TY - JOUR
T1 - MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses
AU - Devine, Jay
AU - Vidal-García, Marta
AU - Liu, Wei
AU - Neves, Amanda
AU - Lo Vercio, Lucas D.
AU - Green, Rebecca M.
AU - Richbourg, Heather A.
AU - Marchini, Marta
AU - Unger, Colton M.
AU - Nickle, Audrey C.
AU - Radford, Bethany
AU - Young, Nathan M.
AU - Gonzalez, Paula N.
AU - Schuler, Robert E.
AU - Bugacov, Alejandro
AU - Rolian, Campbell
AU - Percival, Christopher J.
AU - Williams, Trevor
AU - Niswander, Lee
AU - Calof, Anne L.
AU - Lander, Arthur D.
AU - Visel, Axel
AU - Jirik, Frank R.
AU - Cheverud, James M.
AU - Klein, Ophir D.
AU - Birnbaum, Ramon Y.
AU - Merrill, Amy E.
AU - Ackermann, Rebecca R.
AU - Graf, Daniel
AU - Hemberger, Myriam
AU - Dean, Wendy
AU - Forkert, Nils D.
AU - Murray, Stephen A.
AU - Westerberg, Henrik
AU - Marcucio, Ralph S.
AU - Hallgrímsson, Benedikt
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - 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).
AB - 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).
UR - https://www.scopus.com/pages/publications/85130747914
U2 - 10.1038/s41597-022-01338-x
DO - 10.1038/s41597-022-01338-x
M3 - Article
C2 - 35614082
AN - SCOPUS:85130747914
SN - 2052-4463
VL - 9
JO - Scientific Data
JF - Scientific Data
IS - 1
M1 - 230
ER -