Scientific DataGeneticsPeer-reviewed journal article
MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses
Jay DevineiD, Marta Vidal‐GarcíaiD, Wei LiuiD, Amanda Neves, Lucas Lo VercioiD, Rebecca M. GreeniD, Heather Ashley RichbourgiD, Marta MarchiniiD, Colton M. UngeriD, Audrey C. NickleiD, Bethany N. RadfordiD, Nathan M. YoungiD, Paula GonzáleziD, Robert E. SchuleriD, Alejandro Bugacov, Campbell RolianiD, Christopher J. PercivaliD, Trevor WilliamsiD, Lee Ann NiswanderiD, Anne L. CalofiD, Arthur D. LanderiD, Axel ViseliD, Frank Robert JirikiD, James M. CheverudiD, Ophir D. KleiniD, , Benedikt HallgrímssoniD
- Citations
- 12
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, doi.org/10.25550/3-HXMC) and GitHub (https://github.com/jaydevine/MusMorph).
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