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.

Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop.

Computational tools addressing various components of design-build-test-learn (DBTL) loops for the construction of synthetic genetic networks exist but do not generally cover the entire DBTL loop. This manuscript introduces an end-to-end sequence of tools that together form a DBTL loop called Design Assemble Round Trip (DART). DART provides rational selection and refinement of genetic parts to construct and test a circuit. Computational support for experimental process, metadata management, standardized data collection and reproducible data analysis is provided via the previously published Round Trip (RT) test-learn loop. The primary focus of this work is on the Design Assemble (DA) part of the tool chain, which improves on previous techniques by screening up to thousands of network topologies for robust performance using a novel robustness score derived from dynamical behavior based on circuit topology only. In addition, novel experimental support software is introduced for the assembly of genetic circuits. A complete design-through-analysis sequence is presented using several OR and NOR circuit designs, with and without structural redundancy, that are implemented in budding yeast. The execution of DART tested the predictions of the design tools, specifically with regard to robust and reproducible performance under different experimental conditions. The data analysis depended on a novel application of machine learning techniques to segment bimodal flow cytometry distributions. Evidence is presented that, in some cases, a more complex build may impart more robustness and reproducibility across experimental conditions. Graphical Abstract.

Highly-automated, high-throughput replication of yeast-based logic circuit design assessments.

We describe an experimental campaign that replicated the performance assessment of logic gates engineered into cells of Saccharomyces cerevisiae by Gander et al. Our experimental campaign used a novel high-throughput experimentation framework developed under Defense Advanced Research Projects Agency's Synergistic Discovery and Design program: a remote robotic lab at Strateos executed a parameterized experimental protocol. Using this protocol and robotic execution, we generated two orders of magnitude more flow cytometry data than the original experiments. We discuss our results, which largely, but not completely, agree with the original report and make some remarks about lessons learned. Graphical Abstract.

A toolkit for enhanced reproducibility of RNASeq analysis for synthetic biologists.

Sequencing technologies, in particular RNASeq, have become critical tools in the design, build, test and learn cycle of synthetic biology. They provide a better understanding of synthetic designs, and they help identify ways to improve and select designs. While these data are beneficial to design, their collection and analysis is a complex, multistep process that has implications on both discovery and reproducibility of experiments. Additionally, tool parameters, experimental metadata, normalization of data and standardization of file formats present challenges that are computationally intensive. This calls for high-throughput pipelines expressly designed to handle the combinatorial and longitudinal nature of synthetic biology. In this paper, we present a pipeline to maximize the analytical reproducibility of RNASeq for synthetic biologists. We also explore the impact of reproducibility on the validation of machine learning models. We present the design of a pipeline that combines traditional RNASeq data processing tools with structured metadata tracking to allow for the exploration of the combinatorial design in a high-throughput and reproducible manner. We then demonstrate utility via two different experiments: a control comparison experiment and a machine learning model experiment. The first experiment compares datasets collected from identical biological controls across multiple days for two different organisms. It shows that a reproducible experimental protocol for one organism does not guarantee reproducibility in another. The second experiment quantifies the differences in experimental runs from multiple perspectives. It shows that the lack of reproducibility from these different perspectives can place an upper bound on the validation of machine learning models trained on RNASeq data. Graphical Abstract.

Large-scale design and refinement of stable proteins using sequence-only models.

Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we use a high-throughput, low-fidelity assay to experimentally evaluate the stability of approximately 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We build a neural network model that predicts protein stability given only sequences of amino acids, and compare its performance to the assayed values. We also report another network model that is able to generate the amino acid sequences of novel stable proteins given requested secondary sequences. Finally, we show that the predictive model-despite weaknesses including a noisy data set-can be used to substantially increase the stability of both expert-designed and model-generated proteins.

Round Trip: An Automated Pipeline for Experimental Design, Execution, and Analysis.

Synthetic biology is a complex discipline that involves creating detailed, purpose-built designs from genetic parts. This process is often phrased as a Design-Build-Test-Learn loop, where iterative design improvements can be made, implemented, measured, and analyzed. Automation can potentially improve both the end-to-end duration of the process and the utility of data produced by the process. One of the most important considerations for the development of effective automation and quality data is a rigorous description of implicit knowledge encoded as a formal knowledge representation. The development of knowledge representation for the process poses a number of challenges, including developing effective human-machine interfaces, protecting against and repairing user error, providing flexibility for terminological mismatches, and supporting extensibility to new experimental types. We address these challenges with the DARPA SD2 Round Trip software architecture. The Round Trip is an open architecture that automates many of the key steps in the Test and Learn phases of a Design-Build-Test-Learn loop for high-throughput laboratory science. The primary contribution of the Round Trip is to assist with and otherwise automate metadata creation, curation, standardization, and linkage with experimental data. The Round Trip's focus on metadata supports fast, automated, and replicable analysis of experiments as well as experimental situational awareness and experimental interpretability. We highlight the major software components and data representations that enable the Round Trip to speed up the design and analysis of experiments by 2 orders of magnitude over prior ad hoc methods. These contributions support a number of experimental protocols and experimental types, demonstrating the Round Trip's breadth and extensibility. We describe both an illustrative use case using the Round Trip for an on-the-loop experimental campaign and overall contributions to reducing experimental analysis time and increasing data product volume in the SD2 program.

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.

iVirus 2.0: Cyberinfrastructure-supported tools and data to power DNA virus ecology.

Microbes drive myriad ecosystem processes, but under strong influence from viruses. Because studying viruses in complex systems requires different tools than those for microbes, they remain underexplored. To combat this, we previously aggregated double-stranded DNA (dsDNA) virus analysis capabilities and resources into 'iVirus' on the CyVerse collaborative cyberinfrastructure. Here we substantially expand iVirus's functionality and accessibility, to iVirus 2.0, as follows. First, core iVirus apps were integrated into the Department of Energy's Systems Biology KnowledgeBase (KBase) to provide an additional analytical platform. Second, at CyVerse, 20 software tools (apps) were upgraded or added as new tools and capabilities. Third, nearly 20-fold more sequence reads were aggregated to capture new data and environments. Finally, documentation, as "live" protocols, was updated to maximize user interaction with and contribution to infrastructure development. Together, iVirus 2.0 serves as a uniquely central and accessible analytical platform for studying how viruses, particularly dsDNA viruses, impact diverse microbial ecosystems.

Comparing DNA replication programs reveals large timing shifts at centromeres of endocycling cells in maize roots.

Plant cells undergo two types of cell cycles-the mitotic cycle in which DNA replication is coupled to mitosis, and the endocycle in which DNA replication occurs in the absence of cell division. To investigate DNA replication programs in these two types of cell cycles, we pulse labeled intact root tips of maize (Zea mays) with 5-ethynyl-2'-deoxyuridine (EdU) and used flow sorting of nuclei to examine DNA replication timing (RT) during the transition from a mitotic cycle to an endocycle. Comparison of the sequence-based RT profiles showed that most regions of the maize genome replicate at the same time during S phase in mitotic and endocycling cells, despite the need to replicate twice as much DNA in the endocycle and the fact that endocycling is typically associated with cell differentiation. However, regions collectively corresponding to 2% of the genome displayed significant changes in timing between the two types of cell cycles. The majority of these regions are small with a median size of 135 kb, shift to a later RT in the endocycle, and are enriched for genes expressed in the root tip. We found larger regions that shifted RT in centromeres of seven of the ten maize chromosomes. These regions covered the majority of the previously defined functional centromere, which ranged between 1 and 2 Mb in size in the reference genome. They replicate mainly during mid S phase in mitotic cells but primarily in late S phase of the endocycle. In contrast, the immediately adjacent pericentromere sequences are primarily late replicating in both cell cycles. Analysis of CENH3 enrichment levels in 8C vs 2C nuclei suggested that there is only a partial replacement of CENH3 nucleosomes after endocycle replication is complete. The shift to later replication of centromeres and possible reduction in CENH3 enrichment after endocycle replication is consistent with a hypothesis that centromeres are inactivated when their function is no longer needed.

Arabidopsis DNA Replication Initiates in Intergenic, AT-Rich Open Chromatin.

The selection and firing of DNA replication origins play key roles in ensuring that eukaryotes accurately replicate their genomes. This process is not well documented in plants due in large measure to difficulties in working with plant systems. We developed a new functional assay to label and map very early replicating loci that must, by definition, include at least a subset of replication origins. Arabidopsis (Arabidopsis thaliana) cells were briefly labeled with 5-ethynyl-2'-deoxy-uridine, and nuclei were subjected to two-parameter flow sorting. We identified more than 5500 loci as initiation regions (IRs), the first regions to replicate in very early S phase. These were classified as strong or weak IRs based on the strength of their replication signals. Strong initiation regions were evenly spaced along chromosomal arms and depleted in centromeres, while weak initiation regions were enriched in centromeric regions. IRs are AT-rich sequences flanked by more GC-rich regions and located predominantly in intergenic regions. Nuclease sensitivity assays indicated that IRs are associated with accessible chromatin. Based on these observations, initiation of plant DNA replication shows some similarity to, but is also distinct from, initiation in other well-studied eukaryotic systems.

An 'eFP-Seq Browser' for visualizing and exploring RNA sequencing data.

Improvements in next-generation sequencing technologies have resulted in dramatically reduced sequencing costs. This has led to an explosion of '-seq'-based methods, of which RNA sequencing (RNA-seq) for generating transcriptomic data is the most popular. By analysing global patterns of gene expression in organs/tissues/cells of interest or in response to chemical or environmental perturbations, researchers can better understand an organism's biology. Tools designed to work with large RNA-seq data sets enable analyses and visualizations to help generate hypotheses about a gene's function. We present here a user-friendly RNA-seq data exploration tool, called the 'eFP-Seq Browser', that shows the read map coverage of a gene of interest in each of the samples along with 'electronic fluorescent pictographic' (eFP) images that serve as visual representations of expression levels. The tool also summarizes the details of each RNA-seq experiment, providing links to archival databases and publications. It automatically computes the reads per kilobase per million reads mapped expression-level summaries and point biserial correlation scores to sort the samples based on a gene's expression level or by how dissimilar the read map profile is from a gene splice variant, to quickly identify samples with the strongest expression level or where alternative splicing might be occurring. Links to the Integrated Genome Browser desktop visualization tool allow researchers to visualize and explore the details of RNA-seq alignments summarized in eFP-Seq Browser as coverage graphs. We present four cases of use of the eFP-Seq Browser for ABI3, SR34, SR45a and U2AF65B, where we examine expression levels and identify alternative splicing. The URL for the browser is https://bar.utoronto.ca/eFP-Seq_Browser/. OPEN RESEARCH BADGES: This article has earned an Open Data Badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. Tool is at https://bar.utoronto.ca/eFP-Seq_Browser/; RNA-seq data at https://s3.amazonaws.com/iplant-cdn/iplant/home/araport/rnaseq_bam/ and https://s3.amazonaws.com/iplant-cdn/iplant/home/araport/rnaseq_bam/Klepikova/. Code is available at https://github.com/BioAnalyticResource/eFP-Seq-Browser.

Subtle Perturbations of the Maize Methylome Reveal Genes and Transposons Silenced by Chromomethylase or RNA-Directed DNA Methylation Pathways.

DNA methylation is a chromatin modification that can provide epigenetic regulation of gene and transposon expression. Plants utilize several pathways to establish and maintain DNA methylation in specific sequence contexts. The chromomethylase (CMT) genes maintain CHG (where H = A, C or T) methylation. The RNA-directed DNA methylation (RdDM) pathway is important for CHH methylation. Transcriptome analysis was performed in a collection of Zea mays lines carrying mutant alleles for CMT or RdDM-associated genes. While the majority of the transcriptome was not affected, we identified sets of genes and transposon families sensitive to context-specific decreases in DNA methylation in mutant lines. Many of the genes that are up-regulated in CMT mutant lines have high levels of CHG methylation, while genes that are differentially expressed in RdDM mutants are enriched for having nearby mCHH islands, implicating context-specific DNA methylation in the regulation of expression for a small number of genes. Many genes regulated by CMTs exhibit natural variation for DNA methylation and transcript abundance in a panel of diverse inbred lines. Transposon families with differential expression in the mutant genotypes show few defining features, though several families up-regulated in RdDM mutants show enriched expression in endosperm tissue, highlighting the potential importance for this pathway during reproduction. Taken together, our findings suggest that while the number of genes and transposon families whose expression is reproducibly affected by mild perturbations in context-specific methylation is small, there are distinct patterns for loci impacted by RdDM and CMT mutants.

Platform for Automated Real-Time High Performance Analytics on Medical Image Data.

Biomedical data are quickly growing in volume and in variety, providing clinicians an opportunity for better clinical decision support. Here, we demonstrate a robust platform that uses software automation and high performance computing (HPC) resources to achieve real-time analytics of clinical data, specifically magnetic resonance imaging (MRI) data. We used the Agave application programming interface to facilitate communication, data transfer, and job control between an MRI scanner and an off-site HPC resource. In this use case, Agave executed the graphical pipeline tool GRAphical Pipeline Environment (GRAPE) to perform automated, real-time, quantitative analysis of MRI scans. Same-session image processing will open the door for adaptive scanning and real-time quality control, potentially accelerating the discovery of pathologies and minimizing patient callbacks. We envision this platform can be adapted to other medical instruments, HPC resources, and analytics tools.

Genome-Wide Analysis of the Arabidopsis Replication Timing Program.

Eukaryotes use a temporally regulated process, known as the replication timing program, to ensure that their genomes are fully and accurately duplicated during S phase. Replication timing programs are predictive of genomic features and activity and are considered to be functional readouts of chromatin organization. Although replication timing programs have been described for yeast and animal systems, much less is known about the temporal regulation of plant DNA replication or its relationship to genome sequence and chromatin structure. We used the thymidine analog, 5-ethynyl-2'-deoxyuridine, in combination with flow sorting and Repli-Seq to describe, at high-resolution, the genome-wide replication timing program for Arabidopsis (Arabidopsis thaliana) Col-0 suspension cells. We identified genomic regions that replicate predominantly during early, mid, and late S phase, and correlated these regions with genomic features and with data for chromatin state, accessibility, and long-distance interaction. Arabidopsis chromosome arms tend to replicate early while pericentromeric regions replicate late. Early and mid-replicating regions are gene-rich and predominantly euchromatic, while late regions are rich in transposable elements and primarily heterochromatic. However, the distribution of chromatin states across the different times is complex, with each replication time corresponding to a mixture of states. Early and mid-replicating sequences interact with each other and not with late sequences, but early regions are more accessible than mid regions. The replication timing program in Arabidopsis reflects a bipartite genomic organization with early/mid-replicating regions and late regions forming separate, noninteracting compartments. The temporal order of DNA replication within the early/mid compartment may be modulated largely by chromatin accessibility.

Repliscan: a tool for classifying replication timing regions.

BACKGROUND: Replication timing experiments that use label incorporation and high throughput sequencing produce peaked data similar to ChIP-Seq experiments. However, the differences in experimental design, coverage density, and possible results make traditional ChIP-Seq analysis methods inappropriate for use with replication timing. RESULTS: To accurately detect and classify regions of replication across the genome, we present Repliscan. Repliscan robustly normalizes, automatically removes outlying and uninformative data points, and classifies Repli-seq signals into discrete combinations of replication signatures. The quality control steps and self-fitting methods make Repliscan generally applicable and more robust than previous methods that classify regions based on thresholds. CONCLUSIONS: Repliscan is simple and effective to use on organisms with different genome sizes. Even with analysis window sizes as small as 1 kilobase, reliable profiles can be generated with as little as 2.4x coverage.

Genomic Analysis of the DNA Replication Timing Program during Mitotic S Phase in Maize (Zea mays) Root Tips.

All plants and animals must replicate their DNA, using a regulated process to ensure that their genomes are completely and accurately replicated. DNA replication timing programs have been extensively studied in yeast and animal systems, but much less is known about the replication programs of plants. We report a novel adaptation of the "Repli-seq" assay for use in intact root tips of maize (Zea mays) that includes several different cell lineages and present whole-genome replication timing profiles from cells in early, mid, and late S phase of the mitotic cell cycle. Maize root tips have a complex replication timing program, including regions of distinct early, mid, and late S replication that each constitute between 20 and 24% of the genome, as well as other loci corresponding to ∼32% of the genome that exhibit replication activity in two different time windows. Analyses of genomic, transcriptional, and chromatin features of the euchromatic portion of the maize genome provide evidence for a gradient of early replicating, open chromatin that transitions gradually to less open and less transcriptionally active chromatin replicating in mid S phase. Our genomic level analysis also demonstrated that the centromere core replicates in mid S, before heavily compacted classical heterochromatin, including pericentromeres and knobs, which replicate during late S phase.

ThaleMine: A Warehouse for Arabidopsis Data Integration and Discovery.

ThaleMine (https://apps.araport.org/thalemine/) is a comprehensive data warehouse that integrates a wide array of genomic information of the model plant Arabidopsis thaliana. The data collection currently includes the latest structural and functional annotation from the Araport11 update, the Col-0 genome sequence, RNA-seq and array expression, co-expression, protein interactions, homologs, pathways, publications, alleles, germplasm and phenotypes. The data are collected from a wide variety of public resources. Users can browse gene-specific data through Gene Report pages, identify and create gene lists based on experiments or indexed keywords, and run GO enrichment analysis to investigate the biological significance of selected gene sets. Developed by the Arabidopsis Information Portal project (Araport, https://www.araport.org/), ThaleMine uses the InterMine software framework, which builds well-structured data, and provides powerful data query and analysis functionality. The warehoused data can be accessed by users via graphical interfaces, as well as programmatically via web-services. Here we describe recent developments in ThaleMine including new features and extensions, and discuss future improvements. InterMine has been broadly adopted by the model organism research community including nematode, rat, mouse, zebrafish, budding yeast, the modENCODE project, as well as being used for human data. ThaleMine is the first InterMine developed for a plant model. As additional new plant InterMines are developed by the legume and other plant research communities, the potential of cross-organism integrative data analysis will be further enabled.

Premeditation and Sensation Seeking Moderate the Reasoned Action and Social Reaction Pathways in the Prototype/Willingness Model of Alcohol Use.

BACKGROUND: Drinking can occur because of expectations to drink (reasoned pathway) or because of willingness to drink under certain circumstances (reactive pathway). These pathways are thought to be influenced by different cognitions such as alcohol-related attitudes, norms, or drinking prototypes (Gerrard et al., 2008). Impulsive traits reflect individual differences in the influence of reasoned or reactive factors, however little research has investigated whether impulsivity moderates the effects of cognitive factors predicting alcohol use. OBJECTIVES: We tested whether differences in three impulsivity traits (premeditation, sensation seeking and negative urgency) moderated associations of reasoned (risk/disapproval attitudes and social norms) and reactive (prototype) pathway variables on expectation/willingness to drink and recent alcohol use. METHODS: We collected data from n = 409 college students; the sample was 67% female, 43% Asian American, with Mdnage = 19. Hypotheses were tested using multiple regression. RESULTS: Premeditation and sensation seeking moderated reasoned variable effects on expectation and drinking. Among those low on premeditation, risk attitudes were most associated with drinking expectation, with alcohol prototypes most related to recent drinking. These effects declined at higher premeditation levels. Among those high on sensation seeking, risk attitudes were most associated with expectation and drinking, declining at lower sensation seeking levels. There was little evidence of moderation predicting drinking willingness. CONCLUSIONS/IMPORTANCE: Findings imply personality differences may explain association strength between reasoned but not reactive risk behavior pathways with alcohol outcomes. They have ramifications for personalized prevention programs to reduce drinking through cognition change, as alcohol-related cognition influence may differ depending on personality characteristics.

Feeding Neonates by Cup: A Systematic Review of the Literature.

Objective WHO and UNICEF recommend cup feeding for neonates unable to breastfeed in low-resource settings. In developed countries, cup feeding in lieu of bottle feeding in the neonatal period is hypothesized to improve breastfeeding outcomes for those initially unable to breastfeed. Our aim was to synthesize the entire body of evidence on cup feeding. Methods We searched domestic and international databases for original research. Our search criteria required original data on cup feeding in neonates published in English between January 1990 and December 2014. Results We identified 28 original research papers. Ten were randomized clinical trials, 7 non-randomized intervention studies, and 11 observational studies; 11 were conducted in developing country. Outcomes evaluated included physiologic stability, safety, intake, duration, spillage, weight gain, any and exclusive breastfeeding, length of hospital stay, compliance, and acceptability. Cup feeding appears to be safe though intake may be less and spillage greater relative to bottle or tube feeding. Overall, slightly higher proportions of cup fed versus bottle fed infants report any breastfeeding; a greater proportion of cup fed infants reported exclusive breastfeeding at discharge and beyond. Cup feeding increases breastfeeding in subgroups (e.g. those who intend to breastfeed or women who had a Caesarean section). Compliance and acceptability is problematic in certain settings. Conclusions Further research on long-term breastfeeding outcomes and in low-resource settings would be helpful. Research data on high risk infants (e.g. those with cleft palates) would be informative. Innovative cup feeding approaches to minimize spillage, optimize compliance, and increase breastfeeding feeding are needed.