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

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 languageAmerican English
JournalFaculty Research 2022
Volume9
Issue number1
DOIs
StatePublished - May 25 2022

Keywords

  • JMG
  • Animals
  • Brain
  • Databases
  • Factual
  • Mice
  • X-Ray Microtomography

Disciplines

  • Life Sciences
  • Medicine and Health Sciences

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