Marco Roos

Leiden University Medical Center

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Co-Authors

Barend Mons

Leiden University Medical Center

Rajaram Kaliyaperumal

Leiden University Medical Center

Annika Jacobsen

Leiden University Medical Center

Peter A.C. ‘t Hoen

Radboud University Nijmegen

Eleni Mina

Leiden University Medical Center

Kristina Hettne

Leiden University

Mark Thompson

University of North Texas

Erik Schultes

GO FAIR Foundation

Núria Queralt-Rosiñach

Leiden University Medical Center

César H. Bernabé

Leiden University Medical Center

Cooperative Institutions

Leiden University Medical Center

180

Leiden University

143

RIKEN Center for Integrative Medical Sciences

123

University of Amsterdam

101

Russian Academy of Sciences

Timiryazev Institute of Plant Physiology

Institute of Biophysics and Cell Engineering

Harvard University

European Bioinformatics Institute

Vrije Universiteit Amsterdam

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From peer-reviewed to peer-reproduced: a role for data standards, models and computational workflows in scholarly publishing

bioRxiv (Cold Spring Harbor Laboratory) (2014)

Alejandra González-Beltrán Peter Li Jun Zhao María Susana Ávila-García Marco Roos

Motivation: Reproducing the results from a scientific paper can be challenging due to the absence of data and the computational tools required for their analysis. In addition, details relating to the proce- dures used to obtain the published results can be difficult to discern due to the use of natural language when reporting how experiments have been performed. The Investigation/Study/Assay (ISA), Nanop- ublications (NP) and Research Objects (RO) models are conceptual data modelling frameworks that can structure such information from scientific papers. Computational workflow platforms can also be used to reproduce analyses of data in a principled manner. We assessed the extent by which ISA, NP and RO models, together with the Galaxy workflow system, can capture the experimental processes and reproduce the findings of a previously published paper reporting on the development of SOAPdenovo2, a de novo genome assembler. Results: Executable workflows were developed using Galaxy which reproduced results that were con- sistent with the published findings. A structured representation of the information in the SOAPdenovo2 paper was produced by combining the use of ISA, NP and RO models. By structuring the information in the published paper using these data and scientific workflow modelling frameworks, it was possible to explicitly declare elements of experimental design, variables and findings. The models served as guides in the curation of scientific information and this led to the identification of inconsistencies in the original published paper, thereby allowing its authors to publish corrections in the form of an errata. Availability: SOAPdenovo2 scripts, data and results are available through the GigaScience Database: http://dx.doi.org/10.5524/100044; the workflows are available from GigaGalaxy: http://galaxy. cbiit.cuhk.edu.hk; and the representations using the ISA, NP and RO models are available through the SOAPdenovo2 case study website http://isa-tools.github.io/soapdenovo2/. Contact: philippe.rocca- serra@oerc.ox.ac.uk and susanna.assunta-sansone@oerc.ox.ac.uk

Executable

Data publishing

Macro

Representation

10.1101/011973

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Citations (3)

Botanical richness and endemicity patterns of Borneo derived from species distribution models

Ecography (2009)

Niels Raes Marco Roos J. W. Ferry Slik E. Emiel van Loon Hans ter Steege

This study provides a Borneo‐wide, quantitative assessment of botanical richness and endemicity at a high spatial resolution, and based on actual collection data. To overcome the bias in collection effort, and to be able to predict the presence and absence of species, even for areas where no collections have been made, we constructed species distribution models (SDMs) for all species taxonomically revised in Flora Malesiana. Species richness and endemicity maps were based on 1439 significant SDMs. Mapping of the residuals of the richness‐endemicity relationship identified areas with higher levels of endemicity than can be expected on the basis of species richness, the endemicity hotspots. We were able to identify one previously unknown region of high diversity, the high mountain peaks of East Kalimantan; and two additional endemicity hotspots, the Müller Mountains and the Sangkulirang peninsula. The areas of high diversity and endemicity were characterized by a relatively small range in annual temperature, but with seasonality in temperatures within that range. Furthermore, these areas were least affected by El Niño Southern Oscillation drought events. The endemicity hotspots were found in areas, which were ecologically distinct in altitude, edaphic conditions, annual precipitation, or a combination of these factors. These results can be used to guide conservation efforts of the highly threatened forests of Borneo.

Edaphic

Peninsula

Species distribution

Endemism

10.1111/j.1600-0587.2009.05800.x

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Citations (190)

Future application of ontologies in e-Bioscience.

Marco Roos M.G. Jansen Stuart Marshall Timo M. Breit

Source

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Citations (2)

Component-compatibility in historical biogeography

M. Zandee Marco Roos

-The problems of reconstructing historical relationships for areas of endemism from distributional data for groups of taxa and the cladistic relationships among the members of those groups can be solved by applying the two principles of parsimony and mutual inclusion or exclusion (compatibility) of components. Components can be extracted from a data matrix by means of transcription into partial monothetic sets. The data matrix thus derived represents the distribution over areas for the monophyletic groups in one or more cladograms. It is derived from two different matrices by boolean multiplication. The first matrix gives the binary representation of distributions of taxa over areas of endemism; the second describes the cladogram for the same taxa, in terms of character states converted into binary form by additive binary coding. The derived data matrix can be used in historical biogeography to represent the given phyletic data ( Assumption 0 here newly defined), and can be amended to reflect Assumptions 1 or 2 to accomodate the problems of wide-spread taxa and missing areas. Areacladograms are determined from the derived matrix by searching for the largest sets of mutually compatible components. Area-cladograms are evaluated in terms of support (vicariance) and contradiction (ad hoc interpretations such as dispersal and extinction). Area-cladograms that best fit the data matrix regarding the balance between support and contradiction are selected as the best possible recontructions of relationships among the areas of endemism. The procedure is illustrated by the example of the poeciliid fish genera Heterandria and Xiphophorus, and several other standard examples.

Cladogram

Monophyly

Endemism

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Citations (0)

How interoperable is our semantic model and for who

Niek van Ulzen Anne de Jong Marije Kanis Marco Roos Rajaram Kaliyaperumal

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Meeting on data sharing for Duchenne 21–22 March 2019 Amsterdam, the Netherlands

Neuromuscular Disorders (2019)

Ingrid E.C. Verhaart Peter A.C. ‘t Hoen Marco Roos Elizabeth Vroom

Declaration

Data Sharing

10.1016/j.nmd.2019.08.010

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Citations (3)

The use of Foundational Ontologies in Bioinformatics - Supplementary Material

Zenodo (CERN European Organization for Nuclear Research) (2021)

César H. Bernabé Núria Queralt-Rosiñach Vítor E. Silva Souza Luiz Olavo Bonino da Silva Santos Annika Jacobsen

Supplementary material for the paper "The use of Foundational Ontologies in Bioinformatics".

10.5281/zenodo.6961802

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The use of Foundational Ontologies in Bioinformatics - Supplementary Material

Zenodo (CERN European Organization for Nuclear Research) (2021)

César H. Bernabé Núria Queralt-Rosiñach Vítor E. Silva Souza Luiz Olavo Bonino da Silva Santos Annika Jacobsen

Supplementary material for the paper "The use of Foundational Ontologies in Bioinformatics".

Structural bioinformatics

10.5281/zenodo.6961846

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Additional file 7 of Common disease signatures from gene expression analysis in Huntingtonâ s disease human blood and brain

Figshare (2016)

Eleni Mina Willeke M. C. van Roon‐Mom Kristina Hettne Erik W. van Zwet Jelle J. Goeman

Immune response signature. In this file we include detailed information about the immune response signature, including the annotations and gene identifiers of each module that participates in this signature. (XLS 15 kb)

10.6084/m9.figshare.c.3603713_d5

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Additional file 8 of Common disease signatures from gene expression analysis in Huntingtonâ s disease human blood and brain

Figshare (2016)

Eleni Mina Willeke M. C. van Roon‐Mom Kristina Hettne Erik W. van Zwet Jelle J. Goeman

SCA signature. In this file we include detailed information about the SCA signature, including the annotations and gene identifiers of each module that participates in this signature. (XLS 25 kb)

10.6084/m9.figshare.c.3603713_d7

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