2022
Placing human gene families into their evolutionary context
Dornburg A, Mallik R, Wang Z, Bernal M, Thompson B, Bruford E, Nebert D, Vasiliou V, Yohe L, Yoder J, Townsend J. Placing human gene families into their evolutionary context. Human Genomics 2022, 16: 56. PMID: 36369063, PMCID: PMC9652883, DOI: 10.1186/s40246-022-00429-5.Peer-Reviewed Original ResearchConceptsHuman gene familyGene familyHuman genomeEvolutionary contextGene family evolutionNon-model organismsFirst human genomeGenome biologyComparative genomicsFamily evolutionDistant speciesDraft sequenceGenomic studiesGene expressionGenome sequencingSequence complexityGenomeCancer biologyUnprecedented insightsNovel discoveriesCritical roleOrganismsBiologyComparative approachFamily
2018
Somatic evolutionary timings of driver mutations
Gomez K, Miura S, Huuki LA, Spell BS, Townsend JP, Kumar S. Somatic evolutionary timings of driver mutations. BMC Cancer 2018, 18: 85. PMID: 29347918, PMCID: PMC5774140, DOI: 10.1186/s12885-017-3977-y.Peer-Reviewed Original Research
2011
Taxon Sampling and the Optimal Rates of Evolution for Phylogenetic Inference
Townsend JP, Leuenberger C. Taxon Sampling and the Optimal Rates of Evolution for Phylogenetic Inference. Systematic Biology 2011, 60: 358-365. PMID: 21303824, DOI: 10.1093/sysbio/syq097.Peer-Reviewed Original Research
2009
Maximum-Likelihood Model Averaging To Profile Clustering of Site Types across Discrete Linear Sequences
Zhang Z, Townsend JP. Maximum-Likelihood Model Averaging To Profile Clustering of Site Types across Discrete Linear Sequences. PLOS Computational Biology 2009, 5: e1000421. PMID: 19557160, PMCID: PMC2695770, DOI: 10.1371/journal.pcbi.1000421.Peer-Reviewed Original ResearchConceptsInformation criterionModel averagingBayesian information criterionMaximum likelihood methodModel likelihoodModel uncertaintyModel selectionDescription of clustersLevel of clusteringPrecision of estimationAkaike information criterionParameter rangeCluster countsLikelihood methodComputational biologyCluster sizeGood accuracyConquer strategyAveragingClusteringModelHierarchical clusteringClustersStatisticsEstimation
2007
Enabling a Community to Dissect an Organism: Overview of the Neurospora Functional Genomics Project
Dunlap JC, Borkovich KA, Henn MR, Turner GE, Sachs MS, Glass NL, McCluskey K, Plamann M, Galagan JE, Birren BW, Weiss RL, Townsend JP, Loros JJ, Nelson MA, Lambreghts R, Colot HV, Park G, Collopy P, Ringelberg C, Crew C, Litvinkova L, DeCaprio D, Hood HM, Curilla S, Shi M, Crawford M, Koerhsen M, Montgomery P, Larson L, Pearson M, Kasuga T, Tian C, Baştürkmen M, Altamirano L, Xu J. Enabling a Community to Dissect an Organism: Overview of the Neurospora Functional Genomics Project. Advances In Genetics 2007, 57: 49-96. PMID: 17352902, PMCID: PMC3673015, DOI: 10.1016/s0065-2660(06)57002-6.Peer-Reviewed Original ResearchConceptsFunctional genomics projectsGenomics projectsFilamentous fungiFilamentous fungus NeurosporaFunctional genomic analysisNon-yeast fungiFunctional genomicsNeurospora genomeFungus NeurosporaNovel genesPositional cloningNeurospora crassaAntisense transcriptsGenomic analysisSNP mapAlternative promotersCDNA libraryExpression analysisGene replacementMutant strainSystematic disruptionExpression dataPhenotypic analysisNeurosporaConditions of growth