The Tapis framework and APIs enable a wide range of research and scientific discovery by automating job execution on remote resources, including HPC clusters and servers running in the cloud. While Tapis simplifies interactions with cyberinfrastructure (CI), users still need to specify all details of a job they want to run, including the system, queue, node count, and maximum run time, among other attributes. Moreover, system and application resources must be defined in Tapis before a job is submitted. It can be a challenge for scientists to provide exact configurations and resource requirements for their jobs, which if incorrectly specified can delay their work.In this paper, we investigate developing an intelligent job scheduling capability in Tapis, where job configuration can be partially automated and computational resources can be dynamically provisioned by Tapis. We outline an architecture for such a feature and identify a set of core challenges to be solved. Then, we focus on one specific challenge: predicting queue times for a job on different HPC systems and queues, and we present two sets of results based on machine learning methods. Our first set casts the problem as a regression, which can be used to select the best system from a list of existing options. Our second set frames the problem as a classification, which allows us to compare the use of an existing system with a dynamically provisioned resource.

MOTIVATION: Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that maps response of single perturbations to transcriptional response of the combination of perturbations. RESULTS: The HRM combines high-throughput sequencing with machine learning to infer links between experimental context, prior knowledge of cell regulatory networks, and RNASeq data to predict a gene's dysregulation. We find that the HRM can predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). The model was validated in two organisms, Escherichia coli and Bacillus subtilis, using new experiments conducted after training. Finally, while the HRM is trained with gene expression data, the direct prediction of differential expression makes it possible to also conduct enrichment analyses using its predictions. We show that the HRM can accurately classify >95% of the pathway regulations. The HRM reduces the number of RNASeq experiments needed as responses can be tested in silico prior to the experiment. AVAILABILITY AND IMPLEMENTATION: The HRM software and tutorial are available at https://github.com/sd2e/CDM and the configurable differential expression analysis tools and tutorials are available at https://github.com/SD2E/omics_tools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Summary The explosion of IoT devices and sensors in recent years has led to a demand for efficiently storing, processing and analyzing time‐series data. Geoscience researchers use time‐series data stores such as Hydroserver, Virtual Observatory and Ecological Informatics System (VOEIS), and Cloud‐Hosted Real‐time Data Service (CHORDS). Many of these tools require a great deal of infrastructure to deploy and expertise to manage and scale. The Tapis framework, an NSF funded project, provides science as a service APIs to allow researchers to achieve faster scientific results, by eliminating the need to set up a complex infrastructure stack. The University of Hawai'i (UH) and Texas Advanced Computing Center (TACC) have collaborated to develop an open source Tapis Streams API that builds on the concepts of the CHORDS time series data service to support research. This new hosted service allows storing, processing, annotating, archiving, and querying time‐series data in the Tapis multi‐user and multi‐tenant collaborative platform. The Streams API provides a hosted production level middleware service that enables new data‐driven event workflows capabilities that may be leveraged by researchers and Tapis powered science gateways for handling spatially indexed time‐series datasets.

Summary Araport is an open‐source, online community resource for research on the Arabidopsis thaliana genome and related data. Araport is developed through a partnership between J. Craig Venter Institute, the Texas Advanced Computing Center at The University of Texas at Austin, and The University of Cambridge. Part of the open architecture of Araport is the Science Applications Workspace. Taking an ‘app store’ approach, users can choose applications developed both by the Araport team and community developers to create a customized environment for their work. Araport also provides tooling and support for developing applications for Araport, including an application generator, a rapid development and testing tool, and a straightforward deployment path for publishing applications into the Araport workspace. Copyright © 2015 John Wiley & Sons, Ltd.