MOTIVATION: RNA-sequencing (RNA-seq) of tumor tissue is typically only used to measure gene expression. Here, we present a statistical approach that leverages existing RNA-seq data to also detect somatic copy number alterations (SCNAs), a pervasive phenomenon in human cancers, without a need to sequence the corresponding DNA. RESULTS: We present an analysis of 4942 participant samples from 28 cancers in The Cancer Genome Atlas (TCGA), demonstrating robust detection of SCNAs from RNA-seq. Using genotype imputation and haplotype information, our RNA-based method had a median sensitivity of 85% to detect SCNAs defined by DNA analysis, at high specificity (∼95%). As an example of translational potential, we successfully replicated SCNA features associated with breast cancer subtypes. Our results credential haplotype-based inference based on RNA-seq to detect SCNAs in clinical and population-based settings. AVAILABILITY AND IMPLEMENTATION: The analyses presented use the data publicly available from TCGA Research Network (http://cancergenome.nih.gov/). See Methods for details regarding data downloads. hapLOHseq software is freely available under The MIT license and can be downloaded from http://scheet.org/software.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: 'Next-generation' (NGS) sequencing has wide application in medical genetics, including the detection of somatic variation in cancer. The Ion Torrent-based (IONT) platform is among NGS technologies employed in clinical, research and diagnostic settings. However, identifying mutations from IONT deep sequencing with high confidence has remained a challenge. We compared various computational variant-calling methods to derive a variant identification pipeline that may improve the molecular diagnostic and research utility of IONT. RESULTS: Using IONT, we surveyed variants from the 409-gene Comprehensive Cancer Panel in whole-section tumors, intra-tumoral biopsies and matched normal samples obtained from frozen tissues and blood from four early-stage non-small cell lung cancer (NSCLC) patients. We used MuTect, Varscan2, IONT's proprietary Ion Reporter, and a simple subtraction we called "Poor Man's Caller." Together these produced calls at 637 loci across all samples. Visual validation of 434 called variants was performed, and performance of the methods assessed individually and in combination. Of the subset of inspected putative variant calls (n=223) in genomic regions that were not intronic or intergenic, 68 variants (30%) were deemed valid after visual inspection. Among the individual methods, the Ion Reporter method offered perhaps the most reasonable tradeoffs. Ion Reporter captured 83% of all discovered variants; 50% of its variants were visually validated. Aggregating results from multiple packages offered varied improvements in performance. CONCLUSIONS: Overall, Ion Reporter offered the most attractive performance among the individual callers. This study suggests combined strategies to maximize sensitivity and positive predictive value in variant calling using IONT deep sequencing.

MOTIVATION: The detection of subtle genomic allelic imbalance events has many potential applications. For example, identifying cancer-associated allelic imbalanced regions in low tumor-cellularity samples or in low-proportion tumor subclones can be used for early cancer detection, prognostic assessment and therapeutic selection in cancer patients. We developed hapLOHseq for the detection of subtle allelic imbalance events from next-generation sequencing data. RESULTS: Our method identified events of 10 megabases or greater occurring in as little as 16% of the sample in exome sequencing data (at 80×) and 4% in whole genome sequencing data (at 30×), far exceeding the capabilities of existing software. We also found hapLOHseq to be superior at detecting large chromosomal changes across a series of pancreatic samples from TCGA. AVAILABILITY AND IMPLEMENTATION: hapLOHseq is available at scheet.org/software, distributed under an open source MIT license. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

InfoSleuth is an agent-based system that can be configured to perform many different information management activities in a distributed environment. InfoSleuthTM agents provide a number of complex query services that require resolving ontology-based queries over dynamically changing, distributed, heterogeneous resources. These include distributed query processing, location-independent single-resource updates, event and information monitoring, statistical or inferential data analysis, and trend discovery in complex event streams. It has been used in numerous applications, including the Environmental Data Exchange Network and the Competitive Intelligence System.

The goal of the InfoSleuth project at MCC is to exploit and synthesize new technologies into a unified system that retrieves and processes information in an ever-changing network of information sources. InfoSleuth has its roots in the Carnot project at MCC, which specialized in integrating heterogeneous information bases. However, recent emerging technologies such as internetworking and the World Wide Web have significantly expanded the types, availability, and volume of data available to an information management system. Furthermore, in these new environments, there is no formal control over the registration of new information sources, and applications tend to be developed without complete knowledge of the resources that will be available when they are run. Federated database projects such as Carnot that do static data integration do not scale up and do not cope well with this ever-changing environment. On the other hand, recent Web technologies, based on keyword search engines, are scalable but, unlike federated databases, are incapable of accessing information based on concepts. In this experience paper, we describe the architecture, design, and implementation of a working version of InfoSleuth. We show how InfoSleuth integrates new technological developments such as agent technology, domain ontologies, brokerage, and internet computing, in support of mediated interoperation of data and services in a dynamic and open environment. We demonstrate the use of information brokering and domain ontologies as key elements for scalability.

Article Free Access Share on The InfoSleuth Project Authors: R. J. Bayardo Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , W. Bohrer Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , R. Brice Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , A. Cichocki Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , J. Fowler Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , A. Halal Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , V. Kashyap Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , T. Ksiezyk Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , G. Martin Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , M. Nodine Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , M. Rashid Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , M. Rusinkiewicz Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , R. Shea Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , C. Unnikrishnan Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , A. Unruh Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , D. Woelk Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile Authors Info & Claims SIGMOD '97: Proceedings of the 1997 ACM SIGMOD international conference on Management of dataJune 1997 Pages 543–545https://doi.org/10.1145/253260.253401Online:01 June 1997Publication History 7citation339DownloadsMetricsTotal Citations7Total Downloads339Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Article Free Access Share on Consortium: a framework for transactions in collaborative environments Authors: Vram Kouramajian Knight-Ridder Information, Inc., Mountain View, CA and Information Technology Development, Rice University, Houston, Texas Knight-Ridder Information, Inc., Mountain View, CA and Information Technology Development, Rice University, Houston, TexasView Profile , Ross Dargahi Knight-Ridder Information, Inc., Mountain View, CA and Information Technology Development, Rice University, Houston, Texas Knight-Ridder Information, Inc., Mountain View, CA and Information Technology Development, Rice University, Houston, TexasView Profile , Jerry Fowler Center for Information Infrastructure and Computing Sciences, Baylor College of Medicine, Houston, TX and Information Technology Development, Rice University, Houston, Texas Center for Information Infrastructure and Computing Sciences, Baylor College of Medicine, Houston, TX and Information Technology Development, Rice University, Houston, TexasView Profile , Donald Baker Information Technology Development, Rice University, Houston, Texas Information Technology Development, Rice University, Houston, TexasView Profile Authors Info & Claims CIKM '95: Proceedings of the fourth international conference on Information and knowledge managementDecember 1995 Pages 260–265https://doi.org/10.1145/221270.221585Published:02 December 1995Publication History 1citation325DownloadsMetricsTotal Citations1Total Downloads325Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

The Virtual Notebook System (VNS) is a distributed collaborative hypertext system that has made a successful transition from research prototype to commercial product. Experience in developing and deploying the VNS in diverse settings including biomedical research, undergraduate education, and collaborative system prototyping has developed insight into the use of systems for computer-supported cooperative work (CSCW). This paper provides a brief overview of the VNS, discusses some of its strengths and weaknesses with respect to collaboration, and draws some conclusions about the impact of metaphor and extensibility on the collaborative process.

The authors define a causal distributed breakpoint, which is initiated by a sequential breakpoint in one process of a distributed computation and restores each process in the computation to its earliest state that reflects all events that happened before the breakpoint. An algorithm for finding the causal distributed breakpoint, given a sequential breakpoint in one of the processes, is presented. Approximately consistent checkpoint sets are used for efficiently restoring each process to its state in a causal distributed breakpoint. Causal distributed breakpoints assume deterministic processes that communicate solely by messages. The dependencies that arise from communication between processes are logged. Dependency logging and approximately consistent checkpoint sets are implemented on a network of SUN workstations running the V-System. Overhead on the message-passing primitives varies between 1% and 14% for dependency logging. Execution time overhead for a 200*200 Gaussian elimination is less than 4% and generates a dependency log of 288 kbytes.>