We provide here an updated description of the REDfly (Regulatory Element Database for Fly) database of transcriptional regulatory elements, a unique resource that provides regulatory annotation for the genome of Drosophila and other insects. The genomic sequences regulating insect gene expression-transcriptional cis-regulatory modules (CRMs, e.g., "enhancers") and transcription factor binding sites (TFBSs)-are not currently curated by any other major database resources. However, knowledge of such sequences is important, as CRMs play critical roles with respect to disease as well as normal development, phenotypic variation, and evolution. Characterized CRMs also provide useful tools for both basic and applied research, including developing methods for insect control. REDfly, which is the most detailed existing platform for metazoan regulatory-element annotation, includes over 40,000 experimentally verified CRMs and TFBSs along with their DNA sequences, their associated genes, and the expression patterns they direct. Here, we briefly describe REDfly's contents and data model, with an emphasis on the new features implemented since 2020. We then provide an illustrated walk-through of several common REDfly search use cases.

ColdFront is an open source resource allocation management system, designed to provide a central portal for administration, reporting, and measuring scientific impact of cyberinfrastructure resources. It enables managing access to a diverse set of resource types across large groups of users and provides a rich set of extensible meta data for comprehensive reporting and integration with legacy systems. In this paper we introduce ColdFront, describe three main stakeholders, present the features and integrations currently implemented, and make the case for why ColdFront is vital to enabling research and reducing time-to-science. ColdFront is freely available at https://github.com/ubccr/coldfront.

As a part of a public cloud evaluation, we ran containerized application kernels on Google Cloud. Three different virtual machines (VM) were selected (Table 1): 1) general-purpose CPU with 8 cores, 2) Intel Cascade Lake generation CPU with AVX-512 and with 40 physical cores, and 3) AMD Zen-2 generation CPU with 112 physical cores. The first VM represents a reasonably sized machine for a permanent presence online. The other two represent high compute-capable resources. For the comparison, we use 8 core virtual machines from the Aristotle Cloud Federation (https://federatedcloud.org/) and several XSEDE HPC systems (Table 2).

The Center for Computational Research (CCR) at the University at Buffalo has developed Grendel: a fast, easy to use, bare metal provisioning system for High Performance Computing (HPC). Grendel simplifies network booting racks of compute nodes by providing a robust PXE boot server, rest API, and node management in a single binary for easy installation. In this paper, we describe CCR’s HPC network architecture and how Grendel was used to provision the center’s Linux based compute clusters. We also present some modern features built into Grendel including automatic host discovery, deploying Live OS images to bare metal compute nodes, and delivering kernel, initramfs, and other provisioning assets using access tokens and trusted HTTPS.

As part of the U.S. National Science Foundation (NSF) funded XD Metrics Service project, we are developing tools and techniques for the audit and analysis of High Performance Computing (HPC) and Cloud infrastructure. This includes a suite of tools for the analysis of HPC jobs, based on performance metrics collected from compute nodes. To date, we have developed two closely related utilities: XDMoD, which was designed to monitor usage and performance of NSF’s innovative HPC resources (known as XSEDE), and Open XDMoD, which was designed to monitor usage and performance in academic, governmental or commercial cyberinfrastructures. Considerable effort has been made to continually improve XDMoD, in order to capture the most important aspects of modern research computing.One area in which XDMoD is lacking is in tracking workflows, which are broadly designated as containing the elements of data transfer/input and one to many computational steps. As data sets have become larger, data movement has become more time and resource intensive, and hence more important to characterize. In addition, multiple step workflows, in which one input spawns a complex series of processes, are becoming more common. Although XDMoD currently captures some of the information required to properly track complex workflows, there are clearly some key data that are missing. In this paper, we discuss the existing state of workflow monitoring, and suggest strategies to improve on the information captured.

The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algorithms are trained and tested on different parts of this data set. We find that more than 94% of the test images can be correctly labeled, irrespective of their experimental origin. Because crystal recognition is key to high-density screening and the systematic analysis of crystallization experiments, this approach opens the door to both industrial and fundamental research applications.

Uridine insertion/deletion RNA editing is an essential process in kinetoplastid parasites whereby mitochondrial mRNAs are modified through the specific insertion and deletion of uridines to generate functional open reading frames, many of which encode components of the mitochondrial respiratory chain. The roles of numerous non-enzymatic editing factors have remained opaque given the limitations of conventional methods to interrogate the order and mechanism by which editing progresses and thus roles of individual proteins. Here, we examined whole populations of partially edited sequences using high throughput sequencing and a novel bioinformatic platform, the Trypanosome RNA Editing Alignment Tool (TREAT), to elucidate the roles of three proteins in the RNA Editing Mediator Complex (REMC). We determined that the factors examined function in the progression of editing through a gRNA; however, they have distinct roles and REMC is likely heterogeneous in composition. We provide the first evidence that editing can proceed through numerous paths within a single gRNA and that non-linear modifications are essential, generating commonly observed junction regions. Our data support a model in which RNA editing is executed via multiple paths that necessitate successive re-modification of junction regions facilitated, in part, by the REMC variant containing TbRGG2 and MRB8180.

Haptic interfaces have become common in consumer electronics. They enable easy interaction and information entry without the use of a mouse or keyboard. The work presented here illustrates the application of a haptic interface to crystallization screening in order to provide a natural means for visualizing and selecting results. By linking this to a cloud-based database and web-based application program interface, the same application shifts the approach from 'point and click' to 'touch and share', where results can be selected, annotated and discussed collaboratively. In the crystallographic application, given a suitable crystallization plate, beamline and robotic end effector, the resulting information can be used to close the loop between screening and X-ray analysis, allowing a direct and efficient 'screen to beam' approach. The application is not limited to the area of crystallization screening; 'touch and share' can be used by any information-rich scientific analysis and geographically distributed collaboration.

Uridine insertion/deletion RNA editing in kinetoplastids entails the addition and deletion of uridine residues throughout the length of mitochondrial transcripts to generate translatable mRNAs. This complex process requires the coordinated use of several multiprotein complexes as well as the sequential use of noncoding template RNAs called guide RNAs. The majority of steady-state mitochondrial mRNAs are partially edited and often contain regions of mis-editing, termed junctions, whose role is unclear. Here, we report a novel method for sequencing entire populations of pre-edited partially edited, and fully edited RNAs and analyzing editing characteristics across populations using a new bioinformatics tool, the Trypanosome RNA Editing Alignment Tool (TREAT). Using TREAT, we examined populations of two transcripts, RPS12 and ND7-5', in wild-typeTrypanosoma brucei We provide evidence that the majority of partially edited sequences contain junctions, that intrinsic pause sites arise during the progression of editing, and that the mechanisms that mediate pausing in the generation of canonical fully edited sequences are distinct from those that mediate the ends of junction regions. Furthermore, we identify alternatively edited sequences that constitute plausible alternative open reading frames and identify substantial variability in the 5' UTRs of both canonical and alternatively edited sequences. This work is the first to use high-throughput sequencing to examine full-length sequences of whole populations of partially edited transcripts. Our method is highly applicable to current questions in the RNA editing field, including defining mechanisms of action for editing factors and identifying potential alternatively edited sequences.

The important role high-performance computing HPC resources play in science and engineering research, coupled with its high cost capital, power and manpower, short life and oversubscription, requires us to optimize its usage - an outcome that is only possible if adequate analytical data are collected and used to drive systems management at different granularities - job, application, user and system. This paper presents a method for comprehensive job, application and system-level resource use measurement, and analysis and its implementation. The steps in the method are system-wide collection of comprehensive resource use and performance statistics at the job and node levels in a uniform format across all resources, mapping and storage of the resultant job-wise data to a relational database, which enables further implementation and transformation of the data to the formats required by specific statistical and analytical algorithms. Analyses can be carried out at different levels of granularity: job, user, application or system-wide. Measurements are based on a new lightweight job-centric measurement tool 'TACC_Stats', which gathers a comprehensive set of resource use metrics on all compute nodes and data logged by the system scheduler. The data mapping and analysis tools are an extension of the XDMoD project. The method is illustrated with analyses of resource use for the Texas Advanced Computing Center's Lonestar4, Ranger and Stampede supercomputers and the HPC cluster at the Center for Computational Research. The illustrations are focused on resource use at the system, job and application levels and reveal many interesting insights into system usage patterns and also anomalous behavior due to failure/misuse. The method can be applied to any system that runs the TACC_Stats measurement tool and a tool to extract job execution environment data from the system scheduler. Copyright © 2014 John Wiley & Sons, Ltd.

X-ray crystallography is the predominant method for obtaining atomic-scale information about biological macromolecules. Despite the success of the technique, obtaining well diffracting crystals still critically limits going from protein to structure. In practice, the crystallization process proceeds through knowledge-informed empiricism. Better physico-chemical understanding remains elusive because of the large number of variables involved, hence little guidance is available to systematically identify solution conditions that promote crystallization. To help determine relationships between macromolecular properties and their crystallization propensity, we have trained statistical models on samples for 182 proteins supplied by the Northeast Structural Genomics consortium. Gaussian processes, which capture trends beyond the reach of linear statistical models, distinguish between two main physico-chemical mechanisms driving crystallization. One is characterized by low levels of side chain entropy and has been extensively reported in the literature. The other identifies specific electrostatic interactions not previously described in the crystallization context. Because evidence for two distinct mechanisms can be gleaned both from crystal contacts and from solution conditions leading to successful crystallization, the model offers future avenues for optimizing crystallization screens based on partial structural information. The availability of crystallization data coupled with structural outcomes analyzed through state-of-the-art statistical models may thus guide macromolecular crystallization toward a more rational basis.

Many bioscience fields employ high-throughput methods to screen multiple biochemical conditions. The analysis of these becomes tedious without a degree of automation. Crystallization, a rate limiting step in biological X-ray crystallography, is one of these fields. Screening of multiple potential crystallization conditions (cocktails) is the most effective method of probing a proteins phase diagram and guiding crystallization but the interpretation of results can be consuming. To aid this empirical approach a cocktail distance coefficient was developed to quantitatively compare macromolecule crystallization conditions and outcome. These coefficients were evaluated against an existing similarity metric developed for crystallization, the C6 metric, using both virtual crystallization screens and by comparison of two related 1,536-cocktail high-throughput crystallization screens. Hierarchical clustering was employed to visualize one of these screens and the crystallization results from an exopolyphosphatase-related protein from Bacteroides fragilis, (BfR192) overlaid on this clustering. This demonstrated a strong correlation between certain chemically related clusters and crystal lead conditions. While this analysis was not used to guide the initial crystallization optimization, it led to the re-evaluation of unexplained peaks in the electron density map of the protein and the insertion and correct placement of a sodium, potassium and phosphate atoms in the structure. With these in place, the resulting structure of the putative active site demonstrated features consistent with active sites of other phosphatases which are involved in binding the phosphoryl moieties of nucleotide triphosphates. The new distance coefficient appears to be robust in this application and coupled with hierarchical clustering and the overlay of crystallization outcome reveals information of biological relevance. While tested with a single example the potential applications appear promising.

Many bioscience fields employ high-throughput methods to screen multiple biochemical conditions. The analysis of these becomes tedious without a degree of automation. Crystallization, a rate limiting step in biological X-ray crystallography, is one of these fields. Screening of multiple potential crystallization conditions (cocktails) is the most effective method of probing a proteins phase diagram and guiding crystallization but the interpretation of results can be time-consuming. To aid this empirical approach a cocktail distance coefficient was developed to quantitatively compare macromolecule crystallization conditions and outcome. These coefficients were evaluated against an existing similarity metric developed for crystallization, the C6 metric, using both virtual crystallization screens and by comparison of two related 1,536-cocktail high-throughput crystallization screens. Hierarchical clustering was employed to visualize one of these screens and the crystallization results from an exopolyphosphatase-related protein from Bacteroides fragilis, (BfR192) overlaid on this clustering. This demonstrated a strong correlation between certain chemically related clusters and crystal lead conditions. While this analysis was not used to guide the initial crystallization optimization, it led to the re-evaluation of unexplained peaks in the electron density map of the protein and to the insertion and correct placement of sodium, potassium and phosphate atoms in the structure. With these in place, the resulting structure of the putative active site demonstrated features consistent with active sites of other phosphatases which are involved in binding the phosphoryl moieties of nucleotide triphosphates. The new distance coefficient, CDcoeff, appears to be robust in this application, and coupled with hierarchical clustering and the overlay of crystallization outcome, reveals information of biological relevance. While tested with a single example the potential applications related to crystallography appear promising and the distance coefficient, clustering, and hierarchal visualization of results undoubtedly have applications in wider fields.

Introduction. The microarray datasets from the MicroArray Quality Control (MAQC) project have enabled the assessment of the precision, comparability of microarrays, and other various microarray analysis methods. However, to date no studies that we are aware of have reported the performance of missing value imputation schemes on the MAQC datasets. In this study, we use the MAQC Affymetrix datasets to evaluate several imputation procedures in Affymetrix microarrays. Results. We evaluated several cutting edge imputation procedures and compared them using different error measures. We randomly deleted 5% and 10% of the data and imputed the missing values using imputation tests. We performed 1000 simulations and averaged the results. The results for both 5% and 10% deletion are similar. Among the imputation methods, we observe the local least squares method with k = 4 is most accurate under the error measures considered. The k-nearest neighbor method with k = 1 has the highest error rate among imputation methods and error measures. Conclusions. We conclude for imputing missing values in Affymetrix microarray datasets, using the MAS 5.0 preprocessing scheme, the local least squares method with k = 4 has the best overall performance and k-nearest neighbor method with k = 1 has the worst overall performance. These results hold true for both 5% and 10% missing values.

This paper describes a comprehensive auditing framework, XDMoD, for use by high performance computing centers to readily provide metrics regarding resource utilization (CPU hours, job size, wait time, etc), resource performance, and the center's impact in terms of scholarship and research. This role-based auditing framework is designed to meet the following objectives: (1) provide the user community with an easy to use tool to oversee their allocations and optimize their use of resources, (2) provide staff with easy access to performance metrics and diagnostics to monitor and tune resource performance for the benefit of the users, (3) provide senior management with a tool to easily monitor utilization, user base, and performance of resources, and (4) help ensure that the resources are effectively enabling research and scholarship. XDMoD is initially focused on the NSF TeraGrid (TG) and follow-on XSEDE (XD) program, where it will become a key component of the TG/XSEDE User Portal. However, this auditing system is intended to have a general applicability to any HPC system or center. The XDMoD auditing system is architected using a set of modular components that facilitate the utilization of community contributed components information. It includes an active and reactive (as opposed to passive) service set accessible through a variety of endpoints such as web-based user interface, RESTful web services, and provided development tools. One component also provides a computationally lightweight and flexible application kernel auditing system that reflects best-in-class performance kernels to measure overall system performance with respect to existing applications that are actually being run by users. This allows continuous resource auditing to monitor all aspects of system performance, most critically from a completely user-centric point of view.

In this paper we describe a comprehensive strategy of modelling, sensing, and using energy-aware technologies to improve the operational efficiency of a high-performance computing (HPC) data center. Modelling was performed using state of the art computational fluid dynamics (CFD) for optimizing air flow and air handler placement. Also included were the installation of a complete environmental monitoring system to provide the real-time tools needed to monitor operating conditions throughout the data center and utilize this information to calibrate the CFD models and realize energy savings. This system was used to implement subfloor leak management, isolation of unneeded regions, perforated tile optimization, and bypass airflow management to maximize the efficiency of the center's cooling infrastructure without adding additional power. In addition, given the high-density of servers in a typical HPC data center rack, rack-based chillers that more efficiently deliver cooling to the servers were installed, allowing several of the large data center air handling units to be shut down. These changes, along with implementing a policy of energy awareness in major and minor equipment purchases, allowed us to achieve a remarkable ten-fold increase in computational capacity while simultaneously reducing the total energy consumption by more than 20%. The strategies and techniques utilized here are readily transferable to other existing data centers.

BACKGROUND:Gene fusions are the result of chromosomal aberrations and encode chimeric RNA (fusion transcripts) that play an important role in cancer genesis. Recent advances in high throughput transcriptome sequencing have given rise to computational methods for new fusion discovery. The ability to simulate fusion transcripts is essential for testing and improving those tools.RESULTS:To facilitate this need, we developed FUSIM (FUsion SIMulator), a software tool for simulating fusion transcripts. The simulation of events known to create fusion genes and their resulting chimeric proteins is supported, including inter-chromosome translocation, trans-splicing, complex chromosomal rearrangements, and transcriptional read through events.CONCLUSIONS:FUSIM provides the ability to assemble a dataset of fusion transcripts useful for testing and benchmarking applications in fusion gene discovery.

BACKGROUND:Single nucleotide polymorphisms (SNPs) can lead to the susceptibility and onset of diseases through their effects on gene expression at the posttranscriptional level. Recent findings indicate that SNPs could create, destroy, or modify the efficiency of miRNA binding to the 3'UTR of a gene, resulting in gene dysregulation. With the rapidly growing number of published disease-associated SNPs (dSNPs), there is a strong need for resources specifically recording dSNPs on the 3'UTRs and their nucleotide distance from miRNA target sites. We present here miRdSNP, a database incorporating three important areas of dSNPs, miRNA target sites, and diseases.DESCRIPTION:miRdSNP provides a unique database of dSNPs on the 3'UTRs of human genes manually curated from PubMed. The current release includes 786 dSNP-disease associations for 630 unique dSNPs and 204 disease types. miRdSNP annotates genes with experimentally confirmed targeting by miRNAs and indexes miRNA target sites predicted by TargetScan and PicTar as well as potential miRNA target sites newly generated by dSNPs. A robust web interface and search tools are provided for studying the proximity of miRNA binding sites to dSNPs in relation to human diseases. Searches can be dynamically filtered by gene name, miRBase ID, target prediction algorithm, disease, and any nucleotide distance between dSNPs and miRNA target sites. Results can be viewed at the sequence level showing the annotated locations for miRNA target sites and dSNPs on the entire 3'UTR sequences. The integration of dSNPs with the UCSC Genome browser is also supported.CONCLUSION:miRdSNP provides a comprehensive data source of dSNPs and robust tools for exploring their distance from miRNA target sites on the 3'UTRs of human genes. miRdSNP enables researchers to further explore the molecular mechanism of gene dysregulation for dSNPs at posttranscriptional level. miRdSNP is freely available on the web at http://mirdsnp.ccr.buffalo.edu.