Dataset Information

MetNetAPI: A flexible method to access and manipulate biological network data from MetNet.

ABSTRACT:

Background

Convenient programmatic access to different biological databases allows automated integration of scientific knowledge. Many databases support a function to download files or data snapshots, or a webservice that offers "live" data. However, the functionality that a database offers cannot be represented in a static data download file, and webservices may consume considerable computational resources from the host server.

Results

MetNetAPI is a versatile Application Programming Interface (API) to the MetNetDB database. It abstracts, captures and retains operations away from a biological network repository and website. A range of database functions, previously only available online, can be immediately (and independently from the website) applied to a dataset of interest. Data is available in four layers: molecular entities, localized entities (linked to a specific organelle), interactions, and pathways. Navigation between these layers is intuitive (e.g. one can request the molecular entities in a pathway, as well as request in what pathways a specific entity participates). Data retrieval can be customized: Network objects allow the construction of new and integration of existing pathways and interactions, which can be uploaded back to our server. In contrast to webservices, the computational demand on the host server is limited to processing data-related queries only.

Conclusions

An API provides several advantages to a systems biology software platform. MetNetAPI illustrates an interface with a central repository of data that represents the complex interrelationships of a metabolic and regulatory network. As an alternative to data-dumps and webservices, it allows access to a current and "live" database and exposes analytical functions to application developers. Yet it only requires limited resources on the server-side (thin server/fat client setup). The API is available for Java, Microsoft.NET and R programming environments and offers flexible query and broad data- retrieval methods. Data retrieval can be customized to client needs and the API offers a framework to construct and manipulate user-defined networks. The design principles can be used as a template to build programmable interfaces for other biological databases. The API software and tutorials are available at http://www.metnetonline.org/api.

SUBMITTER: Sucaet Y

PROVIDER: S-EPMC2998519 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

MetNetAPI: A flexible method to access and manipulate biological network data from MetNet.

Sucaet Yves Y Wurtele Eve Syrkin ES

BMC research notes 20101118

<h4>Background</h4>Convenient programmatic access to different biological databases allows automated integration of scientific knowledge. Many databases support a function to download files or data snapshots, or a webservice that offers "live" data. However, the functionality that a database offers cannot be represented in a static data download file, and webservices may consume considerable computational resources from the host server.<h4>Results</h4>MetNetAPI is a versatile Application Program ...[more]

PMID: 21083943

Similar Datasets

Project description:Phylogenetic trees illustrate evolutionary relationships between taxa or genes. Tree figures are crucial when presenting results and data, and by creating clear and effective plots, researchers can describe many kinds of evolutionary patterns. However, producing tree plots can be a time-consuming task, especially as multiple different programs are often needed to adjust and illustrate all data associated with a tree. We present TreeViewer, a new software to draw phylogenetic trees. TreeViewer is flexible, modular, and user-friendly. Plots are produced as the result of a user-defined pipeline, which can be finely customised and easily applied to different trees. Every feature of the program is documented and easily accessible, either in the online manual or within the program's interface. We show how TreeViewer can be used to produce publication-ready figures, saving time by not requiring additional graphical post-processing tools. TreeViewer is freely available for Windows, macOS, and Linux operating systems and distributed under an AGPLv3 licence from https://treeviewer.org. It has a graphical user interface (GUI), as well as a command-line interface, which is useful to work with very large trees and for automated pipelines. A detailed user manual with examples and tutorials is also available. TreeViewer is mainly aimed at users wishing to produce highly customised, publication-quality tree figures using a single GUI software tool. Compared to other GUI tools, TreeViewer offers a richer feature set and a finer degree of customisation. Compared to command-line-based tools and software libraries, TreeViewer's graphical interface is more accessible. The flexibility of TreeViewer's approach to phylogenetic tree plotting enables the program to produce a wide variety of publication-ready figures. Users are encouraged to create their own custom modules to expand the functionalities of the program. This sets the scene for an ever-expanding and ever-adapting software framework that can easily adjust to respond to new challenges.

Project description:BackgroundOne method of identifying cis regulatory differences is to analyze allele-specific expression (ASE) and identify cases of allelic imbalance (AI). RNA-seq is the most common way to measure ASE and a binomial test is often applied to determine statistical significance of AI. This implicitly assumes that there is no bias in estimation of AI. However, bias has been found to result from multiple factors including: genome ambiguity, reference quality, the mapping algorithm, and biases in the sequencing process. Two alternative approaches have been developed to handle bias: adjusting for bias using a statistical model and filtering regions of the genome suspected of harboring bias. Existing statistical models which account for bias rely on information from DNA controls, which can be cost prohibitive for large intraspecific studies. In contrast, data filtering is inexpensive and straightforward, but necessarily involves sacrificing a portion of the data.ResultsHere we propose a flexible Bayesian model for analysis of AI, which accounts for bias and can be implemented without DNA controls. In lieu of DNA controls, this Poisson-Gamma (PG) model uses an estimate of bias from simulations. The proposed model always has a lower type I error rate compared to the binomial test. Consistent with prior studies, bias dramatically affects the type I error rate. All of the tested models are sensitive to misspecification of bias. The closer the estimate of bias is to the true underlying bias, the lower the type I error rate. Correct estimates of bias result in a level alpha test.ConclusionsTo improve the assessment of AI, some forms of systematic error (e.g., map bias) can be identified using simulation. The resulting estimates of bias can be used to correct for bias in the PG model, without data filtering. Other sources of bias (e.g., unidentified variant calls) can be easily captured by DNA controls, but are missed by common filtering approaches. Consequently, as variant identification improves, the need for DNA controls will be reduced. Filtering does not significantly improve performance and is not recommended, as information is sacrificed without a measurable gain. The PG model developed here performs well when bias is known, or slightly misspecified. The model is flexible and can accommodate differences in experimental design and bias estimation.

Dataset Information

MetNetAPI: A flexible method to access and manipulate biological network data from MetNet.

Background

Results

Conclusions

Publications

MetNetAPI: A flexible method to access and manipulate biological network data from MetNet.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets