CyVerse: Cyberinfrastructure for open science.

CyVerse, the largest publicly-funded open-source research cyberinfrastructure for life sciences, has played a crucial role in advancing data-driven research since the 2010s. As the technology landscape evolved with the emergence of cloud computing platforms, machine learning and artificial intelligence (AI) applications, CyVerse has enabled access by providing interfaces, Software as a Service (SaaS), and cloud-native Infrastructure as Code (IaC) to leverage new technologies. CyVerse services enable researchers to integrate institutional and private computational resources, custom software, perform analyses, and publish data in accordance with open science principles. Over the past 13 years, CyVerse has registered more than 124,000 verified accounts from 160 countries and was used for over 1,600 peer-reviewed publications. Since 2011, 45,000 students and researchers have been trained to use CyVerse. The platform has been replicated and deployed in three countries outside the US, with additional private deployments on commercial clouds for US government agencies and multinational corporations. In this manuscript, we present a strategic blueprint for creating and managing SaaS cyberinfrastructure and IaC as free and open-source software.

Prediction of whole-cell transcriptional response with machine learning.

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.

VDJPipe: a pipelined tool for pre-processing immune repertoire sequencing data.

BACKGROUND: Pre-processing of high-throughput sequencing data for immune repertoire profiling is essential to insure high quality input for downstream analysis. VDJPipe is a flexible, high-performance tool that can perform multiple pre-processing tasks with just a single pass over the data files. RESULTS: Processing tasks provided by VDJPipe include base composition statistics calculation, read quality statistics calculation, quality filtering, homopolymer filtering, length and nucleotide filtering, paired-read merging, barcode demultiplexing, 5' and 3' PCR primer matching, and duplicate reads collapsing. VDJPipe utilizes a pipeline approach whereby multiple processing steps are performed in a sequential workflow, with the output of each step passed as input to the next step automatically. The workflow is flexible enough to handle the complex barcoding schemes used in many immunosequencing experiments. Because VDJPipe is designed for computational efficiency, we evaluated this by comparing execution times with those of pRESTO, a widely-used pre-processing tool for immune repertoire sequencing data. We found that VDJPipe requires <10% of the run time required by pRESTO. CONCLUSIONS: VDJPipe is a high-performance tool that is optimized for pre-processing large immune repertoire sequencing data sets.

VDJML: a file format with tools for capturing the results of inferring immune receptor rearrangements.

BACKGROUND: The genes that produce antibodies and the immune receptors expressed on lymphocytes are not germline encoded; rather, they are somatically generated in each developing lymphocyte by a process called V(D)J recombination, which assembles specific, independent gene segments into mature composite genes. The full set of composite genes in an individual at a single point in time is referred to as the immune repertoire. V(D)J recombination is the distinguishing feature of adaptive immunity and enables effective immune responses against an essentially infinite array of antigens. Characterization of immune repertoires is critical in both basic research and clinical contexts. Recent technological advances in repertoire profiling via high-throughput sequencing have resulted in an explosion of research activity in the field. This has been accompanied by a proliferation of software tools for analysis of repertoire sequencing data. Despite the widespread use of immune repertoire profiling and analysis software, there is currently no standardized format for output files from V(D)J analysis. Researchers utilize software such as IgBLAST and IMGT/High V-QUEST to perform V(D)J analysis and infer the structure of germline rearrangements. However, each of these software tools produces results in a different file format, and can annotate the same result using different labels. These differences make it challenging for users to perform additional downstream analyses. RESULTS: To help address this problem, we propose a standardized file format for representing V(D)J analysis results. The proposed format, VDJML, provides a common standardized format for different V(D)J analysis applications to facilitate downstream processing of the results in an application-agnostic manner. The VDJML file format specification is accompanied by a support library, written in C++ and Python, for reading and writing the VDJML file format. CONCLUSIONS: The VDJML suite will allow users to streamline their V(D)J analysis and facilitate the sharing of scientific knowledge within the community. The VDJML suite and documentation are available from https://vdjserver.org/vdjml/ . We welcome participation from the community in developing the file format standard, as well as code contributions.

Identification of a novel inhibitor of dengue virus protease through use of a virtual screening drug discovery Web portal.

We report the discovery of a novel small-molecule inhibitor of the dengue virus (DENV) protease (NS2B-NS3pro) using a newly constructed Web-based portal (DrugDiscovery@TACC) for structure-based virtual screening. Our drug discovery portal, an extension of virtual screening studies performed using IBM's World Community Grid, facilitated access to supercomputer resources managed by the Texas Advanced Computing Center (TACC) and enabled druglike commercially available small-molecule libraries to be rapidly screened against several high-resolution DENV NS2B-NS3pro crystallographic structures. Detailed analysis of virtual screening docking scores and hydrogen-bonding interactions between each docked ligand and the NS2B-NS3pro Ser135 side chain were used to select molecules for experimental validation. Compounds were ordered from established chemical companies, and compounds with established aqueous solubility were tested for their ability to inhibit DENV NS2B-NS3pro cleavage of a model substrate in kinetic studies. As a proof-of-concept, we validated a small-molecule dihydronaphthalenone hit as a single-digit-micromolar mixed noncompetitive inhibitor of the DENV protease. Since the dihydronaphthalenone was predicted to interact with NS2B-NS3pro residues that are largely conserved between DENV and the related West Nile virus (WNV), we tested this inhibitor against WNV NS2B-NS3pro and observed a similar mixed noncompetitive inhibition mechanism. However, the inhibition constants were ∼10-fold larger against the WNV protease relative to the DENV protease. This novel validated lead had no chemical features or pharmacophores associated with adverse toxicity, carcinogenicity, or mutagenicity risks and thus is attractive for additional characterization and optimization.

A Combined Molecular Dynamics and Experimental Study of Doped Polypyrrole.

Polypyrrole (PPy) is a biocompatible, electrically conductive polymer that has great potential for battery, sensor, and neural implant applications. Its amorphous structure and insolubility, however, limit the experimental techniques available to study its structure and properties at the atomic level. Previous theoretical studies of PPy in bulk are also scarce. Using ab initio calculations, we have constructed a molecular mechanics force field of chloride-doped PPy (PPyCl) and undoped PPy. This model has been designed to integrate into the OPLS force field, and parameters are available for the Gromacs and TINKER software packages. Molecular dynamics (MD) simulations of bulk PPy and PPyCl have been performed using this force field, and the effects of chain packing and electrostatic scaling on the bulk polymer density have been investigated. The density of flotation of PPyCl films has been measured experimentally. Amorphous X-ray diffraction of PPyCl was obtained and correlated with atomic structures sampled from MD simulations. The force field reported here is foundational for bridging the gap between experimental measurements and theoretical calculations for PPy based materials.

VDJServer: A Cloud-Based Analysis Portal and Data Commons for Immune Repertoire Sequences and Rearrangements.

Background: Recent technological advances in immune repertoire sequencing have created tremendous potential for advancing our understanding of adaptive immune response dynamics in various states of health and disease. Immune repertoire sequencing produces large, highly complex data sets, however, which require specialized methods and software tools for their effective analysis and interpretation. Results: VDJServer is a cloud-based analysis portal for immune repertoire sequence data that provide access to a suite of tools for a complete analysis workflow, including modules for preprocessing and quality control of sequence reads, V(D)J gene segment assignment, repertoire characterization, and repertoire comparison. VDJServer also provides sophisticated visualizations for exploratory analysis. It is accessible through a standard web browser via a graphical user interface designed for use by immunologists, clinicians, and bioinformatics researchers. VDJServer provides a data commons for public sharing of repertoire sequencing data, as well as private sharing of data between users. We describe the main functionality and architecture of VDJServer and demonstrate its capabilities with use cases from cancer immunology and autoimmunity. Conclusion: VDJServer provides a complete analysis suite for human and mouse T-cell and B-cell receptor repertoire sequencing data. The combination of its user-friendly interface and high-performance computing allows large immune repertoire sequencing projects to be analyzed with no programming or software installation required. VDJServer is a web-accessible cloud platform that provides access through a graphical user interface to a data management infrastructure, a collection of analysis tools covering all steps in an analysis, and an infrastructure for sharing data along with workflows, results, and computational provenance. VDJServer is a free, publicly available, and open-source licensed resource.

Leveraging the national cyberinfrastructure for biomedical research.

In the USA, the national cyberinfrastructure refers to a system of research supercomputer and other IT facilities and the high speed networks that connect them. These resources have been heavily leveraged by scientists in disciplines such as high energy physics, astronomy, and climatology, but until recently they have been little used by biomedical researchers. We suggest that many of the 'Big Data' challenges facing the medical informatics community can be efficiently handled using national-scale cyberinfrastructure. Resources such as the Extreme Science and Discovery Environment, the Open Science Grid, and Internet2 provide economical and proven infrastructures for Big Data challenges, but these resources can be difficult to approach. Specialized web portals, support centers, and virtual organizations can be constructed on these resources to meet defined computational challenges, specifically for genomics. We provide examples of how this has been done in basic biology as an illustration for the biomedical informatics community.

Biocompatibility implications of polypyrrole synthesis techniques.

Polypyrrole (PPy) is an inherently conducting polymer that has shown great promise for biomedical applications within the nervous system. However, to effectively use PPy as a biomaterial implant, it is important to understand and reproducibly control the electrical properties, physical topography and surface chemistry of the polymer. Although there is much research published on the use of PPy in various applications, there is no systematic study linking the methodologies used for PPy synthesis to PPy's basic polymeric properties (e.g., hydrophilicity, surface roughness), and to the biological effects these properties have on cells. Electrochemically synthesized PPy films differ greatly in their characteristics depending on synthesis parameters such as dopant, substrate and thickness, among other parameters. In these studies, we have used three dopants (chloride (Cl), tosylate (ToS), polystyrene sulfonate (PSS)), two substrates (gold and indium tin oxide-coated glass), and a range of thicknesses, to measure and compare the biomedically important characteristics of surface roughness, contact angle, conductivity, dopant stability and cell adhesion (using PC-12 cells and Schwann cells). As predicted, we discovered large differences in roughness depending on the dopant used and the thickness of the film, while substrate choice had little effect. From contact angle measurements, PSS was found to yield the most hydrophilic material, most likely because of free charges from the long PSS chains exposed on the surface of the PPy. ToS-doped PPy films were tenfold more conductive than Cl- or PSS-doped films. X-ray photoelectron spectroscopy studies were used to evaluate dopant concentrations of PPy films stored in water and phosphate buffered saline over 14 days, and conductance studies over the same timeframe measured electrical stability. PSS proved to be the most stable dopant, though all films experienced significant decay in conductivity and dopant concentration. Cell adhesion studies demonstrated the dependence of cell outcome on film thickness and dopant choice. The strengths and weaknesses of different synthesis parameters, as demonstrated by these experiments, are critical design factors that must be leveraged when designing biomedical implants. The results of these studies should provide practical insight to researchers working with conducting polymers, and particularly PPy, on the relationships between synthesis parameters, polymeric properties and biological compatibility.