Algorithms; Bacteria; Bacterial Infections and Mycoses; Beer; Bread; Cell Transformation, Neoplastic; Coccidioidomycosis; Computing Methodologies; Biological Evolution; Fungi; Genetic Engineering; Microbiological Phenomena; Models, Genetic; Models, Theoretical; Mycoses; Neoplasm Metastasis; Neoplasms; Phylogeny; Viruses; Wine; Models, Statistical; Likelihood Functions; Logistic Models; Polymerase Chain Reaction; Sequence Analysis, DNA; Nonlinear Dynamics; Molecular Epidemiology; Gene Transfer Techniques; Crops, Agricultural; Evolution, Molecular; Nature; Sequence Analysis, Protein; Gene Expression Profiling; Public Health Informatics; Microarray Analysis; Genetic Speciation; Host-Pathogen Interactions; Genetic Phenomena; Mathematical Concepts; Metabolic Phenomena; Organisms; Phenomena and Processes
Public Health Interests
Antibiotics Resistance; Asthma; Avian Flu; Bacterial genetics & evolution; Bioinformatics; Biostatistics; Cancer genetics; Evolution; Flu; Genetics; Genomics; Hepatitis; HIV/AIDS; HPV; Human genetic data; Infectious Disease; Infectious Disease Transmission; Influenza; Lyme Disease; Metabolism; Microarray; Microbial Ecology; Modeling; Policy & regulation; Statistical genetics; Statistical models; Tropical Diseases; Zoonosis
1. BIOINFORMATIC TOOLS FOR CANCER GENETICS AND EPIDEMIOLOGY
Whole-exome sequencing has created tremendous potential for revealing the genetic basis and underlying molecular mechanisms of many forms of cancer. However, somatic mutations occur at a significant frequency within tumors of most cancer types, and identification of the mutations that are on the causative trajectory from normal tissue to cancerous tissue is challenging. We are making algorithmic advances in clustering across discrete linear sequences that facilitate maximum likelihood inference of model-averaged clustering in discrete linear sequences of somatic amino acid replacement mutations appearing within mutated genes, and applying evolutionary theory to the repeated evolution of cancer in whole-exome sequence data sets to reveal the level of clonal natural selection for cancer drivers.
2. BIOSTATISTICAL ANALYSIS FOR NONLINEAR MATHEMATICAL MODELS OF THE EPIDEMIOLOGY OF DISEASE
I am developing probabilistic statistical methodologies for the mathematical modeling of disease emergence and spread. For diverse reasons, data for estimation of epidemiological parameters is often sparse. Evaluating a model with the “best point estimate” of sparse data may convey a misleading certitude to policy makers basing decisions on deterministic models of disease outbreak, spread, and persistence. Conversely, policy makers who are aware that models are parameterized with limited data may be dismissive of deterministic predictions that yet have significant validity. We address these issues by probabilistic sensitivity analysis of parameters and full uncertainty analysis of outcomes of interest.
Extensive Research Description
1. TOOLS FOR CANCER GENETICS AND EPIDEMIOLOGY
Whole-exome sequencing has created tremendous potential for revealing the genetic basis and underlying molecular mechanisms of many forms of cancer. However, somatic mutations occur at a significant frequency within tumors of most cancer types, and identification of the mutations that are on the causative trajectory from normal tissue to cancerous tissue is challenging. We are making algorithmic advances in clustering across discrete linear sequences to enact two powerful approaches to this identification. First, we are applying maximum likelihood approaches that we have developed for model-averaged clustering in discrete linear sequences to somatic amino acid replacement mutations appearing within mutated genes. Because amino acids of proteins that are functionally important are locally clustered in domains, mutations in multiple tumors that are functionally important to the development of cancer cluster in the linear sequence of relevant genes, allowing inference of relevance and function even in cases without three-dimensional protein structure. These clustering analyses have the power to demonstrate, for instance, cross-cancer consistency in the functional importance of the DNA binding domain of tumor suppressor p53, whether in a cancer with extensive exome data (ovarian serous adenocarcinoma) or in a cancer with much less extensive exome data (e.g. rectal adenocarcinoma).
Second, we are applying evolutionary theory to the problem of identification of the genetic architecture of underlying cancer development. The path from normal to cancerous tissue is navigated by an evolutionary process. Tools from evolutionary theory have the potential to parse those mutations that are selected within cells on the path to cancer from those mutations that arise incidentally during the somatic evolution of cancer. The theory we are applying makes use of differences in expectation for synonymous and replacement mutations. Synonymous mutations are expected to have no functional impact; thus they yield a proxy expectation for the “incidental” mutations, whereas carcinogenic replacement mutations will spread within tumors more frequently and are clustered within gene sequence. Our theory also employs human population polymorphism data, which most evolutionary biologists believe can be largely assumed to be neutral. This data facilitates calibration of the probable impact of replacement changes to sequence conservation by eliminating the confounding variable of the degree of purifying selection, which decreases the number of mutations observed in some genes and allows others to accumulate many mutations with little impact.
We are extending this approach to estimating selection intensity on mutations along the trajectory toward cancer, revealing the level of selection within tumors for replacement mutations compared to synonymous mutations. This evolutionary analysis is ideal for detecting the history of selection on sites within genes during the evolution of cancer from exome sequencing data. These sites, particularly when representing gain-of-function mutations, will help identify candidate loci for pharmacological intervention. This approach will be applied to identify targets for pharmacological intervention and design “personal genomics” drugs appropriate for the genetics of individual cancers in individual patients. As a component of that project, we are constructing an “active-experiment” cancer exome database to facilitate further bioinformatics investigation of cancer exome data.
2. BIOSTATISTICAL ANALYSIS FOR NONLINEAR MATHEMATICAL MODELS OF THE EPIDEMIOLOGY OF DISEASE
I am developing probabilistic statistical methodologies for the mathematical modeling of disease emergence and spread. Robustness of models has usually been assessed by techniques that explore the relative impact and importance of parameters upon the mathematical behavior of the function and the mathematical predictions of the model. For diverse reasons including the difficulty or cost of acquisition, restrictions due to privacy, and urgency of analysis in the case of outbreaks, data for estimation of epidemiological parameters is often sparse. Evaluating a model with the “best point estimate” of sparse data may convey a misleading certitude to policy makers basing decisions on deterministic models of disease outbreak, spread, and persistence. Conversely, policy makers who are aware that models are parameterized with limited data may be dismissive of deterministic predictions that yet have significant validity. These issues may be most straightforwardly addressed by probabilistic sensitivity analysis of parameters and full uncertainty analysis of outcomes of interest. These analyses amount to accommodating the uncertainty of parameters directly into an analysis by probabilistically resampling data or likely distributions of parameters to calculate a probabilistic distribution of outcomes.
For instance, one of the most common modeling approaches for evaluating interventions is based on differential equation models of disease such as the standard Susceptible-Infected-Recovered (SIR) model. In the SIR model and other more complex constructions, a closed-form solution can often be calculated for the basic reproductive number, R0, the average number of secondary infections that would follow upon a primary infection in a naïve host population. In a population where there is preexisting immunity due to either vaccination or previous infection, the effective reproductive number, Re, is defined as the average number of secondary infections following a primary infection in a population that is not completely naïve.
is of particular interest in public health because interventions that bring its value below 1 are predicted to eradicate the disease. This deterministic threshold of is proposed as the basis for policy decisions regarding the level of interventions that should be implemented. However, the best estimates for the parameters that are needed for the closed-form solution of are inevitably inexact. To address this point, sensitivity analyses are frequently performed to evaluate models and explore the relationship between model parameters and outcomes. In such deterministic sensitivity analyses, one or more parameters are perturbed and the corresponding effects on outcomes are examined. The perturbation can be done either by evaluating the effect of arbitrarily small changes in parameter values (e.g. ± 1%) or by evaluating the effects across a range of values defined by plausible probability density functions. Because the values of other parameters are held fixed at best point estimates, these strategies do not account for interaction effects in non-linear dynamic models, and do not assess global uncertainty in outcome. Uncertainty analysis has been recommended for many fields of mathematical modeling, including medical decision making, as an optimal approach to presenting models. In the case of dynamic transmission modeling, however, authoritative best practices have not included uncertainty analyses. Modeling guidelines recommend probabilistic sensitivity analysis, in which both global parameter uncertainty and output uncertainty are addressed, as the best practice method for uncertainty analysis. Yet that ideal has not been extended to dynamic transmission models, for which its implementation has been challenging.
We are developing methods for global probabilistic sensitivity analysis that allow the contribution of each parameter to model outcomes to be investigated while also taking into account the uncertainty of other model parameters. Uncertainty in parameter values can be accounted for by sampling randomly from empirical data or from probability density functions fit to empirical data. Depending on the instance, such sampling techniques include bootstrapping, Monte Carlo sampling, and Latin hypercube sampling. The model output generated from parameter samples can then be analyzed using linear (e.g. partial correlation coefficients), monotonic (e.g. partial rank correlation coefficients) and non-monotonic statistical tests (e.g. sensitivity index) to determine the contribution of each parameter to the variation in output values. Indeed, for a global sensitivity analysis to yield probabilities associated with outcomes that are of greatest utility to policy makers, probabilistic analyses of parameter uncertainty must be carried through to the model outcomes. For example, the probability of eradication of an epidemic is sensitive to both levels of vaccination and treatment. Moreover, a policy based on the analysis of data should take into consideration not only the best estimate of necessary action, but also the uncertainty around that outcome estimate. The former policy advice, indicating an exact cline of treatment and vaccination that should put into abeyance an influenza epidemic, is very different and can be misleading compared to the probabilistic statement, which gives a policymaker a predictive probability that a particular policy of treatment and vaccination will put into abeyance an influenza epidemic. Similar approaches applied with a next-generation matrix to rabies vaccination in Tanzania were able to demonstrate that WHO goals in two districts of 70% vaccination coverage of dogs had more than enough probability to control rabies, if only the process to achieve those not impractical goals could be mustered.
A public health decision maker would find most useful the assignment of the probability of eradication to each level of treatment, so that they may precisely weigh the cost of intervention against the potential for failure. These probabilistic outcome distributions also feed forward extremely fluidly with cost-effectiveness estimation, a field which has embraced uncertainty analysis but which has until our recent work not incorporated uncertainty from nonlinear infectious disease models into calculations.
We have many projects ongoing in the lab, covering topics summarized below, including many we have already published on and many that we have not. In particular, we have a lot of projects on the somatic evolution of cancer that are not yet in publications.
Early and multiple origins of metastatic lineages within primary tumors
Zhao ZM, Zhao B, Bai Y, Iamarino A, Gaffney SG, Schlessinger J, Lifton RP, Rimm DL, Townsend JP. Proc Natl Acad Sci U S A. 2016 Feb 23;113(8):2140-5.
The Impact of Enhanced Screening and Treatment on Hepatitis C in the United States
Durham DP, Skrip LA, Bruce RD, Vilarinho S, Elbasha EH, Galvani AP, Townsend JP. Clin Infect Dis. 2016 Feb 1;62(3):298-304.
Inferring the origin of metastases from cancer phylogenies
Hong W.S., M. Shpak, J.P. Townsend, 2015. Cancer Research 75(19):4021–4025
Impact of bed capacity on spatiotemporal shifts in Ebola transmission
Townsend JP, Skrip LA, Galvani AP. Proc Natl Acad Sci U S A. 2015 Nov 17;112(46):14125-6.
A comprehensive phylogeny of birds (Aves) using targeted next-generation DNA sequencing
Prum RO, Berv JS, Dornburg A, Field DJ, Townsend JP, Lemmon EM, Lemmon AR. Nature. 2015 Oct 22;526(7574):569-73.
Epidemiological and viral genomic sequence analysis of the 2014 ebola outbreak reveals clustered transmission.
Scarpino SV, Iamarino A, Wells C, Yamin D, Ndeffo-Mbah M, Wenzel NS, Fox SJ, Nyenswah T, Altice FL, Galvani AP, Meyers LA and Townsend JP, 2015. Clinical Infectious Diseases 60(7):1079-1082.
Gene expression evolves under a House-of-Cards model of stabilizing selection
Hodgins-Davis A., D.P. Rice, and J.P. Townsend, 2015. Molecular Biology and Evolution 32(8): 2130–2140.
Probabilistic uncertainty analysis of epidemiological modeling to guide public health intervention policy
Gilbert, J.A., L.A. Meyers, A.P. Galvani, and J.P. Townsend (2014). Probabilistic uncertainty analysis of epidemiological modeling to guide public health intervention policy. Epidemics 6: 37-45.
Yeast response to LA virus indicates coadapted global gene expression during mycoviral infection.
McBride, R. C., Boucher, N., Park, D. S., Turner, P. E., & Townsend, J. P. (2013). Yeast response to LA virus indicates coadapted global gene expression during mycoviral infection. FEMS yeast research, 13(2), 162-179.
Phylogenetic signal and noise: predicting the power of a dataset to resolve phylogeny.
Townsend J.P., Z. Su, and Y.I. Tekle (2012). Phylogenetic signal and noise: predicting the power of a dataset to resolve phylogeny. Systematic Biology 61(5): 835-849.
Assessing the probability of detection of horizontal gene transfer events in bacterial populations.
Townsend, J. P., Bøhn, T., & Nielsen, K. M. (2012). Assessing the probability of detection of horizontal gene transfer events in bacterial populations. Frontiers in microbiology, 3.
LOX: inferring Level Of eXpression from diverse methods of census sequencing.
Zhang, Z., López-Giráldez, F., & Townsend, J. P. (2010). LOX: inferring Level Of eXpression from diverse methods of census sequencing. Bioinformatics, 26(15), 1918-1919. Chicago
Maximum-likelihood model averaging to profile clustering of site types across discrete linear sequences.
Zhang, Z., & Townsend, J. P. (2009). Maximum-likelihood model averaging to profile clustering of site types across discrete linear sequences. PLoS computational biology, 5(6), e1000421.
Bringing Web 2.0 to bioinformatics.
Zhang, Z., Cheung, K. H., & Townsend, J. P. (2009). Bringing Web 2.0 to bioinformatics. Briefings in bioinformatics, 10(1), 1-10.
Bayesian analysis of gene expression levels: statistical quantification of relative mRNA level across multiple strains or treatments
Townsend, J. P., & Hartl, D. L. (2002). Genome Biol, 3(12), 0071.
Full List of PubMed Publications
- Park B, Rutter MT, Fenster CB, Symonds VV, Ungerer MC, Townsend JP: Distributions of Mutational Effects and the Estimation of Directional Selection in Divergent Lineages of Arabidopsis thaliana. Genetics. 2017 May 26; 2017 May 26. PMID: 28550014
- Chen WS, Townsend JP, Yu JB: Radiation-Specific Clinical Data Should Be Included in Existing Large-Scale Genomic Datasets. Int J Radiat Oncol Biol Phys. 2017 May 1; 2017 Jan 16. PMID: 28587055
- Dornburg A, Townsend JP, Brooks W, Spriggs E, Eytan RI, Moore JA, Wainwright PC, Lemmon A, Lemmon EM, Near TJ: New insights on the sister lineage of percomorph fishes with an anchored hybrid enrichment dataset. Mol Phylogenet Evol. 2017 May; 2017 Feb 27. PMID: 28254474
- Somarelli JA, Ware KE, Kostadinov R, Robinson JM, Amri H, Abu-Asab M, Fourie N, Diogo R, Swofford D, Townsend JP: PhyloOncology: Understanding cancer through phylogenetic analysis. Biochim Biophys Acta. 2017 Apr; 2016 Oct 31. PMID: 27810337
- Fitzpatrick MC, Shah HA, Pandey A, Bilinski AM, Kakkar M, Clark AD, Townsend JP, Abbas SS, Galvani AP: One Health approach to cost-effective rabies control in India. Proc Natl Acad Sci U S A. 2016 Dec 20. PMID: 27994161
- Lewnard JA, Townsend JP: Climatic and evolutionary drivers of phase shifts in the plague epidemics of colonial India. Proc Natl Acad Sci U S A. 2016 Dec 20; 2016 Oct 24. PMID: 27791071
- Gaffney SG, Townsend JP: PathScore: a web tool for identifying altered pathways in cancer data. Bioinformatics. 2016 Dec 1; 2016 Aug 8. PMID: 27503224
- Dornburg A, Fisk JN, Tamagnan J, Townsend JP: PhyInformR: phylogenetic experimental design and phylogenomic data exploration in R. BMC Evol Biol. 2016 Dec 1; 2016 Dec 1. PMID: 27905871
- Yamin D, Jones FK, DeVincenzo JP, Gertler S, Kobiler O, Townsend JP, Galvani AP: Vaccination strategies against respiratory syncytial virus. Proc Natl Acad Sci U S A. 2016 Nov 15; 2016 Oct 31. PMID: 27799521
- Fitzpatrick MC, Wenzel NS, Scarpino SV, Althouse BM, Atkins KE, Galvani AP, Townsend JP: Cost-effectiveness of next-generation vaccines: The case of pertussis. Vaccine. 2016 Jun 17; 2016 Apr 14. PMID: 27087151
- Atkins KE, Pandey A, Wenzel NS, Skrip L, Yamin D, Nyenswah TG, Fallah M, Bawo L, Medlock J, Altice FL, Townsend J, Ndeffo-Mbah ML, Galvani AP: Retrospective Analysis of the 2014-2015 Ebola Epidemic in Liberia. Am J Trop Med Hyg. 2016 Apr; 2016 Feb 29. PMID: 26928839
- Wang Z, Li N, Li J, Dunlap JC, Trail F, Townsend JP: The Fast-Evolving phy-2 Gene Modulates Sexual Development in Response to Light in the Model Fungus Neurospora crassa. MBio. 2016 Mar 8; 2016 Mar 8. PMID: 26956589
- Gilbert JA, Medlock J, Townsend JP, Aksoy S, Ndeffo Mbah M, Galvani AP: Determinants of Human African Trypanosomiasis Elimination via Paratransgenesis. PLoS Negl Trop Dis. 2016 Mar; 2016 Mar 8. PMID: 26954675
- Zhao ZM, Zhao B, Bai Y, Iamarino A, Gaffney SG, Schlessinger J, Lifton RP, Rimm DL, Townsend JP: Early and multiple origins of metastatic lineages within primary tumors. Proc Natl Acad Sci U S A. 2016 Feb 23; 2016 Feb 8. PMID: 26858460
- Durham DP, Skrip LA, Bruce RD, Vilarinho S, Elbasha EH, Galvani AP, Townsend JP: The Impact of Enhanced Screening and Treatment on Hepatitis C in the United States. Clin Infect Dis. 2016 Feb 1; 2015 Nov 30. PMID: 26628566
- Alfaro-Murillo JA, Townsend JP, Galvani AP: Optimizing age of cytomegalovirus screening and vaccination to avert congenital disease in the US. Vaccine. 2016 Jan 4; 2015 Nov 27. PMID: 26631416
- Sistrom M, Park D, O'Brien HE, Wang Z, Guttman DS, Townsend JP, Turner PE: Genomic and Gene-Expression Comparisons among Phage-Resistant Type-IV Pilus Mutants of Pseudomonas syringae pathovar phaseolicola. PLoS One. 2015; 2015 Dec 15. PMID: 26670219
- Hollingsworth TD, Adams ER, Anderson RM, Atkins K, Bartsch S, Basáñez MG, Behrend M, Blok DJ, Chapman LA, Coffeng L, Courtenay O, Crump RE, de Vlas SJ, Dobson A, Dyson L, Farkas H, Galvani AP, Gambhir M, Gurarie D, Irvine MA, Jervis S, Keeling MJ, Kelly-Hope L, King C, Lee BY, Le Rutte EA, Lietman TM, Ndeffo-Mbah M, Medley GF, Michael E, Pandey A, Peterson JK, Pinsent A, Porco TC, Richardus JH, Reimer L, Rock KS, Singh BK, Stolk W, Swaminathan S, Torr SJ, Townsend J, Truscott J, Walker M, Zoueva A, NTD Modelling Consortium.: Quantitative analyses and modelling to support achievement of the 2020 goals for nine neglected tropical diseases. Parasit Vectors. 2015 Dec 9; 2015 Dec 9. PMID: 26652272
- Townsend JP, Skrip LA, Galvani AP: Impact of bed capacity on spatiotemporal shifts in Ebola transmission. Proc Natl Acad Sci U S A. 2015 Nov 17; 2015 Oct 30. PMID: 26518509
- Prum RO, Berv JS, Dornburg A, Field DJ, Townsend JP, Lemmon EM, Lemmon AR: A comprehensive phylogeny of birds (Aves) using targeted next-generation DNA sequencing. Nature. 2015 Oct 22; 2015 Oct 7. PMID: 26444237
- Hong WS, Shpak M, Townsend JP: Inferring the Origin of Metastases from Cancer Phylogenies. Cancer Res. 2015 Oct 1; 2015 Aug 10. PMID: 26260528
- Hwang J, Zhao Q, Yang ZL, Wang Z, Townsend JP: Solving the ecological puzzle of mycorrhizal associations using data from annotated collections and environmental samples - an example of saddle fungi. Environ Microbiol Rep. 2015 Aug; 2015 Jul 1. PMID: 26033481
- Hodgins-Davis A, Rice DP, Townsend JP: Gene Expression Evolves under a House-of-Cards Model of Stabilizing Selection. Mol Biol Evol. 2015 Aug; 2015 Apr 20. PMID: 25901014
- Wells C, Yamin D, Ndeffo-Mbah ML, Wenzel N, Gaffney SG, Townsend JP, Meyers LA, Fallah M, Nyenswah TG, Altice FL, Atkins KE, Galvani AP: Harnessing case isolation and ring vaccination to control Ebola. PLoS Negl Trop Dis. 2015 May; 2015 May 29. PMID: 26024528
- Su Z, Townsend JP: Utility of characters evolving at diverse rates of evolution to resolve quartet trees with unequal branch lengths: analytical predictions of long-branch effects. BMC Evol Biol. 2015 May 14; 2015 May 14. PMID: 25968460
- Gilbert JA, Long EF, Brooks RP, Friedland GH, Moll AP, Townsend JP, Galvani AP, Shenoi SV: Integrating Community-Based Interventions to Reverse the Convergent TB/HIV Epidemics in Rural South Africa. PLoS One. 2015; 2015 May 4. PMID: 25938501
- Scarpino SV, Iamarino A, Wells C, Yamin D, Ndeffo-Mbah M, Wenzel NS, Fox SJ, Nyenswah T, Altice FL, Galvani AP, Meyers LA, Townsend JP: Epidemiological and viral genomic sequence analysis of the 2014 ebola outbreak reveals clustered transmission. Clin Infect Dis. 2015 Apr 1; 2014 Dec 15. PMID: 25516185
- Atkins KE, Wenzel NS, Ndeffo-Mbah M, Altice FL, Townsend JP, Galvani AP: Under-reporting and case fatality estimates for emerging epidemics. BMJ. 2015 Mar 16; 2015 Mar 16. PMID: 25779635
- Pandey A, Atkins KE, Medlock J, Wenzel N, Townsend JP, Childs JE, Nyenswah TG, Ndeffo-Mbah ML, Galvani AP: Strategies for containing Ebola in West Africa. Science. 2014 Nov 21; 2014 Oct 30. PMID: 25414312
- Lehr NA, Wang Z, Li N, Hewitt DA, López-Giráldez F, Trail F, Townsend JP: Gene expression differences among three Neurospora species reveal genes required for sexual reproduction in Neurospora crassa. PLoS One. 2014; 2014 Oct 20. PMID: 25329823
- Dornburg A, Townsend JP, Friedman M, Near TJ: Phylogenetic informativeness reconciles ray-finned fish molecular divergence times. BMC Evol Biol. 2014 Aug 8; 2014 Aug 8. PMID: 25103329
- Wu C, Yang F, Smith KM, Peterson M, Dekhang R, Zhang Y, Zucker J, Bredeweg EL, Mallappa C, Zhou X, Lyubetskaya A, Townsend JP, Galagan JE, Freitag M, Dunlap JC, Bell-Pedersen D, Sachs MS: Genome-wide characterization of light-regulated genes in Neurospora crassa. G3 (Bethesda). 2014 Jul 21; 2014 Jul 21. PMID: 25053707
- Talbert-Slagle K, Atkins KE, Yan KK, Khurana E, Gerstein M, Bradley EH, Berg D, Galvani AP, Townsend JP: Cellular superspreaders: an epidemiological perspective on HIV infection inside the body. PLoS Pathog. 2014 May; 2014 May 8. PMID: 24811311
- Gilbert JA, Meyers LA, Galvani AP, Townsend JP: Probabilistic uncertainty analysis of epidemiological modeling to guide public health intervention policy. Epidemics. 2014 Mar; 2013 Nov 19. PMID: 24593920
- Nielsen KM, Bøhn T, Townsend JP: Detecting rare gene transfer events in bacterial populations. Front Microbiol. 2014 Jan 7; 2014 Jan 7. PMID: 24432015
- Wang Z, Lopez-Giraldez F, Lehr N, Farré M, Common R, Trail F, Townsend JP: Global gene expression and focused knockout analysis reveals genes associated with fungal fruiting body development in Neurospora crassa. Eukaryot Cell. 2014 Jan; 2013 Nov 15. PMID: 24243796
- Ndeffo Mbah ML, Poolman EM, Atkins KE, Orenstein EW, Meyers LA, Townsend JP, Galvani AP: Potential cost-effectiveness of schistosomiasis treatment for reducing HIV transmission in Africa--the case of Zimbabwean women. PLoS Negl Trop Dis. 2013; 2013 Aug 1. PMID: 23936578
- Ndeffo Mbah ML, Medlock J, Meyers LA, Galvani AP, Townsend JP: Optimal targeting of seasonal influenza vaccination toward younger ages is robust to parameter uncertainty. Vaccine. 2013 Jun 26; 2013 May 16. PMID: 23684837
- Atkins KE, Townsend JP, Medlock J, Galvani AP: Epidemiological mechanisms of genetic resistance to kuru. J R Soc Interface. 2013 Aug 6; 2013 Jun 5. PMID: 23740487
- Ndeffo Mbah ML, Kjetland EF, Atkins KE, Poolman EM, Orenstein EW, Meyers LA, Townsend JP, Galvani AP: Cost-effectiveness of a community-based intervention for reducing the transmission of Schistosoma haematobium and HIV in Africa. Proc Natl Acad Sci U S A. 2013 May 7; 2013 Apr 15. PMID: 23589884
- McBride RC, Boucher N, Park DS, Turner PE, Townsend JP: Yeast response to LA virus indicates coadapted global gene expression during mycoviral infection. FEMS Yeast Res. 2013 Mar; 2013 Jan 2. PMID: 23122216
- López-Giráldez F, Moeller AH, Townsend JP: Evaluating phylogenetic informativeness as a predictor of phylogenetic signal for metazoan, fungal, and mammalian phylogenomic data sets. Biomed Res Int. 2013; 2013 Jun 26. PMID: 23878813
- Durham DP, Poolman EM, Ibuka Y, Townsend JP, Galvani AP: Reevaluation of epidemiological data demonstrates that it is consistent with cross-immunity among human papillomavirus types. J Infect Dis. 2012 Oct; 2012 Aug 7. PMID: 22872732
- Townsend JP, Su Z, Tekle YI: Phylogenetic signal and noise: predicting the power of a data set to resolve phylogeny. Syst Biol. 2012 Oct; 2012 Mar 3. PMID: 22389443
- Wang Z, Kin K, López-Giráldez F, Johannesson H, Townsend JP: Sex-specific gene expression during asexual development of Neurospora crassa. Fungal Genet Biol. 2012 Jul; 2012 May 22. PMID: 22626843
- Huang T, López-Giráldez F, Townsend JP, Irish VF: RBE controls microRNA164 expression to effect floral organogenesis. Development. 2012 Jun; 2012 May 9. PMID: 22573623
- Wang Z, Lehr N, Trail F, Townsend JP: Differential impact of nutrition on developmental and metabolic gene expression during fruiting body development in Neurospora crassa. Fungal Genet Biol. 2012 May; 2012 Mar 26. PMID: 22469835
- Rice DP, Townsend JP: A test for selection employing quantitative trait locus and mutation accumulation data. Genetics. 2012 Apr; 2012 Jan 31. PMID: 22298701
- Townsend JP, Bøhn T, Nielsen KM: Assessing the probability of detection of horizontal gene transfer events in bacterial populations. Front Microbiol. 2012; 2012 Feb 20. PMID: 22363321
- Hodgins-Davis A, Adomas AB, Warringer J, Townsend JP: Abundant gene-by-environment interactions in gene expression reaction norms to copper within Saccharomyces cerevisiae. Genome Biol Evol. 2012. PMID: 23019066
- Fitzpatrick MC, Hampson K, Cleaveland S, Meyers LA, Townsend JP, Galvani AP: Potential for rabies control through dog vaccination in wildlife-abundant communities of Tanzania. PLoS Negl Trop Dis. 2012; 2012 Aug 21. PMID: 22928056
- Tekle YI, Nielsen KM, Liu J, Pettigrew MM, Meyers LA, Galvani AP, Townsend JP: Controlling antimicrobial resistance through targeted, vaccine-induced replacement of strains. PLoS One. 2012; 2012 Dec 5. PMID: 23227198
- López-Giráldez F, Townsend JP: PhyDesign: an online application for profiling phylogenetic informativeness. BMC Evol Biol. 2011 May 31; 2011 May 31. PMID: 21627831
- Townsend JP, Leuenberger C: Taxon sampling and the optimal rates of evolution for phylogenetic inference. Syst Biol. 2011 May; 2011 Feb 8. PMID: 21303824
- Wang Z, Nilsson RH, Lopez-Giraldez F, Zhuang WY, Dai YC, Johnston PR, Townsend JP: Tasting soil fungal diversity with earth tongues: phylogenetic test of SATé alignments for environmental ITS data. PLoS One. 2011 Apr 21; 2011 Apr 21. PMID: 21533038
- Adomas AB, Lopez-Giraldez F, Clark TA, Wang Z, Townsend JP: Multi-targeted priming for genome-wide gene expression assays. BMC Genomics. 2010 Aug 17; 2010 Aug 17. PMID: 20716356
- Zhang Z, López-Giráldez F, Townsend JP: LOX: inferring Level Of eXpression from diverse methods of census sequencing. Bioinformatics. 2010 Aug 1; 2010 Jun 10. PMID: 20538728
- Townsend JP, Lopez-Giraldez F: Optimal selection of gene and ingroup taxon sampling for resolving phylogenetic relationships. Syst Biol. 2010 Jul; 2010 May 19. PMID: 20547780
- Tekle YI, Grant JR, Kovner AM, Townsend JP, Katz LA: Identification of new molecular markers for assembling the eukaryotic tree of life. Mol Phylogenet Evol. 2010 Jun; 2010 Mar 17. PMID: 20302952
- Zhang Z, Townsend JP: The filamentous fungal gene expression database (FFGED). Fungal Genet Biol. 2010 Mar; 2009 Dec 16. PMID: 20025988
- Hodgins-Davis A, Townsend JP: Evolving gene expression: from G to E to GxE. Trends Ecol Evol. 2009 Dec; 2009 Aug 21. PMID: 19699549
- Zhang Z, Townsend JP: Maximum-likelihood model averaging to profile clustering of site types across discrete linear sequences. PLoS Comput Biol. 2009 Jun; 2009 Jun 26. PMID: 19557160
- Zhang Z, Cheung KH, Townsend JP: Bringing Web 2.0 to bioinformatics. Brief Bioinform. 2009 Jan; 2008 Oct 8. PMID: 18842678
- Wang Z, Johnston PR, Yang ZL, Townsend JP: Evolution of reproductive morphology in leaf endophytes. PLoS One. 2009; 2009 Jan 22. PMID: 19158947
- Townsend JP, López-Giráldez F, Friedman R: The phylogenetic informativeness of nucleotide and amino acid sequences for reconstructing the vertebrate tree. J Mol Evol. 2008 Nov; 2008 Aug 12. PMID: 18696029
- Cheung KH, Yip KY, Townsend JP, Scotch M: HCLS 2.0/3.0: health care and life sciences data mashup using Web 2.0/3.0. J Biomed Inform. 2008 Oct; 2008 Apr 11. PMID: 18487092
- Townsend JP: Sleuthing the difference a nucleotide can make. Mol Ecol. 2008 Jun. PMID: 18565029
- Clark TA, Townsend JP: Quantifying variation in gene expression. Mol Ecol. 2007 Jul. PMID: 17594433
- Townsend JP: Profiling phylogenetic informativeness. Syst Biol. 2007 Apr. PMID: 17464879
- Taylor JW, Turner E, Townsend JP, Dettman JR, Jacobson D: Eukaryotic microbes, species recognition and the geographic limits of species: examples from the kingdom Fungi. Philos Trans R Soc Lond B Biol Sci. 2006 Nov 29. PMID: 17062413
- Landry CR, Townsend JP, Hartl DL, Cavalieri D: Ecological and evolutionary genomics of Saccharomyces cerevisiae. Mol Ecol. 2006 Mar. PMID: 16499686
- Kasuga T, Townsend JP, Tian C, Gilbert LB, Mannhaupt G, Taylor JW, Glass NL: Long-oligomer microarray profiling in Neurospora crassa reveals the transcriptional program underlying biochemical and physiological events of conidial germination. Nucleic Acids Res. 2005; 2005 Nov 14. PMID: 16287898
- Gogarten JP, Townsend JP: Horizontal gene transfer, genome innovation and evolution. Nat Rev Microbiol. 2005 Sep. PMID: 16138096
- Townsend JP, Taylor JW: Designing experiments using spotted microarrays to detect gene regulation differences within and among species. Methods Enzymol. 2005. PMID: 15865986
- Townsend JP: Resolution of large and small differences in gene expression using models for the Bayesian analysis of gene expression levels and spotted DNA microarrays. BMC Bioinformatics. 2004 May 5; 2004 May 5. PMID: 15128431
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