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

Jay Devine, Marta Vidal-García, Wei Liu, Amanda Neves, Lucas D. Lo Vercio, Rebecca M. Green, Heather A. Richbourg, Marta Marchini, Colton M. Unger, Audrey C. Nickle, Bethany Radford, Nathan M. Young, Paula N. Gonzalez, Robert E. Schuler, Alejandro Bugacov, Campbell Rolian, Christopher J. Percival, Trevor Williams, Lee Niswander, Anne L. CalofArthur D. Lander, Axel Visel, Frank R. Jirik, James M. Cheverud, Ophir D. Klein, Ramon Y. Birnbaum, Amy E. Merrill, Rebecca R. Ackermann, Daniel Graf, Myriam Hemberger, Wendy Dean, Nils D. Forkert, Stephen A. Murray, Henrik Westerberg, Ralph S. Marcucio, Benedikt Hallgrímsson

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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).

Original languageEnglish
Article number230
JournalScientific data
Volume9
Issue number1
DOIs
StatePublished - 1 Dec 2022

ASJC Scopus subject areas

  • Information Systems
  • Education
  • Library and Information Sciences
  • Statistics and Probability
  • Computer Science Applications
  • Statistics, Probability and Uncertainty

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