SPECTRE: a suite of phylogenetic tools for reticulate evolution

Split networks are commonly used to visualize collections of bipartitions, also called splits, of a finite set. Such collections arise, for example, in evolutionary studies. Split networks can be viewed as a generalization of phylogenetic trees and may be generated using the SplitsTree package. Recently, the NeighborNet method for generating split networks has become rather popular, in part because it is guaranteed to always generate a circular split system, which can always be displayed by a planar split network. Even so, labels must be placed on the “outside” of the network, which might be problematic in some applications. To help circumvent this problem, it can be helpful to consider so-called flat split systems, which can be displayed by planar split networks where labels are allowed on the inside of the network too. Here, we present a new algorithm that is guaranteed to compute a minimal planar split network displaying a flat split system in polynomial time, provided the split system is given in a certain format. We will also briefly discuss two heuristics that could be useful for analyzing phylogeographic data and that allow the computation of flat split systems in this format in polynomial time.

SUMMARYIn this paper, the first in a series, the authors have developed and implemented new computational algorithms for improving the scalability of CFD simulations on emerging architectures such as multicore high performance computing (HPC) platforms. These algorithmic developments and implementations are classified into three categories: (i) improved partition for multicore platforms, (ii) improved and optimized communication for HPC and (iii) enhancing scalability using computer science based methods. In the first category, the multilevel partitioning strategy was modified to reduce the number of out‐of‐core communications. This resulted in noticeable speedup even for small cases. In the second category, the authors came up with a next generation communication procedure optimized for the architecture and the partitioning. This next generation communication resulted in noticeable speedups. In the third category, improvements with respect to better management of memory were implemented. This again resulted in a speedup of nearly 10%. The overall scalability, as a result of the three algorithmic implementations, yielded ideal and at times superlinear scalability until 3000 processors. In general, the scalability results are very promising and indicate that the approach has a great potential for more complicated multidisciplinary problems such as fluid–structure interaction and aeroelastic simulations. Copyright © 2013 John Wiley & Sons, Ltd.

It is our great pleasure to welcome you to the first annual 2011 Workshop on High-Performance Computing Meets Databases (HPCDB'11) co-located with Supercomputing in Seattle Washington. Informed by the SC2011 theme of data-intensive computing, we propose to bring together researchers in the databases/data management community (exemplified by venues such as SIGMOD, VLDB, and ICDE) and the high-performance computing community to identify opportunities for collaboration and set new research directions. Our thesis is that the techniques and strategies developed in the database community -- algebraic optimization, simple data parallel languages, data models as first-class citizens, logical data independence -- are applicable to problems faced by the HPC community as we enter the exascale age. Exascale HPC platforms will share characteristics with large-scale data processing platforms: relatively small main memory per node, relatively slow communication between nodes, and IO a limiting factor for performance. Further, HPC applications frequently experience "data challenges" beyond just IO performance: new data structures, new algorithms, new high-level languages. Finally, as HPC computations become longer-running, there is a need to make them more interactive. Ad hoc science questions such as "Which regions of the domain exhibit temperatures above some threshold?" and ad hoc systems monitoring questions such as "Which network links are congested?" can potentially be modeled, expressed, and optimized as queries using an approach similar to previous work on high performance querying of sensor networks. We invited 9 papers to be included in the workshop for both presentation and publication in this proceedings. Alex Szalay, a pioneer for the adaptation of databases to high-performance computing applications in a variety of areas beyond his own field of Astronomy, gave an invited talk on his experience building these systems that set the tone for the day. The topics spanned a range of application areas, author backgrounds, and techniques: we heard from those using database query optimization techniques to optimize parallel file systems (Naps et al.), high-level interfaces for deploying combined HPC and data-processing platforms in the cloud (Bunch et al.), a service-oriented approach to high-performance IO (Wu et al.), a detailed consideration of the role of database-style fault-tolerance for exa-scale platforms (van Dam et al.), and several compelling applications ranging from astronomy (Simmhan et al.) to text processing (Gao et al.). In addition to the website http://hpcdb.org, we have established a twitter account @hpcdb and a google group hpcdb to help organize this effort going forward, and more events are planned.

Use of the Bash command shell and language is one of the fundamental skills of a bioinformatician. This language is required for accessing high performance computing (HPC) services and effectively using these resources to improve your analyses. Bash is completely text based, which is different from many graphic based operating systems, but this language is also highly powerful, allowing for significant automation and reproducibility within analysis pipelines. This chapter aims to teach the fundamentals of Bash, including how to create files and folders, how to sort and search through files, and how to use pipes and loops to automate processes. By the end of this chapter, readers should be ready to undertake their first simple bioinformatics analysis.

During the development of scientific disciplines, mainstream periods alternate with revolution periods, where 'out of the way disciplines' can become a mainstream. Just in the moment increasing turbulences announce a new revolution. The variety of 'high performance computing' scenes will be mixed up. Can an increasing application of structurally programmable hardware platforms (computing by the yard) break the monopoly of the von Neumann mainstream paradigm (computing in time) also in multipurpose hardware? From a co-design point of view, the paper tries to provide an overview through the turbulences and tendencies, and introduces a fundamentally new machine paradigm, which uses a field-programmable data path array (FPDPA) providing instruction level parallelism. The paper drafts a structured design space for all kinds of parallel algorithm implementations and platforms: procedural programming versus structural programing, concurrent versus parallel, hardwired versus reconfigurable. A structured view by rearranging the variety of computing science scenes seems to be feasible.

Understanding the three-dimensional structure of an RNA molecule is often essential to understanding its function. Sampling algorithms and energy functions for RNA structure prediction are improving, due to the increasing diversity of structural data available for training statistical potentials and testing structural data, along with a steady supply of blind challenges through the RNA-Puzzles initiative. The recent FARFAR2 algorithm enables near-native structure predictions on fairly complex RNA structures, including automated selection of final candidate models and estimation of model accuracy. Here, we describe the use of a publicly available webserver for RNA modeling for realistic scenarios using FARFAR2, available at https://rosie.rosettacommons.org/farfar2 . We walk through two cases in some detail: a simple model pseudoknot from the frameshifting element of beet western yellows virus modeled using the "basic interface" to the webserver and a replication of RNA-Puzzle 20, a metagenomic twister sister ribozyme, using the "advanced interface." We also describe example runs of FARFAR2 modeling including two kinds of experimental data: a c-di-GMP riboswitch modeled with low-resolution restraints from MOHCA-seq experiments and a tandem GA motif modeled with 1H NMR chemical shifts.

The majority of industrial inspection activities across the globe involve considerable human efforts due to the requirements of robust detection abilities under varying lighting conditions, large field of view, higher speeds, and dynamic range of operation. In recent years, the motivation for integrating inspection and manufacturing operations on the shop floor has grown considerably to maximize the potential benefits of implementing Industry 4.0 initiatives. It is essential to digitize and integrate inspection of raw materials, in-process inventory, and finished components with manufacturing operations for achieving autonomous manufacturing goals. Machine vision offers an excellent alternative to manual inspection at a reduced cost, increased productivity, digital output, and negligible errors. However, its utility is restricted to the finished component inspection currently due to exorbitant equipment costs, requirements of specific lighting conditions, and a controlled operating environment. This paper presents the development of an integrated system combining a camera and ultrasonic sensor to develop a low-cost dimensional measurement system for inspection of raw material and in-process machined components in the shop floor setting. An indigenous inspection system is developed by integrating a camera, ultrasonic sensor, power source, and interactive user interface with onboard processing abilities. The augmentation of the ultrasonic sensor serves a dual purpose of eliminating the need for image calibration and dimensions measurement in the system. An interactive graphical user interface with acquisition trigger abilities and results display is developed using an open-source TkInter framework of the Python environment. A set of dimension measurements are performed for common raw material and in-process machined shapes using the developed setup. The comparison of results obtained using developed inspection setup and commercial machine vision system showed good agreement for common shapes. The efficacy of the developed system is validated for inspection of simple shapes with machining operations on the shop floor in the present work. The proposed system can be easily extended to inspect complex shapes such as gear profiles or camshafts as subsequent research work.

In this paper, a versatile edge-centered data structure is presented based on an analysis about classical representations of graph or network topology. The data structure has many good properties of convenient data accessing, compact topological representation and intuitionistic graph operating, and so on. Further, the data structure has capabilities of representing various networks with arbitrary complexity and topology and implementing codes of various graph algorithms with easy and efficient way. Based on the data structure and the breadth-first traversal strategy, an algorithm is proposed and built for efficiently calculating the shortest path between an given vertex and each other vertex in even very large-scale graph. Experiments and results show that our data structure has the better performance about time and space. It can act as basic data structure in implementations of various graph algorithms and in related applications.

In this paper, a versatile edge-centered data structure is presented based on an analysis about classical representations of graph or network topology. The data structure has many good properties of convenient data accessing, compact topological representation and intuitionistic graph operating, and so on. Further, the data structure has capabilities of representing various networks with arbitrary complexity and topology and implementing codes of various graph algorithms with easy and efficient way. Based on the data structure and the breadth-first traversal strategy, an algorithm is proposed and built for efficiently calculating the shortest path between an given vertex and each other vertex in even very large-scale graph. Experiments and results show that our data structure has the better performance about time and space. It can act as basic data structure in implementations of various graph algorithms and in related applications.

We propose data structures for maintaining shortest path in planar graphs in which the weight of an edge is modified. Our data structures allow us to compute after an update the shortest-path tree rooted at an arbitrary query node in time O(n√log log n) and to perform an update in O((log n)3). Our data structure can be also applied to the problem of maintaining the maximum flow problem in an s − t planar network.

98P - Standardized and compliant bio-informatics pipeline for neoepitope analysis in oncology clinical trials

The research area of Multimedia Content Analysis (MMCA) considers all aspects of the automated extraction of knowledge from multimedia archives and data streams. To satisfy the increasing computational demands of emerging MMCA problems, there is an urgent need to apply High Performance Computing (HPC) techniques. As most MMCA researchers are not also experts in the field of HPC, there is a demand for programming models and tools that can help MMCA researchers in applying these techniques. Ideally, such models and tools should be efficient and easy to use.At present there are several user transparent library-based tools available that aim to satisfy both these conditions. All such tools use a data parallel approach in which data structures (e.g. video frames) are scattered among the available compute nodes. However, for certain MMCA applications a data parallel approach induces intensive communication, which significantly decreases performance. In these situations, we can benefit from applying alternative parallelization approaches.This paper presents an innovative user transparent programming model for MMCA applications that employs task parallelism. We show our programming model to be a viable alternative that is capable of outperforming existing user transparent data parallel approaches. As a result, the model is an important next step towards our goal of integrating data and task parallelism under a familiar sequential programming interface.KeywordsSource NodeApplication Programming InterfaceHigh Performance ComputingTask GraphRuntime SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this paper, we investigate the feasibility of computing DNA patterns on FPGAs by utilising high-performance and parallel computing. In particular, the report analyses and contrasts the FSM -based core implementation of the well-known Smith-Waterman pair-wise sequence alignment algorithm with the dynamic and heuristic alternatives that are currently on the market. In this research project, we investigate the use of parallel and high-performance computing to determine how DNA patterns can be computed on FPGAs. In particular, the report details the findings of a study that compared the FSM -based core implementation of the well-known Smith-Waterman pair-wise sequence alignment algorithm with the algorithm's current dynamic and heuristic programming methods. The study was carried out by comparing these three aspects of the algorithm. The traditional methods of sequence alignment that are software-implied are not adequate to meet the actual data rate requirements. A strategy that is based on the use of hardware makes it possible to process multiple new databases in parallel, which enables a high degree of scalability. A finite state machine serves as the primary data processor in our demonstration of how to organize the sequences of amino acids in a protein (FSM). We also investigate the effectiveness of bit-based sequence alignment algorithms and present the inner stage pipelined FPGA architecture for implementing sequence alignment. Both of these are presented in this paper. Synchronized controllers are utilized in the programming techniques of the algorithm for the purpose of parallel sequence alignment. Inference is the foundation of traditional software-based methods for sequence alignment, but these methods are unable to keep up with the required data rates. A strategy that is based on hardware allows for the processing of parallel tasks with a large number of new databases and provides excellent scalability. Our explanation of the classification of protein sequences is based on a Finite State Machine, which serves as the primary processing unit (FSM). In addition to this, we investigate how well bit-based sequence alignment algorithms work and present the inner stage pipelined Field Programmable Gate Array architecture that can be used to implement sequence alignment. Synchronized controllers are utilized in the process of performing sequence alignment in parallel. The architecture as a whole was made to be scalable and fast, with a small number of computations and the ability to perform parallel processing in hardware using field-programmable gate arrays to compare bits in patterns. Finally, we use FPGA hardware synthesis to show off impressive performance and low resource consumption.

Background:Multiplexed tissue imaging enables the simultaneous detection of dozens of proteins at single-cell resolution, providing unprecedented insights into tissue organization and disease microenvironments. However, the resulting high-dimensional, gigabyte-scale datasets pose significant computational and methodological challenges. Existing analytical workflows, often fragmented between bespoke scripts and static visualizations, lack the scalability and user-friendly interfaces required for efficient, reproducible analysis. To overcome these limitations, we developed SPAC (analysis of SPAtial single-Cell datasets), a scalable, web-based ecosystem that integrates modular pipelines, high-performance computing (HPC) connectivity, and interactive visualization to democratize end-to-end single-cell spatial analysis applied to cellular positional data and protein expression levels.Results:SPAC is built on a modular, layered architecture that leverages community-based and newly developed tools for single-cell and spatial proteomics analysis. A specialized Python package extends these functionalities with custom analysis routines and established software engineering practices. An Interactive Analysis Layer provides web-hosted pipelines for configuring and executing complex workflows, and scalability enhancements that support distributed or parallel execution on GPU-enabled clusters. A Real-Time Visualization Layer delivers dynamic dashboards for immediate data exploration and sharing. As a showcase of its capabilities, SPAC was applied to a 4T1 breast cancer model, analyzing a multiplex imaging dataset comprising 2.6 million cells. GPU acceleration reduced unsupervised clustering runtimes from several hours to under ten minutes, and real-time visualization enabled detailed spatial characterization of tumor subregions.Conclusions:SPAC effectively overcomes key challenges in spatial single-cell analysis by streamlining high-throughput data processing and spatial profiling within an accessible and scalable framework. Its robust architecture, interactive interface and ease of access have the potential to accelerate biomedical research and clinical applications by converting complex imaging data into actionable biological and clinical insights.

A fundamental building block in any graph algorithm is a graph container - - a data structure used to represent the graph. Ideally, a graph container enables efficient access to the underlying graph, has low space usage, and supports updating the graph efficiently. In this paper, we conduct an extensive empirical evaluation of graph containers designed to support running algorithms on large graphs. To our knowledge, this is the first apples-to-apples comparison of graph containers rather than overall systems, which include confounding factors such as differences in algorithm implementations and infrastructure. We measure the running time of 10 highly-optimized algorithms across over 20 different containers and 10 graphs. Somewhat surprisingly, we find that the average algorithm running time does not differ much across containers, especially those that support dynamic updates. Specifically, a simple container based on an off-the-shelf B-tree is only 1.22× slower on average than a highly optimized static one. Moreover, we observe that simplifying a graph-container Application Programming Interface (API) to only a few simple functions incurs a mere 1.16× slowdown compared to a complete API. Finally, we also measure batch-insert throughput in dynamic-graph containers for a full picture of their performance. To perform the benchmarks, we introduce BYO, a unified framework that standardizes evaluations of graph-algorithm performance across different graph containers. BYO extends the Graph Based Benchmark Suite (Dhulipala et al. 18), a state-of-the-art graph algorithm benchmark, to easily plug into different dynamic graph containers and enable fair comparisons between them on a large suite of graph algorithms. While several graph algorithm benchmarks have been developed to date, to the best of our knowledge, BYO is the first system designed to benchmark graph containers.