simple algorithm to analyze middle-range correlations in genomic nucleotide sequences. Analysis by this algorithm of the EMBL Nucleotide Sequence Database demonstrates that all four nucleotides cluster in the genomic nucleotide sequences of eukaryotes on the scale of several hundred base pairs. In prokaryotes, the clustering is weak but still evident. The non-dominant three bases are deficient in the clusters, while A is the most deficient nucleotide in the clusters of C, and vice versa, and G is the most deficient nucleotide in the clusters of T, and vice versa. The algorithm also detects CG islands, extending over 1 kb, in vertebrate sequences. In plants, the CG islands are shown to be much smaller, if they exist at all. A clustering tendency is also exhibited by the TA doublet. Other doublets do not cluster. We observe no strong correlation between nucleotides separated in genomes by > 1 kb.

We present a program UNIREP, written in PowerBASIC for IBM-PCs, that identifies repetitive and unique nucleotide sequences in genomes or parts of genomes. A key feature of the algorithm is an oligonucleotide representation in a numerical code to make possible a comparison of all pairs of oligonucleotides (including overlaps) occurring in the analyzed sequence. This comparison assigns a score to each oligonucleotide, reflecting its similarity/dissimilarity to other oligonucleotides of the same length in the analyzed sequence. The score is plotted along the sequence so that peaks in the plot indicate repetitive regions and very low values reflect unique sequences. The scores are filtered to suppress or enhance the unique or repetitive sequences according to the user's wish. UNIREP is extended by auxiliary programs HIGHER and LOWER to list nucleotide sequences that have scores higher or lower than given limits. The potential of UNIREP is demonstrated using several long nucleotide sequences including the complete genomic sequence of EBV.

This paper describes the program GLOBIC, which compares, characterizes and fingerprints even 0.1 Mbase sequences in a few minutes with the aid of an IBM-AT microcomputer. Instead of the nucleotide sequences themselves, GLOBIC compares the local nucleotide or short oligonucleotide compositions. GLOBIC presents two-dimensional maps of contour lines depicting the similarity of two different sequences, a sequence compared to itself, to its complementary sequence or to a random sequence. A vocabulary is presented to translate the typical patterns appearing in the two-dimensional maps into their meanings as relationships between the compared sequences. The application of GLOBIC is demonstrated using several examples from the genomic nucleotide sequences of bacteriophage T7, adenovirus type-2 and Epstein-Barr virus.

This paper presents a simple program for interactive searching for nucleotide sequences that may code for the helix-turn-helix, zinc finger or leucine zipper motifs in proteins. The helix-turn-helix motifs are predicted using the recently published method of Dodd and Egan, while zinc fingers and leucine zippers are searched for by our original methods. DNABIND is shown to detect all four known helix-turn-helix motifs in bacteriophage lambda genes and both zinc fingers of the adr1 gene of yeast.

Jan Mrázek, Alena Spanová; ANAGEL: a personal computer program for evaluating DNA fragment lengths from distances migrated in an agarose gel, Bioinformatic

This article briefly describes our program Jamsek written in FORTRAN for an ICL 2950/10 computer. Jamsek combines statistical and stereochemical rules most frequently encountered in literature to predict protein secondary structure from its sequence, into a single algorithm. The composite algorithm does not work better than the best existing single algorithms of Garnier et al. (J. Mol. Biol., 120, 97-120, 1978) or Lim (J. Mol. Biol., 88, 873-894, 1974) if percentage of residues with a correctly predicted secondary structure is taken as a criterion. However, it is fairly reliable in predicting the total amount of alpha-helices and beta-sheets in proteins, the secondary structure of highly ordered proteins or their parts and identification of long alpha-helices. It surpasses the previous algorithms by providing a possibility to make a notion about confidence of the prediction of the particular secondary structure elements thanks to the simultaneous availability of four independent predictions of the secondary structure and other relevant data (hydrophobic profile and helical wheel representation). The main body of this article is devoted to a demonstration that output data of Jamsek can simply be used for the prediction of protein topological class, identification of globular proteins containing hydrophobic alpha-helices and, as an auxiliary means, to distinguish between protein coding and non-coding nucleotide sequences.