Parsl

Babuji, Yadu; Woodard, A.; Li, Zhuozhao; Katz, Daniel S.; Clifford, Ben; Kumar, Rohan; Łaciński, Łukasz; Chard, Ryan; Wozniak, Justin M.; Foster, Ian; Wilde, Michael; Chard, Kyle

doi:10.1145/3307681.3325400

Cited by 162 publications

(21 citation statements)

References 19 publications

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“…The first phase of the pipeline is integrated with the Advanced Photon Source (APS) Data Management System at the beamline, which deposits each newly acquired image into a Globus-accessible storage system at the APS. As new images are acquired, Globus Automate flows are launched to process them as follows: 1) moves new files from APS to Theta by using the Globus Transfer service ( 35 ); 2) performs DIALS stills_process ( 36 ) on batches of 256 images by using funcX ( 37 ), a function-as-a-service computation system [funcX uses Parsl ( 38 ) to abstract and acquire nodes on Theta as needed, and dispatches tasks to available nodes]; 3) extracts metadata from files regarding identified diffractions and generates visualizations (funcX) showing the locations of positive hits on the mesh; and 4) publishes raw data, metadata, and visualizations to a portal on the Argonne Leadership Computing Facility (ALCF) Petrel data system ( 39 ). The result of this automated process is an indexed, searchable data collection that provides full traceability from data acquisition to processed data that can be used to inspect and update the running experiment.…”

Section: Methodsmentioning

confidence: 99%

2′-O methylation of RNA cap in SARS-CoV-2 captured by serial crystallography

Wilamowski

Sherrell

Minasov

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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The genome of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) coronavirus has a capping modification at the 5′-untranslated region (UTR) to prevent its degradation by host nucleases. These modifications are performed by the Nsp10/14 and Nsp10/16 heterodimers using S-adenosylmethionine as the methyl donor. Nsp10/16 heterodimer is responsible for the methylation at the ribose 2′-O position of the first nucleotide. To investigate the conformational changes of the complex during 2′-O methyltransferase activity, we used a fixed-target serial synchrotron crystallography method at room temperature. We determined crystal structures of Nsp10/16 with substrates and products that revealed the states before and after methylation, occurring within the crystals during the experiments. Here we report the crystal structure of Nsp10/16 in complex with Cap-1 analog (m7GpppAm2′-O). Inhibition of Nsp16 activity may reduce viral proliferation, making this protein an attractive drug target.

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Section: Methodsmentioning

confidence: 99%

2′-O methylation of RNA cap in SARS-CoV-2 captured by serial crystallography

Wilamowski

Sherrell

Minasov

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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“…It provides an easy entry to cloud-based computing for biomolecular simulation scientists. Crossbow shares many of its design aspects with Parsl [23]. It provides tools to wrap Python functions and external applications (e.g., legacy MD simulation codes), in such a way that they can be combined into workflows using a task-based paradigm.…”

Section: Related Workmentioning

confidence: 99%

Enabling the execution of large scale workflows for molecular dynamics simulations

Andrio¹,

Ramon-Cortes²,

Conejero³

et al. 2021

Preprint

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The usage of workflows has led to progress in many fields of science, where the need to process large amounts of data is coupled with difficulty in accessing and efficiently using High Performance Computing platforms. On the one hand, scientists are focused on their problem and concerned with how to process their data. On top of that, the applications typically have different parts and use different tools for each part, thus complicating the distribution and the reproducibility of the simulations. On the other hand, computer scientists concentrate on how to develop frameworks for the deployment of workflows on HPC or HTC resources; often providing separate solutions for the computational aspects and the data analytic ones. In this paper we present an approach to support biomolecular researchers in the development of complex workflows that i) allow them to compose pipelines of individual simulations built from different tools and interconnected by data dependencies, ii) run them seamlessly on different computational platforms, and iii) scale them up to the large number of cores provided by modern supercomputing infrastructures. Our approach is based on the orchestration of computational building blocks for Molecular Dynamics simulations through an efficient workflow management system that has already been adopted in many scientific fields to run applications on multitudes of computing backends. Results demonstrate the validity of the proposed solution through the execution of massively parallel runs in a supercom- puter facility.

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“…Furthermore, there are usually multiple computational activities in bioinformatics analyses including filtering, normalization, and annotation. Efforts to ensure reproducibility (Cohen-Boulakia et al, 2017) of these analyses involve (but are not limited to) task composition tools (scripts (Babuji et al, 2019), pipelines, scientific workflows (Liew et al, 2016), and software containers (Boettiger, 2015), web-based software platforms, such as Galaxy (Bedoya-Reina et al, 2013), commonly used applications, and source code available in repositories such as Github. We explore these issues in more detail in Section 4.6.…”

Section: Biodiversity Genomicsmentioning

confidence: 99%

A survey of biodiversity informatics: Concepts, practices, and challenges

Gadelha

Siracusa

Dalcin

et al. 2020

WIREs Data Min & Knowl

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The unprecedented size of the human population, along with its associated economic activities, has an ever‐increasing impact on global environments. Across the world, countries are concerned about the growing resource consumption and the capacity of ecosystems to provide resources. To effectively conserve biodiversity, it is essential to make indicators and knowledge openly available to decision‐makers in ways that they can effectively use them. The development and deployment of tools and techniques to generate these indicators require having access to trustworthy data from biological collections, field surveys and automated sensors, molecular data, and historic academic literature. The transformation of these raw data into synthesized information that is fit for use requires going through many refinement steps. The methodologies and techniques applied to manage and analyze these data constitute an area usually called biodiversity informatics. Biodiversity data follow a life cycle consisting of planning, collection, certification, description, preservation, discovery, integration, and analysis. Researchers, whether producers or consumers of biodiversity data, will likely perform activities related to at least one of these steps. This article explores each stage of the life cycle of biodiversity data, discussing its methodologies, tools, and challenges. This article is categorized under: Algorithmic Development > Biological Data Mining

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Parsl

Cited by 162 publications

References 19 publications

2′-O methylation of RNA cap in SARS-CoV-2 captured by serial crystallography

2′-O methylation of RNA cap in SARS-CoV-2 captured by serial crystallography

Enabling the execution of large scale workflows for molecular dynamics simulations

A survey of biodiversity informatics: Concepts, practices, and challenges

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