IHMCIF: An Extension of the PDBx/mmCIF Data Standard for Integrative Structure Determination Methods

IHMCIF (github.com/ihmwg/IHMCIF) is a data information framework that supports archiving and disseminating macromolecular structures determined by integrative or hybrid modeling (IHM), and making them Findable, Accessible, Interoperable, and Reusable (FAIR). IHMCIF is an extension of the Protein Data Bank Exchange/macromolecular Crystallographic Information Framework (PDBx/mmCIF) that serves as the framework for the Protein Data Bank (PDB) to archive experimentally determined atomic structures of biological macromolecules and their complexes with one another and small molecule ligands (e.g., enzyme cofactors and drugs). IHMCIF serves as the foundational data standard for the PDB-Dev prototype system, developed for archiving and disseminating integrative structures. It utilizes a flexible data representation to describe integrative structures that span multiple spatiotemporal scales and structural states with definitions for restraints from a variety of experimental methods contributing to integrative structural biology. The IHMCIF extension was created with the benefit of considerable community input and recommendations gathered by the Worldwide Protein Data Bank (wwPDB) Task Force for Integrative or Hybrid Methods (wwpdb.org/task/hybrid). Herein, we describe the development of IHMCIF to support evolving methodologies and ongoing advancements in integrative structural biology. Ultimately, IHMCIF will facilitate the unification of PDB-Dev data and tools with the PDB archive so that integrative structures can be archived and disseminated through PDB.

Making Common Fund data more findable: catalyzing a data ecosystem

The Common Fund Data Ecosystem (CFDE) has created a flexible system of data federation that enables researchers to discover datasets from across the US National Institutes of Health Common Fund without requiring that data owners move, reformat, or rehost those data. This system is centered on a catalog that integrates detailed descriptions of biomedical datasets from individual Common Fund Programs' Data Coordination Centers (DCCs) into a uniform metadata model that can then be indexed and searched from a centralized portal. This Crosscut Metadata Model (C2M2) supports the wide variety of data types and metadata terms used by individual DCCs and can readily describe nearly all forms of biomedical research data. We detail its use to ingest and index data from 11 DCCs.

New system for archiving integrative structures

Structures of many complex biological assemblies are increasingly determined using integrative approaches, in which data from multiple experimental methods are combined. A standalone system, called PDB-Dev, has been developed for archiving integrative structures and making them publicly available. Here, the data standards and software tools that support PDB-Dev are described along with the new and updated components of the PDB-Dev data-collection, processing and archiving infrastructure. Following the FAIR (Findable, Accessible, Interoperable and Reusable) principles, PDB-Dev ensures that the results of integrative structure determinations are freely accessible to everyone.

Towards Co-Evolution of Data-Centric Ecosystems

Database evolution is a notoriously difficult task, and it is exacerbated by the necessity to evolve database-dependent applications. As science becomes increasingly dependent on sophisticated data management, the need to evolve an array of database-driven systems will only intensify. In this paper, we present an architecture for data-centric ecosystems that allows the components to seamlessly co-evolve by centralizing the models and mappings at the data service and pushing model-adaptive interactions to the database clients. Boundary objects fill the gap where applications are unable to adapt and need a stable interface to interact with the components of the ecosystem. Finally, evolution of the ecosystem is enabled via integrated schema modification and model management operations. We present use cases from actual experiences that demonstrate the utility of our approach.

Experiences with Deriva: An Asset Management Platform for Accelerating eScience

The pace of discovery in eScience is increasingly dependent on a scientist's ability to acquire, curate, integrate, analyze, and share large and diverse collections of data. It is all too common for investigators to spend inordinate amounts of time developing ad hoc procedures to manage their data. In previous work, we presented Deriva, a Scientific Asset Management System, designed to accelerate data driven discovery. In this paper, we report on the use of Deriva in a number of substantial and diverse eScience applications. We describe the lessons we have learned, both from the perspective of the Deriva technology, as well as the ability and willingness of scientists to incorporate Scientific Asset Management into their daily workflows.

Ohmage

Participatory sensing (PS) is a distributed data collection and analysis approach where individuals, acting alone or in groups, use their personal mobile devices to systematically explore interesting aspects of their lives and communities [Burke et al. 2006]. These mobile devices can be used to capture diverse spatiotemporal data through both intermittent self-report and continuous recording from on-board sensors and applications.

Ohmage (http://ohmage.org) is a modular and extensible open-source, mobile to Web PS platform that records, stores, analyzes, and visualizes data from both prompted self-report and continuous data streams. These data streams are authorable and can dynamically be deployed in diverse settings. Feedback from hundreds of behavioral and technology researchers, focus group participants, and end users has been integrated into ohmage through an iterative participatory design process. Ohmage has been used as an enabling platform in more than 20 independent projects in many disciplines. We summarize the PS requirements, challenges and key design objectives learned through our design process, and ohmage system architecture to achieve those objectives. The flexibility, modularity, and extensibility of ohmage in supporting diverse deployment settings are presented through three distinct case studies in education, health, and clinical research.

Network topology generators

Following the long-held belief that the Internet is hierarchical, the network topology generators most widely used by the Internet research community, Transit-Stub and Tiers, create networks with a deliberately hierarchical structure. However, in 1999 a seminal paper by Faloutsos et al. revealed that the Internet's degree distribution is a power-law. Because the degree distributions produced by the Transit-Stub and Tiers generators are not power-laws, the research community has largely dismissed them as inadequate and proposed new network generators that attempt to generate graphs with power-law degree distributions.Contrary to much of the current literature on network topology generators, this paper starts with the assumption that it is more important for network generators to accurately model the large-scale structure of the Internet (such as its hierarchical structure) than to faithfully imitate its local properties (such as the degree distribution). The purpose of this paper is to determine, using various topology metrics, which network generators better represent this large-scale structure. We find, much to our surprise, that network generators based on the degree distribution more accurately capture the large-scale structure of measured topologies. We then seek an explanation for this result by examining the nature of hierarchy in the Internet more closely; we find that degree-based generators produce a form of hierarchy that closely resembles the loosely hierarchical nature of the Internet.

Does AS size determine degree in as topology?

In a recent and much celebrated paper, Faloutsos <i>et al.</i> [6] found that the inter Autonomous System (AS) topology exhibits a power-law degree distribution. This result was quite unexpected in the networking community, and stirred significant interest in exploring the possible causes of this phenomenon. The work of Barabasi <i>et al.</i> [2], and its application to network topology generation in the work of Medina <i>et al.</i> [9], have explored a promising class of models that yield strict power-law degree distributions. These models, which we will refer to collectively as the <i>B-A model</i>, describe the detailed dynamics of the network growth process, modeling the way in which connections are made between ASs. There are two simple connectivity rules that define the evolution of AS connectivity over time: <i>incremental growth</i> where a new AS connects to existing ASs, and <i>preferential connectivity</i> where the likelihood of connecting to an AS is proportional to the vertex outdegree of the target AS. These simple rules, which are similar to the classical "rich get richer" model originally proposed by Simon [12], lead to power-law degree distributions.

While the B-A model provably yields power-law vertex degree distributions, recent empirical evidence indicates that the model may not be consistent with the dynamics underlying the evolution of the actual AS topology. First, there is strong evidence [3, 4] that the degree distribution of the actual AS topology does not conform to a strict power law. However, the distribution is certainly <i>heavy-tailed</i> or <i>highly-variable</i> in the sense that the observed vertex degrees typically range over three or four orders of magnitude; in some cases, the <i>tail</i> of the degree distribution may fit a power law. These observations were gleaned from more complete pictures of AS - level connectivity (obtained by augmenting BGP route tables with peering relationships from other sources) than those used by earlier work [2, 6, 9]. Second, the B-A model's AS connectivity evolution rules can be shown to be inconsistent with empirical AS growth measurements [16]. As such, while the B-A model appears to produce topologies whose degree distribution characteristics exhibit power-law behavior, it cannot be a valid <i>explanation</i> for the connectivity evolution in the AS topology.

Clearly, some of these empirical observations don't corroborate the claim that the B-A model explains the phenomenon of highly variable vertex degrees in the Internet's AS topology [2]. However, the B-A model was originally proposed as a simple illustration of how some elementary mechanisms or rules can give rise to power law vertex degree distributions. As such, it is likely that the B-A model can be modified to accommodate these more recent findings [1], but we will neither discuss here such modifications nor comment on their possibility for success. Instead, we merely note that any such resulting model would seek, as does the original B-A model, to explain the highly variable degree distribution of the AS topology through the detailed dynamics of how connections between ASs are established.

The purpose of this note is to raise the question --- motivated by the B-A approach---of whether the underlying cause of the high variability phenomenon of the vertex degree distribution lies in the detailed dynamics of network growth, or if there are alternative explanations. To that end, we briefly outline an alternative explanation for the AS topology degree distribution. We do not claim to have proven that this explanation holds; our purpose here is merely to expand the dialog to a larger class of explanations for the variability of the AS topology degree distribution.

Scaling of multicast trees

One of the many benefits of multicast, when compared to traditional unicast, is that multicast reduces the overall network load. While the importance of multicast is beyond dispute, there have been surprisingly few attempts to quantify multicast's reduction in overall network load. The only substantial and quantitative effort we are aware of is that of Chuang and Sirbu [3]. They calculate the number of links L in a multicast delivery tree connecting a random source to m random and distinct network sites; extensive simulations over a range of networks suggest that L(m) &prop; m 0.8 . In this paper we examine the function L(m) in more detail and derive the asymptotic form for L(m) in k-ary trees. These results suggest one possible explanation for the universality of the Chuang-Sirbu scaling behavior.