scholarly journals RCX – an R package adapting the Cytoscape Exchange format for biological networks

2021 ◽  
Author(s):  
Florian J Auer ◽  
Frank Kramer
Keyword(s):  
Biological Networks ◽  
Data Exchange ◽  
R Package ◽  
Statistical Computing ◽  
Biological Knowledge ◽  
Data Types ◽  
Web Technologies ◽  
Standard Data ◽  
Exchange Format ◽  
Web Standards

Motivation: The Cytoscape Exchange (CX) format is a JSON-based data structure designed for the transmission of biological networks using standard web technologies. It was developed by the network data exchange (NDEx), which itself serves as online commons to share and collaborate on biological networks. The Cytoscape software for the analysis and visualization of biological networks contributes further elements to capture the visual layout within the CX format. However, there is a fundamental difference between web standards and R of how data has to be structured. Results: Here we present a software package to create, handle, validate, visualize and convert networks in CX format to standard data types and objects within R. Networks in this format can serve as a source for biological knowledge, and also capture the results of the analysis of those while preserving the visual layout across all platforms. The RCX package connects the R environment for statistical computing with platforms for collaboration, analysis and visualization of biological networks. Availability: RCX is a free and open-source R package, available via GitHub (https://github.com/frankkramer-lab/RCX) and submitted to Bioconductor.

scholarly journals Standard for improving emergency information interoperability: the HL7 data elements for emergency department systems

2015 ◽  
Vol 22 (3) ◽  
pp. 529-535 ◽  
Author(s):  
James C McClay ◽  
Peter J Park ◽  
Mark G Janczewski ◽  
Laura Heermann Langford
Keyword(s):  
Emergency Department ◽  
Emergency Care ◽  
Data Exchange ◽  
The United States ◽  
Data Types ◽  
Unique Identifier ◽  
Related Data ◽  
Standard Data ◽  
Multiple Data ◽  
Data Elements

Abstract Background Emergency departments in the United States service over 130 million visits per year. The demands for information from these visits require interoperable data exchange standards. While multiple data exchange specifications are in use, none have undergone rigorous standards review. This paper describes the creation and balloting of the Health Level Seven (HL7) Data Elements for Emergency Department Systems (DEEDS). Methods Existing data exchange specifications were collected and organized into categories reflecting the workflow of emergency care. The concepts were then mapped to existing standards for vocabulary, data types, and the HL7 information model. The HL7 community then processed the specification through the normal balloting process addressing all comments and concerns. The resulting specification was then submitted for publication as an HL7 informational standard. Results The resulting specification contains 525 concepts related to emergency care required for operations and reporting to external agencies. An additional 200 of the most commonly ordered laboratory tests were included. Each concept was given a unique identifier and mapped to Logical Observation Identifiers, Names, and Codes (LOINC). HL7 standard data types were applied. Discussion The HL7 DEEDS specification represents the first set of common ED related data elements to undergo rigorous standards development. The availability of this standard will contribute to improved interoperability of emergency care data.

scholarly journals SPART, a versatile and standardized data exchange format for species partition information

2021 ◽  
Author(s):  
Aurélien Miralles ◽  
Jacques Ducasse ◽  
Sophie Brouillet ◽  
Tomas Flouri ◽  
Tomochika Fujisawa ◽  
...  
Keyword(s):  
Data Exchange ◽  
Species Boundaries ◽  
Data Types ◽  
Standard Format ◽  
Data Exchange Format ◽  
Exchange Format ◽  
Wide Range ◽  
Separate Block ◽  
Computer Based ◽  
Alternative Species

A wide range of data types can be used to delimit species and various computer-based tools dedicated to this task are now available. Although these formalized approaches have significantly contributed to increase the objectivity of SD under different assumptions, they are not routinely used by alpha-taxonomists. One obvious shortcoming is the lack of interoperability among the various independently developed SD programs. Given the frequent incongruences between species partitions inferred by different SD approaches, researchers applying these methods often seek to compare these alternative species partitions to evaluate the robustness of the species boundaries. This procedure is excessively time consuming at present, and the lack of a standard format for species partitions is a major obstacle. Here we propose a standardized format, SPART, to enable compatibility between different SD tools exporting or importing partitions. This format reports the partitions and describes, for each of them, the assignment of individuals to the inferred species. The syntax also allows to optionally report support values, as well as original trees and the full command lines used in the respective SD analyses. Two variants of this format are proposed, overall using the same terminology but presenting the data either optimized for human readability (matricial SPART) or in a format in which each partition forms a separate block (SPART.XML). ABGD, DELINEATE, GMYC, PTP and TR2 have already been adapted to output SPART files and a new version of LIMES has been developed to import, export, merge and split them.

scholarly journals MUREN: a robust and multi-reference approach of RNA-seq transcript normalization

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yance Feng ◽  
Lei M. Li
Keyword(s):  
Biological Significance ◽  
Housekeeping Genes ◽  
R Package ◽  
Data Sets ◽  
Statistical Regression ◽  
Rna Seq ◽  
Least Trimmed Squares ◽  
Standard Data ◽  
Wide Range ◽  
Multiple References

Abstract Background Normalization of RNA-seq data aims at identifying biological expression differentiation between samples by removing the effects of unwanted confounding factors. Explicitly or implicitly, the justification of normalization requires a set of housekeeping genes. However, the existence of housekeeping genes common for a very large collection of samples, especially under a wide range of conditions, is questionable. Results We propose to carry out pairwise normalization with respect to multiple references, selected from representative samples. Then the pairwise intermediates are integrated based on a linear model that adjusts the reference effects. Motivated by the notion of housekeeping genes and their statistical counterparts, we adopt the robust least trimmed squares regression in pairwise normalization. The proposed method (MUREN) is compared with other existing tools on some standard data sets. The goodness of normalization emphasizes on preserving possible asymmetric differentiation, whose biological significance is exemplified by a single cell data of cell cycle. MUREN is implemented as an R package. The code under license GPL-3 is available on the github platform: github.com/hippo-yf/MUREN and on the conda platform: anaconda.org/hippo-yf/r-muren. Conclusions MUREN performs the RNA-seq normalization using a two-step statistical regression induced from a general principle. We propose that the densities of pairwise differentiations are used to evaluate the goodness of normalization. MUREN adjusts the mode of differentiation toward zero while preserving the skewness due to biological asymmetric differentiation. Moreover, by robustly integrating pre-normalized counts with respect to multiple references, MUREN is immune to individual outlier samples.

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