Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research.This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Large-scale Networked Virtual Environments (NVEs) are used to enable rich interactive experiences such as multiplayer games, military and industrial training, and collaborative engineering systems. In these applications, hosts must exchange data updates to maintain a consistent view of the virtual environment. In large-scale environments, the volume of data places a heavy load on the networking and computational resources available to each host. To improve scalability, systems have employed a variety of techniques, including data subscription, data segmentation, and multicast. In this paper, we describe a communication architecture for Networked Virtual Environments that takes advantage of unstructured peer-to-peer (P2P) overlay networks for message distribution. The overlay network design provides scalability while supporting easy deployment over today's Internet. The approach, when combined with data segmentation and data subscription, enables Internet deployment of large-scale NVEs.

Peer-to-peer system decentralization complicates performance monitoring-and real-time monitoring is particularly challenging. We discuss our experience with monitoring the Peer Name Resolution Protocol (PNRP), a peer-to-peer name resolution/routing protocol and overlay deployed over the Internet. We must collect accurate performance data without adding traffic overhead. Unfortunately, no central node has sufficient monitoring information, and scalability and privacy concerns make it impractical to collect reliable real-time data from active client nodes. Our method obtains information from lightweight nodes (Weather Stations) actively deployed on the P2P network and themselves forming a private P2P overlay. We monitor the overall performance of the PNRP protocol on the Internet and produce timely reports supporting network management, security alerting, and system maintenance.

Maintaining desired data availability while minimizing costs is the primary challenge in designing P2P storages. Data placement schemes and data availability calculation methods are two key design choices. Prior work has used the random placement and steady-state availability analysis model widely. We find this approach has two drawbacks: (1) it ignores the time-related property and greatly underestimates data availability in the short term and (2) it ignores differences in peers' availability and uses a mean value, which also leads to the underestimation of data availability. Both of these drawbacks influence the efficiency of storage and bandwidth resource usage. Also, this approach can not ensure all the objects achieve their expected availability due to the second drawback. This paper develops a different data placement scheme called similar-MTTF-MTTR placement, which takes into consideration differences in pees' dynamic characteristics. Under this scheme, this paper also presents a fine-gained analysis model for short-term data availability calculation. As a result, our scheme improves the efficiency of data maintaining significantly. We validate this by comparing with random placement under three different dynamic environments: a wide-area system (PlanetLab), a corporative environment (desktop personal computers at Microsoft Corporation), and a file-sharing system (Maze). The results show that our scheme can save the storage resource usage significantly under all environments.

PNRP (peer name resolution protocol) is a decentralized name resolution and routing protocol built into the Windowstrade operating system that creates an Internet-scale peer-to-peer structured overlay. Ensuring the protocol's performance in Internet scale presents a critical challenge. We describe Internet scale testing of PNRP using a distributed network simulator, WiDS. We use real-world Internet characteristics and PNRP node behaviors to simulate the Internet environment, assess PNRP performance, and support protocol debugging. A case study demonstrates identification of a protocol flaw that could otherwise only be discovered through full-scale deployment experience

Faster networks, faster processors, and standardized protocols have enabled the emergence of interactive applications running over commercial networks such as the Internet. In such applications, multiple users interact with one another by exchanging real-time information such as user position and orientation in a virtual world, live and recorded audio, video, and text. These applications include interactive shopping, team training, virtual meeting rooms, and multi-player games. However, to date, these interactive systems have supported a limited number of information types, offered limited scalability, and have failed to account for a heterogeneous network and processor environment. In this paper, we describe the design and implementation of InVerse, an infrastructure that supports real-time interactive applications on the Internet. InVerse provides a common backplane for disseminating and managing multiple real-time data streams. Within this general-purpose structure, the InVerse system maximizes scalability by implementing a hybrid communications architecture that adapts itself to available network bandwidth, observed network latency, installed network security measures, and available services such as multicast.

Distributed interactive simulation systems are growing to include well over 100,000 dynamic entities for applications such as multiplayer video games, military and industrial training, and collaborative engineering. In these applications, each host receives updates (such as position and orientation) from remote entities, models and renders the scene, and performs other tasks such as collision detection. The number of entities places a heavy burden on both the networking resources and computational resources available to the application. To address these limitations, some systems have aggregated information about groups of simulation entities according to their organizational structure or their location within the virtual world. However traditional aggregation techniques are inadequate because remote hosts need to access entities based on both their organization and their virtual world position. This paper describes projection aggregations, a technique for grouping entities by both their organization and location. Remote hosts use projections to control which entities are represented locally and at what level-of-detail. We describe how projection aggregations are implemented in a networked environment and demonstrate how they reduce network bandwidth and computational requirements. Finally, we argue that projection aggregations represent a general-purpose framework for representing all simulation entities, thereby supporting evolution of entity models.

Corporate network firewalls are well-understood and are becoming commonplace. These firewalls establish a security perimeter that aims to block (or heavily restrict) both incoming and outgoing network communication. We argue that these firewalls are neither effective nor appropriate for academic or corporate research environments needing to maintain information security while still supporting the free exchange of ideas. In this paper we present the Stanford University Research Firewall (SURF), a network firewall design that is suitable for a research environment. While still protecting information and computing resources behind the firewall, this firewall is less restrictive of outward information flow than the traditional model; can be easily deployed; and can give internal users the illusion of unrestricted e-mail, anonymous FTP, and WWW connectivity to the greater Internet. Our experience demonstrates that an adequate firewall for a research environment can be constructed for minimal cost using off-the-shelf software and hardware components.

Reliable multicast communication is important in large-scale distributed applications. For example, reliable multicast is used to transmit terrain and environmental updates in distributed simulations. To date, proposed protocols have not supported these applications' requirements, which include wide-area data distribution, low-latency packet loss detection and recovery, and minimal data and management over-head within fine-grained multicast groups, each containing a single data source.In this paper, we introduce the notion of Log-Based Receiver-reliable Multicast (LBRM) communication, and we describe and evaluate a collection of log-based receiver reliable multicast optimizations that provide an efficient, scalable protocol for high-performance simulation applications. We argue that these techniques provide value to a broader range of applications and that the receiver-reliable model is an appropriate one for communication in general.