Search Results - "Mughal, Mehreen R"

Extreme purifying selection against point mutations in the human genome by Dukler, Noah, Mughal, Mehreen R., Ramani, Ritika, Huang, Yi-Fei, Siepel, Adam

“…Large-scale genome sequencing has enabled the measurement of strong purifying selection in protein-coding genes. Here we describe a new method, called…”
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Learning the properties of adaptive regions with functional data analysis by Mughal, Mehreen R, Koch, Hillary, Huang, Jinguo, Chiaromonte, Francesca, DeGiorgio, Michael

“…Identifying regions of positive selection in genomic data remains a challenge in population genetics. Most current approaches rely on comparing values of…”
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Localizing and Classifying Adaptive Targets with Trend Filtered Regression by Mughal, Mehreen R, DeGiorgio, Michael

“…Identifying genomic locations of natural selection from sequence data is an ongoing challenge in population genetics. Current methods utilizing information…”
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Properties and unbiased estimation of F- and D-statistics in samples containing related and inbred individuals by Mughal, Mehreen R, DeGiorgio, Michael

“…Abstract The Patterson F- and D-statistics are commonly used measures for quantifying population relationships and for testing hypotheses about demographic…”
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Evolutionary and phylogenetic insights from a nuclear genome sequence of the extinct, giant, “subfossil” koala lemur Megaladapis edwardsi by Marciniak, Stephanie, Mughal, Mehreen R., Godfrey, Laurie R., Bankoff, Richard J., Randrianatoandro, Heritiana, Crowley, Brooke E., Bergey, Christina M., Muldoon, Kathleen M., Randrianasy, Jeannot, Raharivololona, Brigitte M., Schuster, Stephan C., Malhi, Ripan S., Yoder, Anne D., Louis, Edward E., Kistler, Logan, Perry, George H.

“…No endemic Madagascar animal with body mass >10 kg survived a relatively recent wave of extinction on the island. From morphological and isotopic analyses of…”
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Statistical Frameworks for Evaluating Evolutionary Hypotheses from Genomic Data by Mughal, Mehreen R

“…As genome sequence data availability increases, the demands for methods that are able to efficiently and rigorously extract relevant information from this data…”
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