Regularized sequence-context mutational trees capture variation in mutation rates across the human genome.

Germline mutation is the mechanism by which genetic variation in a population is created. Inferences derived from mutation rate models are fundamental to many population genetics methods. Previous models have demonstrated that nucleotides flanking polymorphic sites-the local sequence context-explain variation in the probability that a site is polymorphic. However, limitations to these models exist as the size of the local sequence context window expands. These include a lack of robustness to data sparsity at typical sample sizes, lack of regularization to generate parsimonious models and lack of quantified uncertainty in estimated rates to facilitate comparison between models. To address these limitations, we developed Baymer, a regularized Bayesian hierarchical tree model that captures the heterogeneous effect of sequence contexts on polymorphism probabilities. Baymer implements an adaptive Metropolis-within-Gibbs Markov Chain Monte Carlo sampling scheme to estimate the posterior distributions of sequence-context based probabilities that a site is polymorphic. We show that Baymer accurately infers polymorphism probabilities and well-calibrated posterior distributions, robustly handles data sparsity, appropriately regularizes to return parsimonious models, and scales computationally at least up to 9-mer context windows. We demonstrate application of Baymer in three ways-first, identifying differences in polymorphism probabilities between continental populations in the 1000 Genomes Phase 3 dataset, second, in a sparse data setting to examine the use of polymorphism models as a proxy for de novo mutation probabilities as a function of variant age, sequence context window size, and demographic history, and third, comparing model concordance between different great ape species. We find a shared context-dependent mutation rate architecture underlying our models, enabling a transfer-learning inspired strategy for modeling germline mutations. In summary, Baymer is an accurate polymorphism probability estimation algorithm that automatically adapts to data sparsity at different sequence context levels, thereby making efficient use of the available data.

Building more accurate decision trees with the additive tree.

The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as those produced by gradient boosting, and full interaction models, such as CART, have been investigated largely in isolation. We show that these models exist along a spectrum, revealing previously unseen connections between these approaches. This paper introduces a rigorous formalization for the additive tree, an empirically validated learning technique for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although the additive tree is designed primarily to provide both the model interpretability and predictive performance needed for high-stakes applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches.

Reducing Crime by Remediating Vacant Lots: The Moderating Effect of Nearby Land Uses.

Objective: Place-based blight remediation programs have gained popularity in recent years as a crime reduction approach. This study estimated the impact of a citywide vacant lot greening program in Philadelphia on changes in crime over multiple years, and whether the effects were moderated by nearby land uses. Methods: The vacant lot greening program was assessed using quasi-experimental and experimental designs. Entropy distance weighting was used in the quasi-experimental analysis to match control lots to be comparable to greened lots on pre-existing crime trends. Fixed-effects difference-in-differences models were used to estimate the impact of the vacant lot greening program in quasi-experimental and experimental analyses. Results: Vacant lot greening was estimated to reduce total crime and multiple subcategories in both the quasi-experimental and experimental evaluations. Remediating vacant lots had a smaller effect on reducing crime when they were located nearby train stations and alcohol outlets. The crime reductions from vacant lot remediations were larger when they were located near areas of active businesses. There is some suggestive evidence that the effects of vacant lot greening are larger when located in neighborhoods with higher pre-intervention levels of social cohesion. Conclusions: The findings suggest that vacant lot greening provides a sustainable approache to reducing crime in disadvantaged neighborhoods, and the effects may vary by different surrounding land uses. To better understand the mechanisms through which place-based blight remediation interventions reduce crime, future research should measure human activities and neighborly socialization in and around places before and after remediation efforts are implemented.

Modeling lottery incentives for daily adherence.

Many health issues require adherence to recommended daily activities, such as taking medication to manage a chronic condition, walking a certain distance to promote weight loss, or measuring weights to assess fluid balance in heart failure. The cost of nonadherence can be high, with respect to both individual health outcomes and the healthcare system. Incentivizing adherence to daily activities can promote better health in patients and populations and potentially provide long-term cost savings. Multiple incentive structures are possible. We focus here on a daily lottery incentive in which payment occurs when both the participant's lottery number matches the number drawn and the participant adheres to the targeted daily behavior. Our objective is to model the lottery's effect on participants' probability to complete the targeted task, particularly over the short term. We combine two procedures for analyzing such binary time series: a parameter-driven regression model with an autocorrelated latent process and a comparative interrupted time series. We use the output of the regression model as the control generator for the comparative time series in order to create a quasi-experimental design.

Mouse Label-Retaining Cells Are Molecularly and Functionally Distinct From Reserve Intestinal Stem Cells.

BACKGROUND & AIMS: Intestinal homeostasis and regeneration after injury are controlled by 2 different types of cells: slow cycling, injury-resistant reserve intestinal stem cells (ISCs) and actively proliferative ISCs. Putative reserve ISCs have been identified using a variety of methods, including CreER insertions at Hopx or Bmi1 loci in mice and DNA label retention. Label-retaining cells (LRCs) include dormant stem cells in several tissues; in the intestine, LRCs appear to share some properties with reserve ISCs, which can be marked by reporter alleles. We investigated the relationships between these populations. METHODS: Studies were performed in Lgr5-EGFP-IRESCreERT2, Bmi1-CreERT2, Hopx-CreERT2, and TRE-H2BGFP::Hopx-CreERT2::lox-stop-lox-tdTomato mice. Intestinal epithelial cell populations were purified; we compared reporter allele-marked reserve ISCs and several LRC populations (marked by H2B-GFP retention) using histologic flow cytometry and functional and single-cell gene expression assays. RESULTS: LRCs were dynamic and their cellular composition changed with time. Short-term LRCs had properties of secretory progenitor cells undergoing commitment to the Paneth or enteroendocrine lineages, while retaining some stem cell activity. Long-term LRCs lost stem cell activity and were a homogenous population of terminally differentiated Paneth cells. Reserve ISCs marked with HopxCreER were primarily quiescent (in G0), with inactive Wnt signaling and robust stem cell activity. In contrast, most LRCs were in G1 arrest and expressed genes that are regulated by the Wnt pathway or are in the secretory lineage. CONCLUSIONS: LRCs are molecularly and functionally distinct from reporter-marked reserve ISCs. This information provides an important basis for future studies of relationships among ISC populations.

Heterogeneity in readouts of canonical wnt pathway activity within intestinal crypts.

BACKGROUND: Canonical Wnt pathway signaling is necessary for maintaining the proliferative capacity of mammalian intestinal crypt base columnar stem cells (CBCs). Furthermore, dysregulation of the Wnt pathway is a major contributor to disease, including oncogenic transformation of the intestinal epithelium. Given the critical importance of this pathway, numerous tools have been used as proxy measures for Wnt pathway activity, yet the relationship between Wnt target gene expression and reporter allele activity within individual cells at the crypt base remains unclear. RESULTS: Here, we describe a novel Axin2-CreERT2-tdTomato allele that efficiently marks both Wnt(High) CBCs and radioresistant reserve intestinal stem cells. We analyze the molecular and functional identity of Axin2-CreERT2-tdTomato-marked cells using single cell gene expression profiling and tissue regeneration assays and find that Axin2 reporter activity does not necessarily correlate with expression of Wnt target genes and, furthermore, that Wnt target genes themselves vary in their expression patterns at the crypt base. CONCLUSIONS: Wnt target genes and reporter alleles can vary greatly in their cell-type specificity, demonstrating that these proxies cannot be used interchangeably. Furthermore, Axin2-CreERT2-tdTomato is a robust marker of both active and reserve intestinal stem cells and is thus useful for understanding the intestinal stem cell compartment. Developmental Dynamics 245:822-833, 2016. © 2016 Wiley Periodicals, Inc.

Potential Mechanisms for Cancer Resistance in Elephants and Comparative Cellular Response to DNA Damage in Humans.

IMPORTANCE: Evolutionary medicine may provide insights into human physiology and pathophysiology, including tumor biology. OBJECTIVE: To identify mechanisms for cancer resistance in elephants and compare cellular response to DNA damage among elephants, healthy human controls, and cancer-prone patients with Li-Fraumeni syndrome (LFS). DESIGN, SETTING, AND PARTICIPANTS: A comprehensive survey of necropsy data was performed across 36 mammalian species to validate cancer resistance in large and long-lived organisms, including elephants (n = 644). The African and Asian elephant genomes were analyzed for potential mechanisms of cancer resistance. Peripheral blood lymphocytes from elephants, healthy human controls, and patients with LFS were tested in vitro in the laboratory for DNA damage response. The study included African and Asian elephants (n = 8), patients with LFS (n = 10), and age-matched human controls (n = 11). Human samples were collected at the University of Utah between June 2014 and July 2015. EXPOSURES: Ionizing radiation and doxorubicin. MAIN OUTCOMES AND MEASURES: Cancer mortality across species was calculated and compared by body size and life span. The elephant genome was investigated for alterations in cancer-related genes. DNA repair and apoptosis were compared in elephant vs human peripheral blood lymphocytes. RESULTS: Across mammals, cancer mortality did not increase with body size and/or maximum life span (eg, for rock hyrax, 1% [95% CI, 0%-5%]; African wild dog, 8% [95% CI, 0%-16%]; lion, 2% [95% CI, 0%-7%]). Despite their large body size and long life span, elephants remain cancer resistant, with an estimated cancer mortality of 4.81% (95% CI, 3.14%-6.49%), compared with humans, who have 11% to 25% cancer mortality. While humans have 1 copy (2 alleles) of TP53, African elephants have at least 20 copies (40 alleles), including 19 retrogenes (38 alleles) with evidence of transcriptional activity measured by reverse transcription polymerase chain reaction. In response to DNA damage, elephant lymphocytes underwent p53-mediated apoptosis at higher rates than human lymphocytes proportional to TP53 status (ionizing radiation exposure: patients with LFS, 2.71% [95% CI, 1.93%-3.48%] vs human controls, 7.17% [95% CI, 5.91%-8.44%] vs elephants, 14.64% [95% CI, 10.91%-18.37%]; P < .001; doxorubicin exposure: human controls, 8.10% [95% CI, 6.55%-9.66%] vs elephants, 24.77% [95% CI, 23.0%-26.53%]; P < .001). CONCLUSIONS AND RELEVANCE: Compared with other mammalian species, elephants appeared to have a lower-than-expected rate of cancer, potentially related to multiple copies of TP53. Compared with human cells, elephant cells demonstrated increased apoptotic response following DNA damage. These findings, if replicated, could represent an evolutionary-based approach for understanding mechanisms related to cancer suppression.

PECONPI: a novel software for uncovering pathogenic copy number variations in non-syndromic sensorineural hearing loss and other genetically heterogeneous disorders.

This report describes an algorithm developed to predict the pathogenicity of copy number variants (CNVs) in large sample cohorts. CNVs (genomic deletions and duplications) are found in healthy individuals and in individuals with genetic diagnoses, and differentiation of these two classes of CNVs can be challenging and usually requires extensive manual curation. We have developed PECONPI, an algorithm to assess the pathogenicity of CNVs based on gene content and CNV frequency. This software was applied to a large cohort of patients with genetically heterogeneous non-syndromic hearing loss to score and rank each CNV based on its relative pathogenicity. Of 636 individuals tested, we identified the likely underlying etiology of the hearing loss in 14 (2%) of the patients (1 with a homozygous deletion, 7 with a deletion of a known hearing loss gene and a point mutation on the trans allele and 6 with a deletion larger than 1 Mb). We also identified two probands with smaller deletions encompassing genes that may be functionally related to their hearing loss. The ability of PECONPI to determine the pathogenicity of CNVs was tested on a second genetically heterogeneous cohort with congenital heart defects (CHDs). It successfully identified a likely etiology in 6 of 355 individuals (2%). We believe this tool is useful for researchers with large genetically heterogeneous cohorts to help identify known pathogenic causes and novel disease genes.

Transcriptional regulation via TF-modifying enzymes: an integrative model-based analysis.

Transcription factor activity is largely regulated through post-translational modification. Here, we report the first integrative model of transcription that includes both interactions between transcription factors and promoters, and between transcription factors and modifying enzymes. Simulations indicate that our method is robust against noise. We validated our tool on a well-studied stress response network in yeast and on a STAT1-mediated regulatory network in human B cells. Our work represents a significant step toward a comprehensive model of gene transcription.

Financial status and travel time to driving schools as barriers to obtaining a young driver license in a state with comprehensive young driver licensing policy.

The highest lifetime risk for a motor vehicle crash is immediately after the point of licensure, with teen drivers most at risk. Comprehensive teen driver licensing policies that require completion of driver education and behind-the-wheel training along with Graduated Driver Licensing (GDL) are associated with lower young driver crash rates early in licensure. We hypothesize that lack of financial resources and travel time to driving schools reduce the likelihood for teens to complete driver training and gain a young driver's license before age 18. We utilize licensing data from the Ohio Bureau of Motor Vehicles on over 35,000 applicants between 15.5 and 25 years old collected between 2017 and 2019. This dataset of driving schools is maintained by the Ohio Department of Public Safety and is linked with Census tract-level socioeconomic data from the U.S. Census. Using logit models, we estimate the completion of driver training and license obtainment among young drivers in the Columbus, Ohio metro area. We find that young drivers in lower-income Census tracts have a lower likelihood to complete driver training and get licensed before age 18. As travel time to driving schools increases, teens in wealthier Census tracts are more likely to forgo driver training and licensure than teens in lower-income Census tracts. For jurisdictions aspiring to improve safe driving for young drivers, our findings help shape recommendations on policies to enhance access to driver training and licensure especially among teens living in lower-income Census tracts.

Comparison of Simulated Outcomes of Colorectal Cancer Surgery at the Highest-Performing vs Chosen Local Hospitals.

Importance: Variation in outcomes across hospitals adversely affects surgical patients. The use of high-quality hospitals varies by population, which may contribute to surgical disparities. Objective: To simulate the implications of data-driven hospital selection for social welfare among patients who underwent colorectal cancer surgery. Design, Setting, and Participants: This economic evaluation used the hospital inpatient file from the Florida Agency for Health Care Administration. Surgical outcomes of patients who were treated between January 1, 2016, and December 31, 2018 (training cohort), were used to estimate hospital performance. Costs and benefits of care at alternative hospitals were assessed in patients who were treated between January 1, 2019, and December 31, 2019 (testing cohort). The cohorts comprised patients 18 years or older who underwent elective colorectal resection for benign or malignant neoplasms. Data were analyzed from March to October 2022. Exposures: Using hierarchical logistic regression, we estimated the implications of hospital selection for in-hospital mortality risk in patients in the training cohort. These estimates were applied to patients in the testing cohort using bayesian simulations to compare outcomes at each patient's highest-performing and chosen local hospitals. Analyses were stratified by race and ethnicity to evaluate the potential implications for equity. Main Outcomes and Measures: The primary outcome was the mean patient-level change in social welfare, a composite measure balancing the value of reduced mortality with associated costs of care at higher-performing hospitals. Results: A total of 21 098 patients (mean [SD] age, 67.3 [12.0] years; 10 782 males [51.1%]; 2232 Black [10.6%] and 18 866 White [89.4%] individuals) who were treated at 178 hospitals were included. A higher-quality local hospital was identified for 3057 of 5000 patients (61.1%) in the testing cohort. Selecting the highest-performing hospital was associated with a 26.5% (95% CI, 24.5%-29.0%) relative reduction and 0.24% (95% CI, 0.23%-0.25%) absolute reduction in mortality risk. A mean amount of $1953 (95% CI, $1744-$2162) was gained in social welfare per patient treated. Simulated reassignment to a higher-quality local hospital was associated with a 23.5% (95% CI, 19.3%-32.9%) relative reduction and 0.26% (95% CI, 0.21%-0.30%) absolute reduction in mortality risk for Black patients, with $2427 (95% CI, $1697-$3158) gained in social welfare. Conclusions and Relevance: In this economic evaluation, using procedure-specific hospital performance as the primary factor in the selection of a local hospital for colorectal cancer surgery was associated with improved outcomes for both patients and society. Surgical outcomes data can be used to transform care and guide policy in colorectal cancer.

Predicting spatio-temporal population patterns of Borrelia burgdorferi, the Lyme disease pathogen.

The causative bacterium of Lyme disease, Borrelia burgdorferi, expanded from an undetected human pathogen into the etiologic agent of the most common vector-borne disease in the United States over the last several decades. Systematic field collections of the tick vector reveal increases in the geographic range and prevalence of B. burgdorferi-infected ticks that coincided with increases in human Lyme disease incidence across New York State.We investigate the impact of environmental features on the population dynamics of B. burgdorferi. Analytical models developed using field collections of nearly 19,000 nymphal Ixodes scapularis and spatially and temporally explicit environmental features accurately explained the variation in the nymphal infection prevalence of B. burgdorferi across space and time.Importantly, the model identified environmental features reflecting landscape ecology, vertebrate hosts, climatic metrics, climate anomalies and surveillance efforts that can be used to predict the biogeographical patterns of B. burgdorferi-infected ticks into future years and in previously unsampled areas.Forecasting the distribution and prevalence of a pathogen at fine geographic scales offers a powerful strategy to mitigate a serious public health threat. Synthesis and applications. A decade of environmental and tick data was collected to create a model that accurately predicts the infection prevalence of Borrelia burgdorferi over space and time. This predictive model can be extrapolated to create a high-resolution risk map of the Lyme disease pathogen for future years that offers an inexpensive approach to improve both ecological management and public health strategies to mitigate disease risk.

Mapping structural landmarks, ligand binding sites, and missense mutations to the collagen IV heterotrimers predicts major functional domains, novel interactions, and variation in phenotypes in inherited diseases affecting basement membranes.

Collagen IV is the major protein found in basement membranes. It comprises three heterotrimers (α1α1α2, α3α4α5, and α5α5α6) that form distinct networks, and are responsible for membrane strength and integrity.We constructed linear maps of the collagen IV heterotrimers ("interactomes") that indicated major structural landmarks, known and predicted ligand-binding sites, and missense mutations, in order to identify functional and disease-associated domains, potential interactions between ligands, and genotype­phenotype relationships. The maps documented more than 30 known ligand-binding sites as well as motifs for integrins, heparin, von Willebrand factor (VWF), decorin, and bone morphogenetic protein (BMP). They predicted functional domains for angiogenesis and haemostasis, and disease domains for autoimmunity, tumor growth and inhibition, infection, and glycation. Cooperative ligand interactions were indicated by binding site proximity, for example, between integrins, matrix metalloproteinases, and heparin. The maps indicated that mutations affecting major ligand-binding sites, for example, for Von Hippel Lindau (VHL) protein in the α1 chain or integrins in the α5 chain, resulted in distinctive phenotypes (Hereditary Angiopathy, Nephropathy, Aneurysms, and muscle Cramps [HANAC] syndrome, and early-onset Alport syndrome, respectively). These maps further our understanding of basement membrane biology and disease, and suggest novel membrane interactions, functions, and therapeutic targets.

Community vibrancy and its relationship with safety in Philadelphia.

To what extent can the strength of a local urban community impact neighborhood safety? We construct measures of community vibrancy based on a unique dataset of block party permit approvals from the City of Philadelphia. Our first measure captures the overall volume of block party events in a neighborhood whereas our second measure captures differences in the type (regular versus spontaneous) of block party activities. We use both regression modeling and propensity score matching to control for the economic, demographic and land use characteristics of the surrounding neighborhood when examining the relationship between crime and our two measures of community vibrancy. We conduct our analysis on aggregate levels of crime and community vibrancy from 2006 to 2015 as well as the trends in community vibrancy and crime over this time period. We find that neighborhoods with a higher number of block parties have a significantly higher crime rate, while those holding a greater proportion of spontaneous block party events have a significantly lower crime rate. We also find that neighborhoods which have an increase in the proportion of spontaneous block parties over time are significantly more likely to have a decreasing trend in total crime incidence over that same time period.

Estimating disease vector population size from citizen science data.

Citizen science projects have the potential to address hypotheses requiring extremely large datasets that cannot be collected with the financial and labour constraints of most scientific projects. Data collection by the general public could expand the scope of scientific enquiry if these data accurately capture the system under study. However, data collection inconsistencies by the untrained public may result in biased datasets that do not accurately represent the natural world. In this paper, we harness the availability of scientific and public datasets of the Lyme disease tick vector to identify and account for biases in citizen science tick collections. Estimates of tick abundance from the citizen science dataset correspond moderately with estimates from direct surveillance but exhibit consistent biases. These biases can be mitigated by including factors that may impact collector participation or effort in statistical models, which, in turn, result in more accurate estimates of tick population sizes. Accounting for collection biases within large-scale, public participation datasets could update species abundance maps and facilitate using the wealth of citizen science data to answer scientific questions at scales that are not feasible with traditional datasets.

Spatio-temporal variation in environmental features predicts the distribution and abundance of Ixodes scapularis.

Many species have experienced dramatic changes in both geographic range and population sizes in recent history. Increases in the geographic range or population size of disease vectors have public health relevance as these increases often precipitate the emergence of infectious diseases in human populations. Accurately identifying environmental factors affecting the biogeographic patterns of vector species is a long-standing analytical challenge, stemming from a paucity of data capturing periods of rapid changes in vector demographics. We systematically investigated the occurrence and abundance of nymphal Ixodes scapularis ticks at 532 sampling locations throughout New York State (NY), USA, between 2008 and 2018, a time frame that encompasses the emergence of diseases vectored by these ticks. Analyses of these field-collected data demonstrated a range expansion into northern and western NY during the last decade. Nymphal abundances increased in newly colonised areas, while remaining stable in areas with long-standing populations over the last decade. These trends in the geographic range and abundance of nymphs correspond to both the geographic expansion of human Lyme disease cases and increases in incidence rates. Analytic models fitted to these data incorporating time, space, and environmental factors, accurately identified drivers of the observed changes in nymphal occurrence and abundance. These models accounted for the spatial and temporal variation in the occurrence and abundance of nymphs and can accurately predict nymphal population patterns in future years. Forecasting disease risk at fine spatial scales prior to the transmission season can influence both public health mitigation strategies and individual behaviours, potentially impacting tick-borne disease risk and subsequently human disease incidence.

Cis-regulatory modules in the mammalian liver: composition depends on strength of Foxa2 consensus site.

Foxa2 is a critical transcription factor that controls liver development and plays an important role in hepatic gluconeogensis in adult mice. Here, we use genome-wide location analysis for Foxa2 to identify its targets in the adult liver. We then show by computational analyses that Foxa2 containing cis-regulatory modules are not constructed from a random assortment of binding sites for other transcription factors expressed in the liver, but rather that their composition depends on the strength of the Foxa2 consensus site present. Genes containing a cis-regulatory module with a medium or weak Foxa2 consensus site are much more liver-specific than the genes with a strong consensus site. We not only provide a better understanding of the mechanisms of Foxa2 regulation but also introduce a novel method for identification of different cis-regulatory modules involving a single factor.

Deciphering the combinatorial landscape of immunity.

From cellular activation to drug combinations, immunological responses are shaped by the action of multiple stimuli. Synergistic and antagonistic interactions between stimuli play major roles in shaping immune processes. To understand combinatorial regulation, we present the immune Synergistic/Antagonistic Interaction Learner (iSAIL). iSAIL includes a machine learning classifier to map and interpret interactions, a curated compendium of immunological combination treatment datasets, and their global integration into a landscape of ~30,000 interactions. The landscape is mined to reveal combinatorial control of interleukins, checkpoints, and other immune modulators. The resource helps elucidate the modulation of a stimulus by interactions with other cofactors, showing that TNF has strikingly different effects depending on co-stimulators. We discover new functional synergies between TNF and IFNß controlling dendritic cell-T cell crosstalk. Analysis of laboratory or public combination treatment studies with this user-friendly web-based resource will help resolve the complex role of interaction effects on immune processes.

A Bayesian approach to efficient differential allocation for resampling-based significance testing.

BACKGROUND: Large-scale statistical analyses have become hallmarks of post-genomic era biological research due to advances in high-throughput assays and the integration of large biological databases. One accompanying issue is the simultaneous estimation of p-values for a large number of hypothesis tests. In many applications, a parametric assumption in the null distribution such as normality may be unreasonable, and resampling-based p-values are the preferred procedure for establishing statistical significance. Using resampling-based procedures for multiple testing is computationally intensive and typically requires large numbers of resamples. RESULTS: We present a new approach to more efficiently assign resamples (such as bootstrap samples or permutations) within a nonparametric multiple testing framework. We formulated a Bayesian-inspired approach to this problem, and devised an algorithm that adapts the assignment of resamples iteratively with negligible space and running time overhead. In two experimental studies, a breast cancer microarray dataset and a genome wide association study dataset for Parkinson's disease, we demonstrated that our differential allocation procedure is substantially more accurate compared to the traditional uniform resample allocation. CONCLUSION: Our experiments demonstrate that using a more sophisticated allocation strategy can improve our inference for hypothesis testing without a drastic increase in the amount of computation on randomized data. Moreover, we gain more improvement in efficiency when the number of tests is large. R code for our algorithm and the shortcut method are available at http://people.pcbi.upenn.edu/~lswang/pub/bmc2009/.