Hybrid genome assembly and annotation of Danionella translucida

Studying neuronal circuits at cellular resolution is very challenging in vertebrates due to the size and optical turbidity of their brains. Danionella translucida, a close relative of zebrafish, was recently introduced as a model organism for investigating neural network interactions in adult individuals. Danionella remains transparent throughout its life, has the smallest known vertebrate brain and possesses a rich repertoire of complex behaviours. Here we sequenced, assembled and annotated the Danionella translucida genome employing a hybrid Illumina/Nanopore read library as well as RNA-seq of embryonic, larval and adult mRNA. We achieved high assembly continuity using low-coverage long-read data and annotated a large fraction of the transcriptome. This dataset will pave the way for molecular research and targeted genetic manipulation of this novel model organism

Exploring nest structures of acorn dwelling ants with x-ray microtomography and surface based 3D visibility graph analysis

The physical spaces within which organisms live affect their biology and in many cases can be considered part of their extended phenotype. The nests of social insect societies have a fundamental impact on their ability to function as complex superorganisms. Ants in many species excavate elaborate subterranean nests, but others inhabit relatively small pre-formed cavities within rock crevices and hollow seeds. Temnothorax ants, which often nest within acorns, have become a model system for studying collective decision making. While these ants have demonstrated remarkable degrees of rationality and consistent precision with regard to their nest choices, never before has the fine scale internal architecture and spatial organization of their nests been investigated. We used x-ray microtomography to record high resolution 3D scans of Temnothorax colonies within their acorns. These data were then quantified using image segmentation and surface based 3D visibility graph analysis (sbVGA), a new computational methodology for analysing spatial structures. The visibility graph analysis method integrates knowledge from the field of architecture with the empirical study of animal- built structures, thus providing the first methodological cross-disciplinary synergy of these two research areas. We found a surprisingly high surface area and degree of spatial heterogeneity within the acorn nests. Specific regions, such as those associated with the locations of queens and brood, were significantly more conducive to connectivity than others. From an architect’s point of view, spatial analysis research has never focused on all-surface 3D movement, as we describe within ant nests. Therefore, we believe our approach will provide new methods for understanding both human design and the comparative biology of habitat spaces

Large Genomes Assembly Using MAPREDUCE Framework

Knowing the genome sequence of an organism is the essential step toward understanding its genomic and genetic characteristics. Currently, whole genome shotgun (WGS) sequencing is the most widely used genome sequencing technique to determine the entire DNA sequence of an organism. Recent advances in next-generation sequencing (NGS) techniques have enabled biologists to generate large DNA sequences in a high-throughput and low-cost way. However, the assembly of NGS reads faces significant challenges due to short reads and an enormously high volume of data. Despite recent progress in genome assembly, current NGS assemblers cannot generate high-quality results or efficiently handle large genomes with billions of reads. In this research, we proposed a new Genome Assembler based on MapReduce (GAMR), which tackles both limitations. GAMR is based on a bi-directed de Bruijn graph and implemented using the MapReduce framework. We designed a distributed algorithm for each step in GAMR, making it scalable in assembling large-scale genomes. We also proposed novel gap-filling algorithms to improve assembly results to achieve higher accuracy and more extended continuity. We evaluated the assembly performance of GAMR using benchmark data and compared it against other NGS assemblers. We also demonstrated the scalability of GAMR by using it to assemble loblolly pine (~22Gbp). The results showed that GAMR finished the assembly much faster and with a much lower requirement of computing resources

A multistep bioinformatic approach detects putative regulatory elements in gene promoters

BACKGROUND: Searching for approximate patterns in large promoter sequences frequently produces an exceedingly high numbers of results. Our aim was to exploit biological knowledge for definition of a sheltered search space and of appropriate search parameters, in order to develop a method for identification of a tractable number of sequence motifs. RESULTS: Novel software (COOP) was developed for extraction of sequence motifs, based on clustering of exact or approximate patterns according to the frequency of their overlapping occurrences. Genomic sequences of 1 Kb upstream of 91 genes differentially expressed and/or encoding proteins with relevant function in adult human retina were analyzed. Methodology and results were tested by analysing 1,000 groups of putatively unrelated sequences, randomly selected among 17,156 human gene promoters. When applied to a sample of human promoters, the method identified 279 putative motifs frequently occurring in retina promoters sequences. Most of them are localized in the proximal portion of promoters, less variable in central region than in lateral regions and similar to known regulatory sequences. COOP software and reference manual are freely available upon request to the Authors. CONCLUSION: The approach described in this paper seems effective for identifying a tractable number of sequence motifs with putative regulatory role

Selfishness and Malice in Distributed Systems

Large-scale distributed systems are increasingly prevalent. Two issues can impact the performance of such systems: selfishness and malice. Selfish players can reduce social welfare of games, and malicious nodes can disrupt networks. In this dissertation, we provide algorithms to address both of these issues. One approach to ameliorating selfishness in large networks is the idea of a mediator. A mediator implements a correlated equilibrium when it proposes a strategy to each player privately such that the mediators proposal is the best interest for every player to follow. In this dissertation, we present a mediator that implements the best correlated equilibrium for an extended El Farol game. The extended El Farol game we consider has both positive and negative network effects. We study the degree to which this type of mediator can decrease the social cost. In particular, we give an exact characterization of Mediation Value (MV) and Enforcement Value (EV) for this game. MV measures the efficiency of our mediator compared to the best Nash equilibrium, and EV measures the efficiency of our mediator compared to the optimal social cost. This sort of exact characterization is uncommon for games with both kinds of network effects. An interesting outcome of our results is that both the MV and EV values can be unbounded for our game. Recent years have seen significant interest in designing networks that are self-healing in the sense that they can automatically recover from adversarial attacks. Previous work shows that it is possible for a network to automatically recover, even when an adversary repeatedly deletes nodes in the network. However, there have not yet been any algorithms that self-heal in the case where an adversary takes over nodes in the network. In this dissertation, we address this gap. In particular, we describe a communication network over n nodes that ensures the following properties, even when an adversary controls up to t ≤ (1/4 − ε)n nodes, for any constant ε \u3e 0. First, the network provides point-to-point communication with message cost and latency that are asymptotically optimal in an amortized sense. Second, the expected total number of message corruptions is O(t(log* n)^2), after which the adversarially controlled nodes are effectively quarantined so that they cause no more corruptions. In the problem of reliable multiparty computation (RMC), there are n parties, each with an individual input, and the parties want to jointly and reliably compute a function f over n inputs, assuming that it is not necessary to maintain the privacy of the inputs. The problem is complicated by the fact that an omniscient adversary controls a hidden fraction of the parties. We describe a self-healing algorithm for this problem. In particular, for a fixed function f, with n parties and m gates, we describe how to perform RMC repeatedly as the inputs to f change. Our algorithm maintains the following properties, even when an adversary controls up to t ≤ (1/4 − ε)n parties, for any constant ε \u3e 0. First, our algorithm performs each reliable computation with the following amortized resource costs: O(m + n log n) messages, O(m + n log n) computational operations, and O(\ell) latency, where \ell is the depth of the circuit that computes f. Second, the expected total number of corruptions is O(t(log* n)^2). Our empirical results show that the message cost reduces by up to a factor of 60 for communication and a factor of 65 for computation, compared to algorithms of no self-healing

Cooperative object transport with a swarm of e-puck robots: robustness and scalability of evolved collective strategies

Cooperative object transport in distributed multi-robot systems requires the coordination and synchronisation of pushing/pulling forces by a group of autonomous robots in order to transport items that cannot be transported by a single agent. The results of this study show that fairly robust and scalable collective transport strategies can be generated by robots equipped with a relatively simple sensory apparatus (i.e. no force sensors and no devices for direct communication). In the experiments described in this paper, homogeneous groups of physical e-puck robots are required to coordinate and synchronise their actions in order to transport a heavy rectangular cuboid object as far as possible from its starting position to an arbitrary direction. The robots are controlled by dynamic neural networks synthesised using evolutionary computation techniques. The best evolved controller demonstrates an effective group transport strategy that is robust to variability in the physical characteristics of the object (i.e. object mass and size of the longest object’s side) and scalable to different group sizes. To run these experiments, we designed, built, and mounted on the robots a new sensor that returns the agents’ displacement on a 2D plane. The study shows that the feedback generated by the robots’ sensors relative to the object’s movement is sufficient to allow the robots to coordinate their efforts and to sustain the transports for an extended period of time. By extensively analysing successful behavioural strategies, we illustrate the nature of the operational mechanisms underpinning the coordination and synchronisation of actions during group transport

Cooperative object transport with a swarm of e-puck robots: robustness and scalability of evolved collective strategies

Cooperative object transport in distributed multi-robot systems requires the coordination and synchronisation of pushing/pulling forces by a group of autonomous robots in order to transport items that cannot be transported by a single agent. The results of this study show that fairly robust and scalable collective transport strategies can be generated by robots equipped with a relatively simple sensory apparatus (i.e. no force sensors and no devices for direct communication). In the experiments described in this paper, homogeneous groups of physical e-puck robots are required to coordinate and synchronise their actions in order to transport a heavy rectangular cuboid object as far as possible from its starting position to an arbitrary direction. The robots are controlled by dynamic neural networks synthesised using evolutionary computation techniques. The best evolved controller demonstrates an effective group transport strategy that is robust to variability in the physical characteristics of the object (i.e. object mass and size of the longest object’s side) and scalable to different group sizes. To run these experiments, we designed, built, and mounted on the robots a new sensor that returns the agents’ displacement on a 2D plane. The study shows that the feedback generated by the robots’ sensors relative to the object’s movement is sufficient to allow the robots to coordinate their efforts and to sustain the transports for an extended period of time. By extensively analysing successful behavioural strategies, we illustrate the nature of the operational mechanisms underpinning the coordination and synchronisation of actions during group transport

Spatio-Temporal Context in Agent-Based Meeting Scheduling

Meeting scheduling is a common task for organizations of all sizes. It involves searching for a time and place when and where all the participants can meet. However, scheduling a meeting is generally difficult in that it attempts to satisfy the preferences of all participants. Negotiation tends to be an iterative and time consuming task. Proxy agents can handle the negotiation on behalf of the individuals without sacrificing their privacy or overlooking their preferences. This thesis examines the implications of formalizing meeting scheduling as a spatiotemporal negotiation problem. The “Children in the Rectangular Forest” (CRF) canonical model is applied to meeting scheduling. By formalizing meeting scheduling within the CRF model, a generalized problem emerges that establishes a clear relationship with other spatiotemporal distributed scheduling problems. The thesis also examines the implications of the proposed formalization to meeting scheduling negotiations. A protocol for meeting location selection is presented and evaluated using simulations