Scientific Data (May 2022)

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. Calof,
  • Arthur 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

DOI
https://doi.org/10.1038/s41597-022-01338-x
Journal volume & issue
Vol. 9, no. 1
pp. 1 – 18

Abstract

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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 ).