Supervised machine learning is an essential but difficult to use approach in biomedical data analysis. The Galaxy-ML toolkit (https://galaxyproject.org/community/machine-learning/) makes supervised machine learning more accessible to biomedical scientists by enabling them to perform end-to-end reproducible machine learning analyses at large scale using only a web browser. Galaxy-ML extends Galaxy (https://galaxyproject.org), a biomedical computational workbench used by tens of thousands of scientists across the world, with a suite of tools for all aspects of supervised machine learning.

MOTIVATION: Large biomedical datasets, such as those from genomics and imaging, are increasingly being stored on commercial and institutional cloud computing platforms. This is because cloud-scale computing resources, from robust backup to high-speed data transfer to scalable compute and storage, are needed to make these large datasets usable. However, one challenge for large-scale biomedical data on the cloud is providing secure access, especially when datasets are distributed across platforms. While there are open Web protocols for secure authentication and authorization, these protocols are not in wide use in bioinformatics and are difficult to use for even technologically sophisticated users. RESULTS: We have developed a generic and extensible approach for securely accessing biomedical datasets distributed across cloud computing platforms. Our approach combines OpenID Connect and OAuth2, best-practice Web protocols for authentication and authorization, together with Galaxy (https://galaxyproject.org), a web-based computational workbench used by thousands of scientists across the world. With our enhanced version of Galaxy, users can access and analyze data distributed across multiple cloud computing providers without any special knowledge of access/authorization protocols. Our approach does not require users to share permanent credentials (e.g. username, password, API key), instead relying on automatically generated temporary tokens that refresh as needed. Our approach is generalizable to most identity providers and cloud computing platforms. To the best of our knowledge, Galaxy is the only computational workbench where users can access biomedical datasets across multiple cloud computing platforms using best-practice Web security approaches and thereby minimize risks of unauthorized data access and credential use. AVAILABILITY AND IMPLEMENTATION: Freely available for academic and commercial use under the open-source Academic Free License (https://opensource.org/licenses/AFL-3.0) from the following Github repositories: https://github.com/galaxyproject/galaxy and https://github.com/galaxyproject/cloudauthz.

Scientists are sequencing new genomes at an increasing rate with the goal of associating genome contents with phenotypic traits. After a new genome is sequenced and assembled, structural gene annotation is often the first step in analysis. Despite advances in computational gene prediction algorithms, most eukaryotic genomes still benefit from manual gene annotation. This requires access to good genome browsers to enable annotators to visualize and evaluate multiple lines of evidence (e.g., sequence similarity, RNA sequencing [RNA-Seq] results, gene predictions, repeats) and necessitates many volunteers to participate in the work. To address the technical barriers to creating genome browsers, the Genomics Education Partnership (GEP; https://gep.wustl.edu/) has partnered with the Galaxy Project (https://galaxyproject.org) to develop G-OnRamp (http://g-onramp.org), a web-based platform for creating UCSC Genome Browser Assembly Hubs and JBrowse genome browsers. G-OnRamp also converts a JBrowse instance into an Apollo instance for collaborative genome annotations in research and educational settings. The genome browsers produced can be transferred to the CyVerse Data Store for long-term access. G-OnRamp enables researchers to easily visualize their experimental results, educators to create Course-based Undergraduate Research Experiences (CUREs) centered on genome annotation, and students to participate in genomics research. In the process, students learn about genes/genomes and about how to utilize large datasets. Development of G-OnRamp was guided by extensive user feedback. Sixty-five researchers/educators from >40 institutions participated through in-person workshops, which produced >20 genome browsers now available for research and education. Genome browsers generated for four parasitoid wasp species have been used in a CURE engaging students at 15 colleges and universities. Our assessment results in the classroom demonstrate that the genome browsers produced by G-OnRamp are effective tools for engaging undergraduates in research and in enabling their contributions to the scientific literature in genomics. Expansion of such genomics research/education partnerships will be beneficial to researchers, faculty, and students alike.

SUMMARY: G-OnRamp provides a user-friendly, web-based platform for collaborative, end-to-end annotation of eukaryotic genomes using UCSC Assembly Hubs and JBrowse/Apollo genome browsers with evidence tracks derived from sequence alignments, ab initio gene predictors, RNA-Seq data and repeat finders. G-OnRamp can be used to visualize large genomics datasets and to perform collaborative genome annotation projects in both research and educational settings. AVAILABILITY AND IMPLEMENTATION: The virtual machine images and tutorials are available on the G-OnRamp web site (http://g-onramp.org/deployments). The source code is available under an Academic Free License version 3.0 through the goeckslab GitHub repository (https://github.com/goeckslab). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

One-dimensional intervals incremental inverted index (Di4) is a multi-resolution, single-dimension indexing framework for efficient, scalable, and extensible computation of genomic interval expressions. The framework has a tri-layer architecture: the semantic layer provides orthogonal and generic means (including the support of user-defined function) of sense-making and higher-lever reasoning from region-based datasets; the logical layer provides building blocks for region calculus and topological relations between intervals; the physical layer abstracts from persistence technology and makes the model adaptable to variety of persistence technologies, spanning from small-scale (e.g., B+tree) to large-scale (e.g., LevelDB). The extensibility of Di4 to application scenarios is shown with an example of comparative evaluation of ChIP-seq and DNase-Seq replicates. Performance of Di4 is benchmarked for small and large scale scenarios under common bioinformatics application scenarios. Di4 is freely available from https://genometric.github.io/Di4.

Biomedical data exploration requires integrative analyses of large datasets using a diverse ecosystem of tools. For more than a decade, the Galaxy project (https://galaxyproject.org) has provided researchers with a web-based, user-friendly, scalable data analysis framework complemented by a rich ecosystem of tools (https://usegalaxy.org/toolshed) used to perform genomic, proteomic, metabolomic, and imaging experiments. Galaxy can be deployed on the cloud (https://launch.usegalaxy.org), institutional computing clusters, and personal computers, or readily used on a number of public servers (e.g., https://usegalaxy.org). In this paper, we present our plan and progress towards creating Galaxy-as-a-Service-a federation of distributed data and computing resources into a panoptic analysis platform. Users can leverage a pool of public and institutional resources, in addition to plugging-in their private resources, helping answer the challenge of resource divergence across various Galaxy instances and enabling seamless analysis of biomedical data.

Abstract Contact: [email protected]

"Scientific community" refers to a group of people collaborating together on scientific-research-related activities who also share common goals, interests, and values. Such communities play a key role in many bioinformatics activities. Communities may be linked to a specific location or institute, or involve people working at many different institutions and locations. Education and training is typically an important component of these communities, providing a valuable context in which to develop skills and expertise, while also strengthening links and relationships within the community. Scientific communities facilitate: (i) the exchange and development of ideas and expertise; (ii) career development; (iii) coordinated funding activities; (iv) interactions and engagement with professionals from other fields; and (v) other activities beneficial to individual participants, communities, and the scientific field as a whole. It is thus beneficial at many different levels to understand the general features of successful, high-impact bioinformatics communities; how individual participants can contribute to the success of these communities; and the role of education and training within these communities. We present here a quick guide to building and maintaining a successful, high-impact bioinformatics community, along with an overview of the general benefits of participating in such communities. This article grew out of contributions made by organizers, presenters, panelists, and other participants of the ISMB/ECCB 2013 workshop "The 'How To Guide' for Establishing a Successful Bioinformatics Network" at the 21st Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) and the 12th European Conference on Computational Biology (ECCB).

High throughput sequencing assays have given rise to the field of genomics and transformed biomedical research into a computational science. Due to the large size of genomics datasets, high-performance computing is essential for analysis. Galaxy (http://galaxyproject.org) is a popular Web-based platform that can be used for all facets of genomic analyses, including data retrieval and integration, multi-step analysis, repeated analyses via workflows, visualization, collaboration, and publication. This paper describes Galaxy and discusses four lessons learned from the development of Galaxy. First, Galaxy uses open, extensible frameworks so that it can be adapted to new technologies as they become available. Second, by leveraging Web technologies, Galaxy makes genomics tools accessible to everyone and provides a common platform for collaboration. Third, Galaxy fosters community amongst both developers and users and encourages each community to adapt and extend Galaxy to meet their needs. Finally, Galaxy software development and genomic research are closely coupled, and challenges encountered during genomic research drive Galaxy development.

Social navigation is a promising approach for supporting privacy and security management. By aggregating and presenting the choices made by others, social navigation systems can provide users with easily understandable guidance on security and privacy decisions, rather than requiring that they understand low-level technical details in order to make informed decisions. We have developed two prototype systems to explore how social navigation can help users manage their privacy and security. The Acumen system employs social navigation to address a common privacy activity, managing Internet cookies, and the Bonfire system uses social navigation to help users manage their personal firewall. Our experiences with Acumen and Bonfire suggest that, despite the promise of social navigation, there are significant challenges in applying these techniques to the domains of end-user privacy and security management. Due to features of these domains, individuals may misuse community data when making decisions, leading to incorrect individual decisions, inaccurate community data, and “herding ” behavior that is an example of what economists term an informational cascade. By understanding this phenomenon in these terms, we develop and present two general approaches for mitigating herding in social navigation systems that support end-user security and privacy management, mitigation via algorithms and mitigation via user interaction. Mitigation via user interaction is a novel and promising approach to mitigating cascades in social navigation systems. Categories and Subject Descriptors H5.3 [Information interfaces and presentation (e.g., HCI)]: Group and Organizational Interfaces- collaborative computing, theory and models. K4.1 [Computers and Society]: Public Policy Issues – privacy.

This paper presents research analyzing the role of computational technology in the domain of nonprofit fundraising. Nonprofits are a cornerstone of many societies and are especially prominent in the United States, where $295 billion, or slightly more than 2% of the U.S. Gross Domestic Product (i.e. total national revenue), was directed toward charitable causes in 2006. Nonprofits afford many worthwhile endeavors, including crisis relief, basic services to those in need, public education and the arts, and preservation of the natural environment. In this paper, we identify six roles that computational technology plays in support of nonprofit fundraising and present two models characterizing technology use in this domain: (1) a cycle of technology-assisted fundraising and (2) a model of relationships among stakeholders in technology-assisted fundraising. Finally, we identify challenges and research opportunities for collaborative computing in the unique and exciting nonprofit fundraising domain.

Saori is a computation infrastructure that enables users and end-user applications to leverage social networks to mediate information dissemination. Saori provides users with awareness of and control over the information dissemination process within social networks; Saori enables users to employ both technological and social methods to manage information sharing. Saori users can create policies that mediate sharing by exploiting social network structures. Saori also provides social data to users; this data encourages users to be accountable for how they share information. We integrated Saori into a Wiki Wiki Web to demonstrate a concrete use of the infrastructure.

NuggetMine is an intelligent groupware application that collaborates with a workgroup to increase information nugget sharing among the group. Information nuggets are small amounts of self-contained information, such as the URL of an interesting news article, a book title, or the time and location of a local art event. NuggetMine and the workgroup work together to build, maintain, and utilize a repository-or "mine"-of information nuggets. Group members submit nuggets to NuggetMine, which organizes and augments the submitted nuggets and provides a desktop interface to each group member. This interface makes it easy for group members to submit nuggets, view nuggets, and explore the mine. NuggetMine distributes the tasks necessary to share nuggets between it and the workgroup so as to best utilize the skills of each collaborator. In this paper, we describe the NuggetMine application and interface and present a pilot study of the application.

In this paper, we describe Augur, a groupware calendar system to support personal calendaring practices, informal workplace communication, and the socio-technical evolution of the calendar system within a workgroup. Successful design and deployment of groupware calendar systems have been shown to depend on several converging, interacting perspectives. We describe calendar-based work practices as viewed from these perspectives, and present the Augur system in support of them. Augur allows users to retain the flexibility of personal calendars by anticipating and compensating for inaccurate calendar entries and idiosyncratic event names. We employ predictive user models of event attendance, intelligent processing of calendar text, and discovery of shared events to drive novel calendar visualizations that facilitate interpersonal communication. In addition, we visualize calendar access to support privacy management and long-term evolution of the calendar system.

For intelligent interfaces attempting to learn a user's interests, the cost of obtaining labeled training instances is prohibitive because the user must directly label each training instance, and few users are willing to do so. We present an approach that circumvents the need for human-labeled pages. Instead, we learn “surrogate” tasks where the desired output is easily measured, such as the number of hyperlinks clicked on a page or the amount of scrolling performed. Our assumption is that these outputs will highly correlate with the user's interests. In other words, by unobtrusively “observing” the user's behavior we are able to learn functions of value. For example, an intelligent browser could silently observe the user's browsing behavior during the day, then use these training examples to learn such functions and gather, during the middle of the night, pages that are likely to be of interest to the user. Previous work has focused on learning a user profile by passively observing the hyperlinks clicked on and those passed over. We extend this approach by measuring user mouse and scrolling activity in addition to user browsing activity. We present empirical results that demonstrate our agent can accurately predict some easily measured aspects of one's use of his or her browser.