High-Throughput Quality Control of DMSO Acoustic Dispensing Using Photometric Dye Methods

Quintero, Catherine; Tran, K.T.; Szewczak, Alexander A.

doi:10.1177/2211068213486787

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…MongoDB was chosen on account of: 1) the database system can easily be scaled out allowing a modern data management approach such as data centric architecture can potentially be applied to this database system [28,29]; 2) it is possible to change the data schema of the conceptual data layer implemented in the API library [28]; and 3) the MongoDB supports large file storage [29] for storing data such as gene expression data or sequencing reads. However, the schema free property of MongoDB allows storing unstructured data into the database, this might cause data inconsistency.…”

Section: Methodsmentioning

confidence: 99%

A dedicated database system for handling multi-level data in systems biology

Pornputtapong

Wanichthanarak

Nilsson

et al. 2014

Source Code Biol Med

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BackgroundAdvances in high-throughput technologies have enabled extensive generation of multi-level omics data. These data are crucial for systems biology research, though they are complex, heterogeneous, highly dynamic, incomplete and distributed among public databases. This leads to difficulties in data accessibility and often results in errors when data are merged and integrated from varied resources. Therefore, integration and management of systems biological data remain very challenging.MethodsTo overcome this, we designed and developed a dedicated database system that can serve and solve the vital issues in data management and hereby facilitate data integration, modeling and analysis in systems biology within a sole database. In addition, a yeast data repository was implemented as an integrated database environment which is operated by the database system. Two applications were implemented to demonstrate extensibility and utilization of the system. Both illustrate how the user can access the database via the web query function and implemented scripts. These scripts are specific for two sample cases: 1) Detecting the pheromone pathway in protein interaction networks; and 2) Finding metabolic reactions regulated by Snf1 kinase.Results and conclusionIn this study we present the design of database system which offers an extensible environment to efficiently capture the majority of biological entities and relations encountered in systems biology. Critical functions and control processes were designed and implemented to ensure consistent, efficient, secure and reliable transactions. The two sample cases on the yeast integrated data clearly demonstrate the value of a sole database environment for systems biology research.

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Section: Methodsmentioning

confidence: 99%

A dedicated database system for handling multi-level data in systems biology

Pornputtapong

Wanichthanarak

Nilsson

et al. 2014

Source Code Biol Med

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“…Many commercial and academic SDMS's are currently available and have been used in production-oriented and research laboratories respectively for many years [7]- [10]. Commercial SDMS products typically support only a single organization or domain in a rigid manner and are oriented toward business processes rather than research.…”

Section: Introductionmentioning

confidence: 99%

DataFed: Towards Reproducible Research via Federated Data Management

Stansberry

Somnath

Breet

et al. 2019

2019 International Conference on Computational Science and Computational Intelligence (CSCI)

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The increasingly collaborative, globalized nature of scientific research combined with the need to share data and the explosion in data volumes present an urgent need for a scientific data management system (SDMS). An SDMS presents a logical and holistic view of data that greatly simplifies and empowers data organization, curation, searching, sharing, dissemination, etc. We present DataFed -a lightweight, distributed SDMS that spans a federation of storage systems within a loosely-coupled network of scientific facilities. Unlike existing SDMS offerings, DataFed uses high-performance and scalable user management and data transfer technologies that simplify deployment, maintenance, and expansion of DataFed. DataFed provides web-based and command-line interfaces to manage data and integrate with complex scientific workflows. DataFed represents a step towards reproducible scientific research by enabling reliable staging of the correct data at the desired environment.

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Instrument Quality Control

Jayakody¹,

Hull-Ryde²

2016

Methods in Molecular Biology

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Well-defined quality control (QC) processes are used to determine whether a certain procedure or action conforms to a widely accepted standard and/or set of guidelines, and are important components of any laboratory quality assurance program (Popa-Burke et al., J Biomol Screen 14: 1017-1030, 2009). In this chapter, we describe QC procedures useful for monitoring the accuracy and precision of laboratory instrumentation, most notably automated liquid dispensers. Two techniques, gravimetric QC and photometric QC, are highlighted in this chapter. When used together, these simple techniques provide a robust process for evaluating liquid handler accuracy and precision, and critically underpin high-quality research programs.

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High-Throughput Quality Control of DMSO Acoustic Dispensing Using Photometric Dye Methods

Cited by 4 publications

References 13 publications

A dedicated database system for handling multi-level data in systems biology

A dedicated database system for handling multi-level data in systems biology

DataFed: Towards Reproducible Research via Federated Data Management

Instrument Quality Control

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