Briefly, we use computational approaches to study how DNA and protein sequences evolve, broadly interpreted. We test and develop methods for inferring evolutionary patterns from DNA sequence data, and we also conduct empirical work to study the evolutionary pressures acting in specific protein families and/or organisms. Stay tuned as content in this page develops…
SJ Spielman and SL Kosakovsky Pond. 2018. Relative evolutionary rates in proteins are largely insensitive to the substitution model. Mol. Biol. Evol. 35(9): 2307–2317.
SJ Spielman and SL Kosakovsky Pond. 2018. Relative evolutionary rate inference in HyPhy with LEISR. PeerJ 6:e4339.
SJ Spielman. 2018. phyphy: Python package for facilitating the execution and parsing of HyPhy standard analyses. Journal of Open Source Software, 3(21), 514.
S Weaver, SD Shank, SJ Spielman, M Li, SV Muse, and SL Kosakovsky Pond. 2018. Datamonkey 2.0: A modern web application for characterizing selective and other evolutionary processes. Mol. Biol. Evol. 35(3): 773-777.
DK Sydykova, BR Jack, SJ Spielman, and CO Wilke. 2017. Measuring evolutionary rates of proteins in a structural context. F1000Research 6:1845.
EL Jackson, SJ Spielman, and CO Wilke. 2017. Computational prediction of the tolerance to amino-acid deletion in green-fluorescent protein. PLOS ONE 12(4): e0164905.
Z Kadlecova, SJ Spielman, D Loerke, A Mohanakrishnan, DK Reed, and SL Schmid. 2017. Regulation of clathrin-mediated endocytosis by hierarchical allosteric activation of AP2. J Cell Biol 216(1): 167–179.
SJ Spielman, S Wan, and CO Wilke. 2016. A comparison of one-rate and two-rate inference frameworks for site-specific dN/dS estimation. Genetics 204(2): 499–511.
SJ Spielman and CO Wilke. 2016. Extensively parameterized mutation–selection models reliably capture site-specific selective constraint. Mol Biol Evol 33(11): 2990–3002.
EL Jackson, A Shahmoradi, SJ Spielman, BR Jack, and CO Wilke. 2016. Intermediate divergence levels maximize the strength of structure–sequence correlations in enzymes and viral proteins. Protein Sci 25(7): 1341-1353.
J Echave, SJ Spielman, and CO Wilke. 2016. Causes of evolutionary rate variation among protein sites. Nature Rev Genet 17: 109-121.
SJ Spielman and CO Wilke. 2015. Pyvolve: A flexible Python module for simulating sequences along phylogenies. PLOS ONE 10(9): e0139047.
AG Meyer, SJ Spielman, T Bedford, and CO Wilke. 2015. Time dependence of evolutionary metrics during the 2009 pandemic influenza virus outbreak. Virus Evolution. 1(1):vev006-10.
SJ Spielman and CO Wilke. 2015. The relationship between dN/dS and scaled selection coefficients. Mol Biol Evol 32(4): 1097-1108.
SJ Spielman, K Kumar, and CO Wilke. 2015. Comprehensive, structurally-curated alignment and phylogeny of vertebrate biogenic amine receptors. PeerJ 3:e773.
SJ Spielman, ET Dawson, and CO Wilke. 2014. Limited utility of residue masking for positive-selection inference. Mol Biol Evol 31(9): 2496-2500. 2014.
A Shahmoradi, DK Sydykova, SJ Spielman, EL Jackson, ET Dawson, AG Meyer, and CO Wilke. 2014. Predicting evolutionary site variability from structure in viral proteins: buriedness, packing, flexibility, and design. J Mol Evol 79:130–142. 2014.
[PREPRINT] SJ Spielman* , AG Meyer*, and CO Wilke. 2014. Increased evolutionary rate in the 2014 West African Ebola outbreak is due to transient polymorphism and not positive selection. bioRxiv. *Authors contributed equally to this work.
MZ Tien, AG Meyer, DK Sydykova, SJ Spielman, and CO Wilke. 2013. Maximum allowed solvent accessibilites of residues in proteins. PLOS One 8(11):e80635. 2013.
SJ Spielman and CO Wilke. 2013. Membrane environment imposes unique selection pressures in transmembrane domains of G-protein coupled receptors. J Mol Evol 76(3):172-182. 2013.