_A_05_BUSCO

Assay: _A_05_BUSCO Short Name: 05_BUSCO Assay Class: DRY Assay Type: BUSCO Title: BUSCO post filtering and reassignment Description: BUSCO post filtering and reassignment pISA Assay creation date: 2019-10-22 pISA Assay creator: Maja Zagorscak Phenodata: None Featuredata: None Data: see ./input/path_to_files.txt

Submitter: Maja Zagorscak

Biological problem addressed: Validation

Investigation: _I_STRT

Study: _S_03_stCuSTr

_A_02.1_BUSCO

Assay: _A_02.1_BUSCO Short Name: 02.1_BUSCO Assay Class: DRY Assay Type: BUSCO Title: BUSCO on assemblies and tr2aacds output Description: BUSCO on assemblies and tr2aacds output pISA Assay creation date: 2019-10-22 pISA Assay creator: Maja Zagorscak Phenodata: None Featuredata: None Data: see ./input/path_to_files.txt

Submitter: Maja Zagorscak

Biological problem addressed: Validation

Investigation: _I_STRT

Study: _S_03_stCuSTr

Harmonizing semantic annotations for computational models in biology

Abstract (Expand)

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability … and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.

Authors: Maxwell Lewis Neal, Matthias König, David Nickerson, Göksel Mısırlı, Reza Kalbasi, Andreas Dräger, Koray Atalag, Vijayalakshmi Chelliah, Michael T Cooling, Daniel L Cook, Sharon Crook, Miguel de Alba, Samuel H Friedman, Alan Garny, John H Gennari, Padraig Gleeson, Martin Golebiewski, Michael Hucka, Nick Juty, Chris Myers, Brett G Olivier, Herbert M Sauro, Martin Scharm, Jacky L Snoep, Vasundra Touré, Anil Wipat, Olaf Wolkenhauer, Dagmar Waltemath

Date Published: 1st Mar 2019

Publication Type: Journal

Powered by
 (v.1.16.0-pre)
About FAIRDOMHub | Funding and Programmes | Credits | Terms & Conditions | Imprint | Cookie preferences
Copyright © 2008 - 2024 The University of Manchester and HITS gGmbH