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Algorithms
L. C. Osborne, Palmer, S. E., Lisberger, S. G., and Bialek, W., The neural basis for combinatorial coding in a cortical population response., J Neurosci, vol. 28, no. 50, pp. 13522-31, 2008.
S. Heinicke, Livstone, M. S., Lu, C., Oughtred, R., Kang, F., Angiuoli, S. V., White, O., Botstein, D., and Dolinski, K., The Princeton Protein Orthology Database (P-POD): a comparative genomics analysis tool for biologists., PLoS One, vol. 2, no. 8, p. e766, 2007.
N. Slonim, Atwal, G. Singh, Tkačik, G., and Bialek, W., Information-based clustering., Proc Natl Acad Sci U S A, vol. 102, no. 51, pp. 18297-302, 2005.
Y. Deng, Coen, P., Sun, M., and Shaevitz, J. W., Efficient multiple object tracking using mutually repulsive active membranes., PLoS One, vol. 8, no. 6, p. e65769, 2013.
G. J. Stephens, Johnson-Kerner, B., Bialek, W., and Ryu, W. S., From modes to movement in the behavior of Caenorhabditis elegans., PLoS One, vol. 5, no. 11, p. e13914, 2010.
M. Wyart, Botstein, D., and Wingreen, N. S., Evaluating gene expression dynamics using pairwise RNA FISH data., PLoS Comput Biol, vol. 6, no. 11, p. e1000979, 2010.
L. S. Chen and Storey, J. D., Eigen-R2 for dissecting variation in high-dimensional studies., Bioinformatics, vol. 24, no. 19, pp. 2260-2, 2008.
P. V. Gordon, Muratov, C. B., and Shvartsman, S. Y., Local accumulation times for source, diffusion, and degradation models in two and three dimensions., J Chem Phys, vol. 138, no. 10, p. 104121, 2013.
Z. Khan, Bloom, J. S., Kruglyak, L., and Singh, M., A practical algorithm for finding maximal exact matches in large sequence datasets using sparse suffix arrays., Bioinformatics, vol. 25, no. 13, pp. 1609-16, 2009.
C. S. Greene and Troyanskaya, O. G., Accurate evaluation and analysis of functional genomics data and methods., Ann N Y Acad Sci, vol. 1260, pp. 95-100, 2012.
J. W. Shaevitz and Fletcher, D. A., Enhanced three-dimensional deconvolution microscopy using a measured depth-varying point-spread function., J Opt Soc Am A Opt Image Sci Vis, vol. 24, no. 9, pp. 2622-7, 2007.
W. Wang, J Cherry, M., Nochomovitz, Y., Jolly, E., Botstein, D., and Li, H., Inference of combinatorial regulation in yeast transcriptional networks: a case study of sporulation., Proc Natl Acad Sci U S A, vol. 102, no. 6, pp. 1998-2003, 2005.
T. Sharpee, Rust, N. C., and Bialek, W., Analyzing neural responses to natural signals: maximally informative dimensions., Neural Comput, vol. 16, no. 2, pp. 223-50, 2004.
C. Huttenhower, Hibbs, M., Myers, C., and Troyanskaya, O. G., A scalable method for integration and functional analysis of multiple microarray datasets., Bioinformatics, vol. 22, no. 23, pp. 2890-7, 2006.
E. Segal, Shapira, M., Regev, iv, A., Pe'er, D., Botstein, D., Koller, D., and Friedman, N., Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data., Nat Genet, vol. 34, no. 2, pp. 166-76, 2003.
L. Batsilas, Berezhkovskii, A. M., and Shvartsman, S. Y., Stochastic model of autocrine and paracrine signals in cell culture assays., Biophys J, vol. 85, no. 6, pp. 3659-65, 2003.
A. Ochoa, Llinás, M., and Singh, M., Using context to improve protein domain identification., BMC Bioinformatics, vol. 12, p. 90, 2011.
J. Zhou, Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., and Troyanskaya, O. G., Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk., Nat Genet, vol. 50, no. 8, pp. 1171-1179, 2018.
C. L. Myers, Chen, X., and Troyanskaya, O. G., Visualization-based discovery and analysis of genomic aberrations in microarray data., BMC Bioinformatics, vol. 6, p. 146, 2005.
M. F. Clasquin, Melamud, E., and Rabinowitz, J. D., LC-MS data processing with MAVEN: a metabolomic analysis and visualization engine., Curr Protoc Bioinformatics, vol. Chapter 14, p. Unit14.11, 2012.
J. P. Nguyen, Linder, A. N., Plummer, G. S., Shaevitz, J. W., and Leifer, A. M., Automatically tracking neurons in a moving and deforming brain., PLoS Comput Biol, vol. 13, no. 5, p. e1005517, 2017.
R. G. Endres and Wingreen, N. S., Weight matrices for protein-DNA binding sites from a single co-crystal structure., Phys Rev E Stat Nonlin Soft Matter Phys, vol. 73, no. 6 Pt 1, p. 061921, 2006.
S. R. McAllister, Feng, X. - J., DiMaggio, P. A., Floudas, C. A., Rabinowitz, J. D., and Rabitz, H., Descriptor-free molecular discovery in large libraries by adaptive substituent reordering., Bioorg Med Chem Lett, vol. 18, no. 22, pp. 5967-70, 2008.
P. Jiang and Singh, M., SPICi: a fast clustering algorithm for large biological networks., Bioinformatics, vol. 26, no. 8, pp. 1105-11, 2010.
C. L. Myers, Dunham, M. J., Kung, S. Y., and Troyanskaya, O. G., Accurate detection of aneuploidies in array CGH and gene expression microarray data., Bioinformatics, vol. 20, no. 18, pp. 3533-43, 2004.
C. Huttenhower, Flamholz, A. I., Landis, J. N., Sahi, S., Myers, C. L., Olszewski, K. L., Hibbs, M. A., Siemers, N. O., Troyanskaya, O. G., and Coller, H. A., Nearest Neighbor Networks: clustering expression data based on gene neighborhoods., BMC Bioinformatics, vol. 8, p. 250, 2007.
J. W. Shaevitz, Block, S. M., and Schnitzer, M. J., Statistical kinetics of macromolecular dynamics., Biophys J, vol. 89, no. 4, pp. 2277-85, 2005.
J. A. Capra and Singh, M., Predicting functionally important residues from sequence conservation., Bioinformatics, vol. 23, no. 15, pp. 1875-82, 2007.
M. D. Chikina and Troyanskaya, O. G., An effective statistical evaluation of ChIPseq dataset similarity., Bioinformatics, vol. 28, no. 5, pp. 607-13, 2012.
J. J. Zartman, Cheung, L. S., Niepielko, M. G., Bonini, C., Haley, B., Yakoby, N., and Shvartsman, S. Y., Pattern formation by a moving morphogen source., Phys Biol, vol. 8, no. 4, p. 045003, 2011.
J. A. Capra, Laskowski, R. A., Thornton, J. M., Singh, M., and Funkhouser, T. A., Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure., PLoS Comput Biol, vol. 5, no. 12, p. e1000585, 2009.
Z. Khan, Amini, S., Bloom, J. S., Ruse, C., Caudy, A. A., Kruglyak, L., Singh, M., Perlman, D. H., and Tavazoie, S., Accurate proteome-wide protein quantification from high-resolution 15N mass spectra., Genome Biol, vol. 12, no. 12, p. R122, 2011.
Z. Barutcuoglu, Schapire, R. E., and Troyanskaya, O. G., Hierarchical multi-label prediction of gene function., Bioinformatics, vol. 22, no. 7, pp. 830-6, 2006.
P. H. Bradley, Brauer, M. J., Rabinowitz, J. D., and Troyanskaya, O. G., Coordinated concentration changes of transcripts and metabolites in Saccharomyces cerevisiae., PLoS Comput Biol, vol. 5, no. 1, p. e1000270, 2009.
C. L. Myers, Barrett, D. R., Hibbs, M. A., Huttenhower, C., and Troyanskaya, O. G., Finding function: evaluation methods for functional genomic data., BMC Genomics, vol. 7, p. 187, 2006.
P. A. DiMaggio, McAllister, S. R., Floudas, C. A., Feng, X. - J., Rabinowitz, J. D., and Rabitz, H. A., Biclustering via optimal re-ordering of data matrices in systems biology: rigorous methods and comparative studies., BMC Bioinformatics, vol. 9, p. 458, 2008.
J. Zhou and Troyanskaya, O. G., Global quantitative modeling of chromatin factor interactions., PLoS Comput Biol, vol. 10, no. 3, p. e1003525, 2014.
S. R McIsaac, Silverman, S. J., Parsons, L., Xu, P., Briehof, R., McClean, M. N., and Botstein, D., Visualization and analysis of mRNA molecules using fluorescence in situ hybridization in Saccharomyces cerevisiae., J Vis Exp, no. 76, p. e50382, 2013.
Y. Guan, Ackert-Bicknell, C. L., Kell, B., Troyanskaya, O. G., and Hibbs, M. A., Functional genomics complements quantitative genetics in identifying disease-gene associations., PLoS Comput Biol, vol. 6, no. 11, p. e1000991, 2010.
C. Huttenhower and Troyanskaya, O. G., Bayesian data integration: a functional perspective., Comput Syst Bioinformatics Conf, pp. 341-51, 2006.
C. L. Myers and Troyanskaya, O. G., Context-sensitive data integration and prediction of biological networks., Bioinformatics, vol. 23, no. 17, pp. 2322-30, 2007.
E. Banks, Nabieva, E., Peterson, R., and Singh, M., NetGrep: fast network schema searches in interactomes., Genome Biol, vol. 9, no. 9, p. R138, 2008.
D. Gresham, Curry, B., Ward, A., D Gordon, B., Brizuela, L., Kruglyak, L., and Botstein, D., Optimized detection of sequence variation in heterozygous genomes using DNA microarrays with isothermal-melting probes., Proc Natl Acad Sci U S A, vol. 107, no. 4, pp. 1482-7, 2010.
M. A. Hibbs, Hess, D. C., Myers, C. L., Huttenhower, C., Li, K., and Troyanskaya, O. G., Exploring the functional landscape of gene expression: directed search of large microarray compendia., Bioinformatics, vol. 23, no. 20, pp. 2692-9, 2007.
J. Zhou, Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., and Troyanskaya, O. G., Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk., Nat Genet, vol. 51, no. 6, pp. 973-980, 2019.
C. Huttenhower, K Mutungu, T., Indik, N., Yang, W., Schroeder, M., Forman, J. J., Troyanskaya, O. G., and Coller, H. A., Detailing regulatory networks through large scale data integration., Bioinformatics, vol. 25, no. 24, pp. 3267-74, 2009.
O. Troyanskaya, Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., and Altman, R. B., Missing value estimation methods for DNA microarrays., Bioinformatics, vol. 17, no. 6, pp. 520-5, 2001.
G. Tkačik, Callan, C. G., and Bialek, W., Information capacity of genetic regulatory elements., Phys Rev E Stat Nonlin Soft Matter Phys, vol. 78, no. 1 Pt 1, p. 011910, 2008.
E. Kruus, Thumfort, P., Tang, C., and Wingreen, N. S., Gibbs sampling and helix-cap motifs., Nucleic Acids Res, vol. 33, no. 16, pp. 5343-53, 2005.
E. Melamud, Vastag, L., and Rabinowitz, J. D., Metabolomic analysis and visualization engine for LC-MS data., Anal Chem, vol. 82, no. 23, pp. 9818-26, 2010.
A. M. Berezhkovskii and Shvartsman, S. Y., Physical interpretation of mean local accumulation time of morphogen gradient formation., J Chem Phys, vol. 135, no. 15, p. 154115, 2011.
M. Coppey, Berezhkovskii, A. M., Kim, Y., Boettiger, A. N., and Shvartsman, S. Y., Modeling the bicoid gradient: diffusion and reversible nuclear trapping of a stable protein., Dev Biol, vol. 312, no. 2, pp. 623-30, 2007.
S. Li, Stone, H. A., and Murphy, C. T., A microfluidic device and automatic counting system for the study of C. elegans reproductive aging., Lab Chip, vol. 15, no. 2, pp. 524-31, 2015.
J. P. Huelsenbeck and Andolfatto, P., Inference of population structure under a Dirichlet process model., Genetics, vol. 175, no. 4, pp. 1787-802, 2007.
R. G. Endres and Wingreen, N. S., Maximum likelihood and the single receptor., Phys Rev Lett, vol. 103, no. 15, p. 158101, 2009.
M. E. Kavousanakis, Kanodia, J. S., Kim, Y., Kevrekidis, I. G., and Shvartsman, S. Y., A compartmental model for the bicoid gradient., Dev Biol, vol. 345, no. 1, pp. 12-7, 2010.
L. Qiao, Nachbar, R. B., Kevrekidis, I. G., and Shvartsman, S. Y., Bistability and oscillations in the Huang-Ferrell model of MAPK signaling., PLoS Comput Biol, vol. 3, no. 9, pp. 1819-26, 2007.
J. Song and Singh, M., How and when should interactome-derived clusters be used to predict functional modules and protein function?, Bioinformatics, vol. 25, no. 23, pp. 3143-50, 2009.
L. Peña-Castillo, Tasan, M., Myers, C. L., Lee, H., Joshi, T., Zhang, C., Guan, Y., Leone, M., Pagnani, A., Kim, W. Kyu, Krumpelman, C., Tian, W., Obozinski, G., Qi, Y., Mostafavi, S., Lin, G. Ning, Berriz, G. F., Gibbons, F. D., Lanckriet, G., Qiu, J., Grant, C., Barutcuoglu, Z., Hill, D. P., Warde-Farley, D., Grouios, C., Ray, D., Blake, J. A., Deng, M., Jordan, M. I., Noble, W. S., Morris, Q., Klein-Seetharaman, J., Bar-Joseph, iv, Z., Chen, T., Sun, F., Troyanskaya, O. G., Marcotte, E. M., Xu, D., Hughes, T. R., and Roth, F. P., A critical assessment of Mus musculus gene function prediction using integrated genomic evidence., Genome Biol, vol. 9 Suppl 1, p. S2, 2008.
C. H. Hansen, Sourjik, V., and Wingreen, N. S., A dynamic-signaling-team model for chemotaxis receptors in Escherichia coli., Proc Natl Acad Sci U S A, vol. 107, no. 40, pp. 17170-5, 2010.
F. Markowetz, Kostka, D., Troyanskaya, O. G., and Spang, R., Nested effects models for high-dimensional phenotyping screens., Bioinformatics, vol. 23, no. 13, pp. i305-12, 2007.
J. S. Bloom, Khan, Z., Kruglyak, L., Singh, M., and Caudy, A. A., Measuring differential gene expression by short read sequencing: quantitative comparison to 2-channel gene expression microarrays., BMC Genomics, vol. 10, p. 221, 2009.
R. G. Endres, Schulthess, T. C., and Wingreen, N. S., Toward an atomistic model for predicting transcription-factor binding sites., Proteins, vol. 57, no. 2, pp. 262-8, 2004.
Z. Khan, Bloom, J. S., Garcia, B. A., Singh, M., and Kruglyak, L., Protein quantification across hundreds of experimental conditions., Proc Natl Acad Sci U S A, vol. 106, no. 37, pp. 15544-8, 2009.
G. Tkačik, Gregor, T., and Bialek, W., The role of input noise in transcriptional regulation., PLoS One, vol. 3, no. 7, p. e2774, 2008.
J. Zhou, Schor, I. E., Yao, V., Theesfeld, C. L., Marco-Ferreres, R., Tadych, A., Furlong, E. E. M., and Troyanskaya, O. G., Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development., PLoS Genet, vol. 15, no. 9, p. e1008382, 2019.
R. Osada, Zaslavsky, E., and Singh, M., Comparative analysis of methods for representing and searching for transcription factor binding sites., Bioinformatics, vol. 20, no. 18, pp. 3516-25, 2004.
I. Nemenman, Lewen, G. D., Bialek, W., and van Steveninck, R. R. de Ruyte, Neural coding of natural stimuli: information at sub-millisecond resolution., PLoS Comput Biol, vol. 4, no. 3, p. e1000025, 2008.
S. Goyal and Wingreen, N. S., Growth-induced instability in metabolic networks., Phys Rev Lett, vol. 98, no. 13, p. 138105, 2007.
C. P. Broedersz, Wang, X., Meir, Y., Loparo, J. J., Rudner, D. Z., and Wingreen, N. S., Condensation and localization of the partitioning protein ParB on the bacterial chromosome., Proc Natl Acad Sci U S A, vol. 111, no. 24, pp. 8809-14, 2014.
O. G. Troyanskaya, Arbell, O., Koren, Y., Landau, G. M., and Bolshoy, A., Sequence complexity profiles of prokaryotic genomic sequences: a fast algorithm for calculating linguistic complexity., Bioinformatics, vol. 18, no. 5, pp. 679-88, 2002.
C. Huttenhower, Schroeder, M., Chikina, M. D., and Troyanskaya, O. G., The Sleipnir library for computational functional genomics., Bioinformatics, vol. 24, no. 13, pp. 1559-61, 2008.
A. V. Rangan, McGrouther, C. C., Kelsoe, J., Schork, N., Stahl, E., Zhu, Q., Krishnan, A., Yao, V., Troyanskaya, O., Bilaloglu, S., Raghavan, P., Bergen, S., Jureus, A., and Landen, M., A loop-counting method for covariate-corrected low-rank biclustering of gene-expression and genome-wide association study data., PLoS Comput Biol, vol. 14, no. 5, p. e1006105, 2018.
C. Huttenhower and Troyanskaya, O. G., Assessing the functional structure of genomic data., Bioinformatics, vol. 24, no. 13, pp. i330-8, 2008.
Y. Guan, Myers, C. L., Hess, D. C., Barutcuoglu, Z., Caudy, A. A., and Troyanskaya, O. G., Predicting gene function in a hierarchical context with an ensemble of classifiers., Genome Biol, vol. 9 Suppl 1, p. S3, 2008.
M. A. Hibbs, Dirksen, N. C., Li, K., and Troyanskaya, O. G., Visualization methods for statistical analysis of microarray clusters., BMC Bioinformatics, vol. 6, p. 115, 2005.
K. M. Chen, Cofer, E. M., Zhou, J., and Troyanskaya, O. G., Selene: a PyTorch-based deep learning library for sequence data., Nat Methods, vol. 16, no. 4, pp. 315-318, 2019.
J. A. Capra and Singh, M., Characterization and prediction of residues determining protein functional specificity., Bioinformatics, vol. 24, no. 13, pp. 1473-80, 2008.
A. Baryshnikova, Costanzo, M., Kim, Y., Ding, H., Koh, J., Toufighi, K., Youn, J. - Y., Ou, J., San Luis, B. - J., Bandyopadhyay, S., Hibbs, M., Hess, D., Gingras, A. - C., Bader, G. D., Troyanskaya, O. G., Brown, G. W., Andrews, B., Boone, C., and Myers, C. L., Quantitative analysis of fitness and genetic interactions in yeast on a genome scale., Nat Methods, vol. 7, no. 12, pp. 1017-24, 2010.
R. M. Cooper, Wingreen, N. S., and Cox, E. C., An excitable cortex and memory model successfully predicts new pseudopod dynamics., PLoS One, vol. 7, no. 3, p. e33528, 2012.
M. D. Chikina, Huttenhower, C., Murphy, C. T., and Troyanskaya, O. G., Global prediction of tissue-specific gene expression and context-dependent gene networks in Caenorhabditis elegans., PLoS Comput Biol, vol. 5, no. 6, p. e1000417, 2009.
C. Huttenhower, Haley, E. M., Hibbs, M. A., Dumeaux, V., Barrett, D. R., Coller, H. A., and Troyanskaya, O. G., Exploring the human genome with functional maps., Genome Res, vol. 19, no. 6, pp. 1093-106, 2009.
T. Mora and Wingreen, N. S., Limits of sensing temporal concentration changes by single cells., Phys Rev Lett, vol. 104, no. 24, p. 248101, 2010.
O. G. Troyanskaya, Integrated analysis of microarray results., Methods Mol Biol, vol. 382, pp. 429-37, 2007.
Z. Barutcuoglu, Airoldi, E. M., Dumeaux, V., Schapire, R. E., and Troyanskaya, O. G., Aneuploidy prediction and tumor classification with heterogeneous hidden conditional random fields., Bioinformatics, vol. 25, no. 10, pp. 1307-13, 2009.
Y. -suk Lee, Wong, A. K., Tadych, A., Hartmann, B. M., Park, C. Y., DeJesus, V. A., Ramos, I., Zaslavsky, E., Sealfon, S. C., and Troyanskaya, O. G., Interpretation of an individual functional genomics experiment guided by massive public data., Nat Methods, vol. 15, no. 12, pp. 1049-1052, 2018.
C. Huttenhower, Hibbs, M. A., Myers, C. L., Caudy, A. A., Hess, D. C., and Troyanskaya, O. G., The impact of incomplete knowledge on evaluation: an experimental benchmark for protein function prediction., Bioinformatics, vol. 25, no. 18, pp. 2404-10, 2009.
O. G. Troyanskaya, Putting microarrays in a context: integrated analysis of diverse biological data., Brief Bioinform, vol. 6, no. 1, pp. 34-43, 2005.
C. Y. Park, Hess, D. C., Huttenhower, C., and Troyanskaya, O. G., Simultaneous genome-wide inference of physical, genetic, regulatory, and functional pathway components., PLoS Comput Biol, vol. 6, no. 11, p. e1001009, 2010.
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R. B. Corbett-Detig, Zhou, J., Clark, A. G., Hartl, D. L., and Ayroles, J. F., Genetic incompatibilities are widespread within species., Nature, vol. 504, no. 7478, pp. 135-7, 2013.
M. S. Bartolomei, Webber, A. L., Brunkow, M. E., and Tilghman, S. M., Epigenetic mechanisms underlying the imprinting of the mouse H19 gene., Genes Dev, vol. 7, no. 9, pp. 1663-73, 1993.
M. J. O'Neill, Ingram, R. S., Vrana, P. B., and Tilghman, S. M., Allelic expression of IGF2 in marsupials and birds., Dev Genes Evol, vol. 210, no. 1, pp. 18-20, 2000.
M. K. Shin, Russell, L. B., and Tilghman, S. M., Molecular characterization of four induced alleles at the Ednrb locus., Proc Natl Acad Sci U S A, vol. 94, no. 24, pp. 13105-10, 1997.
J. P. Huelsenbeck and Andolfatto, P., Inference of population structure under a Dirichlet process model., Genetics, vol. 175, no. 4, pp. 1787-802, 2007.
N. S. Tolwinski and Wieschaus, E., A nuclear function for armadillo/beta-catenin., PLoS Biol, vol. 2, no. 4, p. E95, 2004.
S. B. Zusman, Sweeton, D., and Wieschaus, E. F., short gastrulation, a mutation causing delays in stage-specific cell shape changes during gastrulation in Drosophila melanogaster., Dev Biol, vol. 129, no. 2, pp. 417-27, 1988.
T. P. O'Brien, Metallinos, D. L., Chen, H., Shin, M. K., and Tilghman, S. M., Complementation mapping of skeletal and central nervous system abnormalities in mice of the piebald deletion complex., Genetics, vol. 143, no. 1, pp. 447-61, 1996.
C. D. van Raamsdonk and Tilghman, S. M., Dosage requirement and allelic expression of PAX6 during lens placode formation., Development, vol. 127, no. 24, pp. 5439-48, 2000.
alpha-Fetoproteins
R. Godbout, Ingram, R. S., and Tilghman, S. M., Fine-structure mapping of the three mouse alpha-fetoprotein gene enhancers., Mol Cell Biol, vol. 8, no. 3, pp. 1169-78, 1988.
J. H. Millonig, Emerson, J. A., Levorse, J. M., and Tilghman, S. M., Molecular analysis of the distal enhancer of the mouse alpha-fetoprotein gene., Mol Cell Biol, vol. 15, no. 7, pp. 3848-56, 1995.
H. Yoo-Warren, Pachnis, V., Ingram, R. S., and Tilghman, S. M., Two regulatory domains flank the mouse H19 gene., Mol Cell Biol, vol. 8, no. 11, pp. 4707-15, 1988.
J. Vacher and Tilghman, S. M., Dominant negative regulation of the mouse alpha-fetoprotein gene in adult liver., Science, vol. 250, no. 4988, pp. 1732-5, 1990.
J. A. Emerson, Vacher, J., Cirillo, L. A., Tilghman, S. M., and Tyner, A. L., The zonal expression of alpha-fetoprotein transgenes in the livers of adult mice., Dev Dyn, vol. 195, no. 1, pp. 55-66, 1992.
B. T. Spear and Tilghman, S. M., Role of alpha-fetoprotein regulatory elements in transcriptional activation in transient heterokaryons., Mol Cell Biol, vol. 10, no. 10, pp. 5047-54, 1990.
M. H. Feuerman, Godbout, R., Ingram, R. S., and Tilghman, S. M., Tissue-specific transcription of the mouse alpha-fetoprotein gene promoter is dependent on HNF-1., Mol Cell Biol, vol. 9, no. 10, pp. 4204-12, 1989.
J. Vacher, Camper, S. A., Krumlauf, R., Compton, R. S., and Tilghman, S. M., raf regulates the postnatal repression of the mouse alpha-fetoprotein gene at the posttranscriptional level., Mol Cell Biol, vol. 12, no. 2, pp. 856-64, 1992.
R. Godbout and Tilghman, S. M., Configuration of the alpha-fetoprotein regulatory domain during development., Genes Dev, vol. 2, no. 8, pp. 949-56, 1988.
S. A. Camper and Tilghman, S. M., Postnatal repression of the alpha-fetoprotein gene is enhancer independent., Genes Dev, vol. 3, no. 4, pp. 537-46, 1989.
A. L. Tyner, Godbout, R., Compton, R. S., and Tilghman, S. M., The ontogeny of alpha-fetoprotein gene expression in the mouse gastrointestinal tract., J Cell Biol, vol. 110, no. 4, pp. 915-27, 1990.
X. J. Guan, Arhin, G., Leung, J., and Tilghman, S. M., Linkage between vitamin D-binding protein and alpha-fetoprotein in the mouse., Mamm Genome, vol. 7, no. 2, pp. 103-6, 1996.
Amidinotransferases
L. L. Sandell, Guan, X. - J., Ingram, R., and Tilghman, S. M., Gatm, a creatine synthesis enzyme, is imprinted in mouse placenta., Proc Natl Acad Sci U S A, vol. 100, no. 8, pp. 4622-7, 2003.
Amino Acid Motifs
J. H. Fong, Keating, A. E., and Singh, M., Predicting specificity in bZIP coiled-coil protein interactions., Genome Biol, vol. 5, no. 2, p. R11, 2004.
J. Miller, Zeng, C., Wingreen, N. S., and Tang, C., Emergence of highly designable protein-backbone conformations in an off-lattice model., Proteins, vol. 47, no. 4, pp. 506-12, 2002.
C. Hunter, Sung, P., Schejter, E. D., and Wieschaus, E., Conserved domains of the Nullo protein required for cell-surface localization and formation of adherens junctions., Mol Biol Cell, vol. 13, no. 1, pp. 146-57, 2002.
E. Kruus, Thumfort, P., Tang, C., and Wingreen, N. S., Gibbs sampling and helix-cap motifs., Nucleic Acids Res, vol. 33, no. 16, pp. 5343-53, 2005.
A. Stathopoulos and Levine, M., Localized repressors delineate the neurogenic ectoderm in the early Drosophila embryo., Dev Biol, vol. 280, no. 2, pp. 482-93, 2005.
S. Weng, Dong, Q., Balakrishnan, R., Christie, K., Costanzo, M., Dolinski, K., Dwight, S. S., Engel, S., Fisk, D. G., Hong, E., Issel-Tarver, L., Sethuraman, A., Theesfeld, C., Andrada, R., Binkley, G., Lane, C., Schroeder, M., Botstein, D., and J Cherry, M., Saccharomyces Genome Database (SGD) provides biochemical and structural information for budding yeast proteins., Nucleic Acids Res, vol. 31, no. 1, pp. 216-8, 2003.
P. Jiang, Singh, M., and Coller, H. A., Computational assessment of the cooperativity between RNA binding proteins and MicroRNAs in Transcript Decay., PLoS Comput Biol, vol. 9, no. 5, p. e1003075, 2013.
Amino Acid Sequence
L. R. Swem, Swem, D. L., Wingreen, N. S., and Bassler, B. L., Deducing receptor signaling parameters from in vivo analysis: LuxN/AI-1 quorum sensing in Vibrio harveyi., Cell, vol. 134, no. 3, pp. 461-73, 2008.
C. Rauskolb, Peifer, M., and Wieschaus, E., extradenticle, a regulator of homeotic gene activity, is a homolog of the homeobox-containing human proto-oncogene pbx1., Cell, vol. 74, no. 6, pp. 1101-12, 1993.
C. L. Kingsford, Chazelle, B., and Singh, M., Solving and analyzing side-chain positioning problems using linear and integer programming., Bioinformatics, vol. 21, no. 7, pp. 1028-36, 2005.
K. R. Christie, Weng, S., Balakrishnan, R., Costanzo, M. C., Dolinski, K., Dwight, S. S., Engel, S. R., Feierbach, B., Fisk, D. G., Hirschman, J. E., Hong, E. L., Issel-Tarver, L., Nash, R., Sethuraman, A., Starr, B., Theesfeld, C. L., Andrada, R., Binkley, G., Dong, Q., Lane, C., Schroeder, M., Botstein, D., and J Cherry, M., Saccharomyces Genome Database (SGD) provides tools to identify and analyze sequences from Saccharomyces cerevisiae and related sequences from other organisms., Nucleic Acids Res, vol. 32, no. Database issue, pp. D311-4, 2004.
L. J. Kurihara, Semenova, E., Levorse, J. M., and Tilghman, S. M., Expression and functional analysis of Uch-L3 during mouse development., Mol Cell Biol, vol. 20, no. 7, pp. 2498-504, 2000.
B. Riggleman, Wieschaus, E., and Schedl, P., Molecular analysis of the armadillo locus: uniformly distributed transcripts and a protein with novel internal repeats are associated with a Drosophila segment polarity gene., Genes Dev, vol. 3, no. 1, pp. 96-113, 1989.
M. Peifer and Wieschaus, E., The segment polarity gene armadillo encodes a functionally modular protein that is the Drosophila homolog of human plakoglobin., Cell, vol. 63, no. 6, pp. 1167-76, 1990.
A. Ochoa, Llinás, M., and Singh, M., Using context to improve protein domain identification., BMC Bioinformatics, vol. 12, p. 90, 2011.
L. S. Rose and Wieschaus, E., The Drosophila cellularization gene nullo produces a blastoderm-specific transcript whose levels respond to the nucleocytoplasmic ratio., Genes Dev, vol. 6, no. 7, pp. 1255-68, 1992.
S. Parks and Wieschaus, E., The Drosophila gastrulation gene concertina encodes a G alpha-like protein., Cell, vol. 64, no. 2, pp. 447-58, 1991.
J. Doran Cande, Chopra, V. S., and Levine, M., Evolving enhancer-promoter interactions within the tinman complex of the flour beetle, Tribolium castaneum., Development, vol. 136, no. 18, pp. 3153-60, 2009.
D. Munro, Ghersi, D., and Singh, M., Two critical positions in zinc finger domains are heavily mutated in three human cancer types., PLoS Comput Biol, vol. 14, no. 6, p. e1006290, 2018.
C. Hunter, Sung, P., Schejter, E. D., and Wieschaus, E., Conserved domains of the Nullo protein required for cell-surface localization and formation of adherens junctions., Mol Biol Cell, vol. 13, no. 1, pp. 146-57, 2002.
J. F. Ayroles, Carbone, M. Anna, Stone, E. A., Jordan, K. W., Lyman, R. F., Magwire, M. M., Rollmann, S. M., Duncan, L. H., Lawrence, F., Anholt, R. R. H., and Mackay, T. F. C., Systems genetics of complex traits in Drosophila melanogaster., Nat Genet, vol. 41, no. 3, pp. 299-307, 2009.
J. A. Capra and Singh, M., Predicting functionally important residues from sequence conservation., Bioinformatics, vol. 23, no. 15, pp. 1875-82, 2007.
Z. Khan, Amini, S., Bloom, J. S., Ruse, C., Caudy, A. A., Kruglyak, L., Singh, M., Perlman, D. H., and Tavazoie, S., Accurate proteome-wide protein quantification from high-resolution 15N mass spectra., Genome Biol, vol. 12, no. 12, p. R122, 2011.
M. Peifer and Wieschaus, E., The product of the Drosophila melanogaster segment polarity gene armadillo is highly conserved in sequence and expression in the housefly Musca domestica., J Mol Evol, vol. 36, no. 3, pp. 224-33, 1993.
J. A. Zallen, Cohen, Y., Hudson, A. M., Cooley, L., Wieschaus, E., and Schejter, E. D., SCAR is a primary regulator of Arp2/3-dependent morphological events in Drosophila., J Cell Biol, vol. 156, no. 4, pp. 689-701, 2002.
M. Costa, Wilson, E. T., and Wieschaus, E., A putative cell signal encoded by the folded gastrulation gene coordinates cell shape changes during Drosophila gastrulation., Cell, vol. 76, no. 6, pp. 1075-89, 1994.
P. B. Vrana, Matteson, P. G., Schmidt, J. V., Ingram, R. S., Joyce, A., Prince, K. L., Dewey, M. J., and Tilghman, S. M., Genomic imprinting of a placental lactogen gene in Peromyscus., Dev Genes Evol, vol. 211, no. 11, pp. 523-32, 2001.
Y. Zhen, Aardema, M. L., Medina, E. M., Schumer, M., and Andolfatto, P., Parallel molecular evolution in an herbivore community., Science, vol. 337, no. 6102, pp. 1634-7, 2012.
E. S. Click, Stearns, T., and Botstein, D., Systematic structure-function analysis of the small GTPase Arf1 in yeast., Mol Biol Cell, vol. 13, no. 5, pp. 1652-64, 2002.
A. Bejsovec and Wieschaus, E., Signaling activities of the Drosophila wingless gene are separately mutable and appear to be transduced at the cell surface., Genetics, vol. 139, no. 1, pp. 309-20, 1995.
D. H. Lenz, Mok, K. C., Lilley, B. N., Kulkarni, R. V., Wingreen, N. S., and Bassler, B. L., The small RNA chaperone Hfq and multiple small RNAs control quorum sensing in Vibrio harveyi and Vibrio cholerae., Cell, vol. 118, no. 1, pp. 69-82, 2004.
S. Hayashi, Rubinfeld, B., Souza, B., Polakis, P., Wieschaus, E., and Levine, A. J., A Drosophila homolog of the tumor suppressor gene adenomatous polyposis coli down-regulates beta-catenin but its zygotic expression is not essential for the regulation of Armadillo., Proc Natl Acad Sci U S A, vol. 94, no. 1, pp. 242-7, 1997.
A. V. Persikov, Osada, R., and Singh, M., Predicting DNA recognition by Cys2His2 zinc finger proteins., Bioinformatics, vol. 25, no. 1, pp. 22-9, 2009.
T. M. Bartlett, Bratton, B. P., Duvshani, A., Miguel, A., Sheng, Y., Martin, N. R., Nguyen, J. P., Persat, A., Desmarais, S. M., VanNieuwenhze, M. S., Huang, K. Casey, Zhu, J., Shaevitz, J. W., and Gitai, Z., A Periplasmic Polymer Curves Vibrio cholerae and Promotes Pathogenesis., Cell, vol. 168, no. 1-2, pp. 172-185.e15, 2017.
D. E. Coulter, Swaykus, E. A., Beran-Koehn, M. A., Goldberg, D., Wieschaus, E., and Schedl, P., Molecular analysis of odd-skipped, a zinc finger encoding segmentation gene with a novel pair-rule expression pattern., EMBO J, vol. 9, no. 11, pp. 3795-804, 1990.
P. Armand, Knapp, A. C., Hirsch, A. J., Wieschaus, E. F., and Cole, M. D., A novel basic helix-loop-helix protein is expressed in muscle attachment sites of the Drosophila epidermis., Mol Cell Biol, vol. 14, no. 6, pp. 4145-54, 1994.
C. I. Brannan, Dees, E. C., Ingram, R. S., and Tilghman, S. M., The product of the H19 gene may function as an RNA., Mol Cell Biol, vol. 10, no. 1, pp. 28-36, 1990.
T. Mora, Walczak, A. M., Bialek, W., and Callan, C. G., Maximum entropy models for antibody diversity., Proc Natl Acad Sci U S A, vol. 107, no. 12, pp. 5405-10, 2010.
E. G. Emberly, Wingreen, N. S., and Tang, C., Designability of alpha-helical proteins., Proc Natl Acad Sci U S A, vol. 99, no. 17, pp. 11163-8, 2002.
J. Grosshans and Wieschaus, E., A genetic link between morphogenesis and cell division during formation of the ventral furrow in Drosophila., Cell, vol. 101, no. 5, pp. 523-31, 2000.
M. Peifer, McCrea, P. D., Green, K. J., Wieschaus, E., and Gumbiner, B. M., The vertebrate adhesive junction proteins beta-catenin and plakoglobin and the Drosophila segment polarity gene armadillo form a multigene family with similar properties., J Cell Biol, vol. 118, no. 3, pp. 681-91, 1992.
E. D. Schejter and Wieschaus, E., bottleneck acts as a regulator of the microfilament network governing cellularization of the Drosophila embryo., Cell, vol. 75, no. 2, pp. 373-85, 1993.
D. J. Brooks, Fresco, J. R., Lesk, A. M., and Singh, M., Evolution of amino acid frequencies in proteins over deep time: inferred order of introduction of amino acids into the genetic code., Mol Biol Evol, vol. 19, no. 10, pp. 1645-55, 2002.
K. D. Irvine and Wieschaus, E., fringe, a Boundary-specific signaling molecule, mediates interactions between dorsal and ventral cells during Drosophila wing development., Cell, vol. 79, no. 4, pp. 595-606, 1994.
J. A. Capra and Singh, M., Characterization and prediction of residues determining protein functional specificity., Bioinformatics, vol. 24, no. 13, pp. 1473-80, 2008.
Amino Acid Substitution
R. S. Dwyer, Ricci, D. P., Colwell, L. J., Silhavy, T. J., and Wingreen, N. S., Predicting functionally informative mutations in Escherichia coli BamA using evolutionary covariance analysis., Genetics, vol. 195, no. 2, pp. 443-55, 2013.
K. Thornton, Bachtrog, D., and Andolfatto, P., X chromosomes and autosomes evolve at similar rates in Drosophila: no evidence for faster-X protein evolution., Genome Res, vol. 16, no. 4, pp. 498-504, 2006.
M. J. Hickman, Petti, A. A., Ho-Shing, O., Silverman, S. J., R McIsaac, S., Lee, T. A., and Botstein, D., Coordinated regulation of sulfur and phospholipid metabolism reflects the importance of methylation in the growth of yeast., Mol Biol Cell, vol. 22, no. 21, pp. 4192-204, 2011.
W. - L. Ng, Wei, Y., Perez, L. J., Cong, J., Long, T., Koch, M., Semmelhack, M. F., Wingreen, N. S., and Bassler, B. L., Probing bacterial transmembrane histidine kinase receptor-ligand interactions with natural and synthetic molecules., Proc Natl Acad Sci U S A, vol. 107, no. 12, pp. 5575-80, 2010.
M. L. Aardema, Zhen, Y., and Andolfatto, P., The evolution of cardenolide-resistant forms of Na⁺,K⁺ -ATPase in Danainae butterflies., Mol Ecol, vol. 21, no. 2, pp. 340-9, 2012.
Y. Zhen, Aardema, M. L., Medina, E. M., Schumer, M., and Andolfatto, P., Parallel molecular evolution in an herbivore community., Science, vol. 337, no. 6102, pp. 1634-7, 2012.
P. Andolfatto, Hitchhiking effects of recurrent beneficial amino acid substitutions in the Drosophila melanogaster genome., Genome Res, vol. 17, no. 12, pp. 1755-62, 2007.
B. Callahan, Neher, R. A., Bachtrog, D., Andolfatto, P., and Shraiman, B. I., Correlated evolution of nearby residues in Drosophilid proteins., PLoS Genet, vol. 7, no. 2, p. e1001315, 2011.

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