MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses

Authors

Jay Devine, Alberta Children’s Hospital Research Institute
Marta Vidal-García, Alberta Children’s Hospital Research Institute
Wei Liu, Alberta Children’s Hospital Research Institute
Amanda Neves, McMaster University
Lucas D. Lo Vercio, Alberta Children’s Hospital Research Institute
Rebecca M. Green, University of Pittsburgh
Heather A. Richbourg, University of California, San Francisco
Marta Marchini, Alberta Children’s Hospital Research Institute
Colton M. Unger, McCaig Institute for Bone and Joint Health
Audrey C. Nickle, Herman Ostrow School of Dentistry of USC
Bethany Radford, Alberta Children’s Hospital Research Institute
Nathan M. Young, University of California, San Francisco
Paula N. Gonzalez, Consejo Nacional de Investigaciones Científicas y Técnicas
Robert E. Schuler, Information Sciences Institute
Alejandro Bugacov, Information Sciences Institute
Campbell Rolian, McCaig Institute for Bone and Joint Health
Christopher J. Percival, Stony Brook University
Trevor Williams, University of Colorado Anschutz Medical Campus
Lee Niswander, University of Colorado Boulder
Anne L. Calof, University of California, Irvine
Arthur D. Lander, University of California, Irvine
Axel Visel, Lawrence Berkeley National Laboratory
Frank R. Jirik, Alberta Children’s Hospital Research Institute

Document Type

Article

Publication Date

12-1-2022

Publication Title

Scientific Data

Volume

9

Issue

1

Pages

1-18

Publisher Name

Nature Research

Abstract

Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen (N = 10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase (www.facebase.org, https://doi.org/10.25550/3-HXMC) and GitHub (https://github.com/jaydevine/MusMorph).

Identifier

85130747914 (Scopus)

Recommended Citation

Devine, Jay; Vidal-García, Marta; Liu, Wei; Neves, Amanda; Lo Vercio, Lucas D.; Green, Rebecca M.; Richbourg, Heather A.; Marchini, Marta; Unger, Colton M.; Nickle, Audrey C.; Radford, Bethany; Young, Nathan M.; Gonzalez, Paula N.; Schuler, Robert E.; Bugacov, Alejandro; Rolian, Campbell; Percival, Christopher J.; Williams, Trevor; Niswander, Lee; Calof, Anne L.; Lander, Arthur D.; Visel, Axel; and Jirik, Frank R.. MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses. Scientific Data, 9, 1: 1-18, 2022. Retrieved from Loyola eCommons, Biology: Faculty Publications and Other Works, http://dx.doi.org/10.1038/s41597-022-01338-x

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.