gray mouse lemur
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Heredity ◽  
2021 ◽  
Author(s):  
C. Ryan Campbell ◽  
George P. Tiley ◽  
Jelmer W. Poelstra ◽  
Kelsie E. Hunnicutt ◽  
Peter A. Larsen ◽  
...  

2020 ◽  
Vol 107 (1) ◽  
Author(s):  
Lance A. Durden ◽  
Sharon E. Kessler ◽  
Ute Radespiel ◽  
Alida F. Hasiniaina ◽  
Alexandr A. Stekolnikov ◽  
...  

2019 ◽  
Vol 303 (5) ◽  
pp. 1354-1363
Author(s):  
Marissa L. Boettcher ◽  
Kaitlyn C. Leonard ◽  
Edwin Dickinson ◽  
Fabienne Aujard ◽  
Anthony Herrel ◽  
...  

2019 ◽  
Author(s):  
C. Ryan Campbell ◽  
George P. Tiley ◽  
Jelmer W. Poelstra ◽  
Kelsie E. Hunnicutt ◽  
Peter A. Larsen ◽  
...  

AbstractSpontaneous germline mutations are the raw material on which evolution acts, and knowledge of their frequency and genomic distribution is crucial for understanding how evolution operates at both long and short timescales. At present, the rate and spectrum of de novo mutations have been directly characterized in only a few lineages. It is therefore critical to expand the phylogenetic scope of these studies to gain a more general understanding of observed mutation rate patterns. Our study provides the first direct mutation rate estimate for a strepsirrhine (i.e., the lemurs and lorises), which comprise nearly half of the primate clade. Using high-coverage linked-read sequencing for a focal quartet of gray mouse lemurs (Microcebus murinus), we estimated the mutation rate to be 1.64 × 10−8 (95% credible interval: 1.41 × 10−8 to 1.98 × 10−8) mutations/site/generation. This estimate is higher than those measured for most previously characterized mammals. Further, we found an unexpectedly low count of paternal mutations, and only a modest overrepresentation of mutations at CpG-sites. Given the surprising nature of these observations, we conducted an independent analysis of context-dependent substitution types for gray mouse lemur and five additional primate species. This analysis yielded patterns consistent with the mutation spectrum from the pedigree mutation-rate analysis, which provides confidence in our ability to accurately identify de novo mutations with our data and bioinformatic filters.


Gene ◽  
2018 ◽  
Vol 677 ◽  
pp. 332-339 ◽  
Author(s):  
K.K. Biggar ◽  
B.E. Luu ◽  
C.W. Wu ◽  
F. Pifferi ◽  
M. Perret ◽  
...  

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