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Background Short-read data from next-generation sequencing technologies are now being generated across a range of research projects. The fidelity of this data can be affected by several factors and it is important to have simple and reliable approaches for monitoring it at the level of individual experiments. Results We developed a fast, scalable and accurate approach to estimating error rates in short reads, which has the added advantage of not requiring a reference genome. We build on the fundamental observation that there is a linear relationship between the copy number for a given read and the number of erroneous reads that differ from the read of interest by one or two bases. The slope of this relationship can be transformed to give an estimate of the error rate, both by read and by position. We present simulation studies as well as analyses of real data sets illustrating the precision and accuracy of this method, and we show that it is more accurate than alternatives that count the difference between the sample of interest and a reference genome. We show how this methodology led to the detection of mutations in the genome of the PhiX strain used for calibration of Illumina data. The proposed method is implemented in an R package, which can be downloaded from http://bcb.dfci.harvard.edu/∼vwang/shadowRegression.html . Conclusions The proposed method can be used to monitor the quality of sequencing pipelines at the level of individual experiments without the use of reference genomes. Furthermore, having an estimate of the error rates gives one the opportunity to improve analyses and inferences in many applications of next-generation sequencing data.

We describe a postgenomic in silico approach for identifying genes that are likely to be essential and estimate their proportion in haploid genomes. With the knowledge of all sites eligible for mutagenesis and an experimentally determined partial list of nonessential genes from genome mutagenesis, a Bayesian statistical method provides reasonable predictions of essential genes with a subsaturation level of random mutagenesis. For mutagenesis, a transposon such as Himar1 is suitable as it inserts randomly into TA sites. All of the possible insertion sites may be determined a priori from the genome sequence and with this information, data on experimentally hit TA sites may be used to predict the proportion of genes that cannot be mutated. As a model, we used the Mycobacterium tuberculosis genome. Using the Himar1 transposon, we created a genetically defined collection of 1,425 insertion mutants. Based on our Bayesian statistical analysis using Markov chain Monte Carlo and the observed frequencies of transposon insertions in all of the genes, we estimated that the M. tuberculosis genome contains 35% (95% confidence interval, 28%-41%) essential genes. This analysis further revealed seven functional groups with high probabilities of being enriched in essential genes. The PE-PGRS (Pro-Glu polymorphic GC-rich repetitive sequence) family of genes, which are unique to mycobacteria, the polyketide/nonribosomal peptide synthase family, and mycolic and fatty acid biosynthesis gene families were disproportionately enriched in essential genes. At subsaturation levels of mutagenesis with a random transposon such as Himar1, this approach permits a statistical prediction of both the proportion and identities of essential genes of sequenced genomes.

Statistics departments are facing rapid growth in enrollments and increases in demand for courses. This article discusses the use of design and analysis of experiments (DAE) as a nonterminal second course in statistics for undergraduate statistics majors, minors, and other students seeking exposure to the practice of statistics beyond the introductory course. DAE is a gateway to approaching statistical thinking as data-based problem solving by exposing students to statistical, computational, data, and communication skills in the second course. Given the somewhat antiquated view of design and deemphasis of classical design of experiments topics in the new ASA curriculum guidelines, DAE may seem an odd choice for the second course; however, it exposes students to the breadth of the statistical problem-solving process, explores foundational issues of the discipline, and is accessible to students who have not yet finished their advanced mathematical training. These skills remain essential in the data science era as students must be equipped to understand the potential and peril of found data using the principles of design. While DAE may not be the appropriate second course for all statistics programs, it provides a strong foundation for causal inference and experimental design for students pursuing a B.S. in Statistics in a program housed in a department of statistics. [Received December 2014. Revised July 2015.]

Developments in metrology provide the opportunity to improve process monitoring by obtaining many measurements on each sampled unit. Increasing the number of measurements may increase the sensitivity of control charts to detection of flaws in local regions; however, the correlation between spatially proximal measurements may introduce redundancy and inefficiency in the test. This paper extends multivariate statistical process control to spatial-data monitoring by recognizing the spatial correlation between multiple measurements on the same item and replacing the sample covariance matrix with a parameterized covariance based on the semivariogram. The properties of this control chart for the mean of a spatial process are explored with simulated data and the method is illustrated with an example using ultrasonic technology to obtain nondestructive measurements of bottle thickness.

The health of American democracy has literally been challenged. The global pandemic has powerfully exposed a long-standing truth: electoral policies that are frequently referred to as \"convenience voting\" are really a mode of \"survival voting\" for millions of Americans. As our data show, racial minorities are overrepresented among voters whose health is most vulnerable, and politicians have leveraged these health disparities to subordinate the political voice of racial minorities. To date, data about racial disparities in health has played a very limited role in assessing voting rights. A new health lens on the racial impacts of voting rules would beneficially inform-and perhaps even fundamentally alter-how we address several common voting rights issues. A new focus on the disparate health effects of voting rules, grounded in the kind of solid empirical evidence we provide, could reinvigorate the Voting Rights Act (VRA) by providing new avenues for assessing voting rights, for litigating and judging voter suppression claims under section 2, and even informing a new coverage formula in a modified section 5. This evidence arrives at a critical juncture for the VRA which has been stripped of much of its bite by the Supreme Court and is currently being debated in Congress. The clear and compelling story told by our data are a clarion call to legislators, courts, and litigators to reconceptualize and strengthen voting rights by accounting for the barriers that health disparities pose to minority access to the ballot.

Vulnerability drives disaster law, yet the literature lacks both an overarching analysis of the different aspects of vulnerability and a nuanced examination of the factors that shape disaster outcomes. Though central to disaster law and policy, vulnerability often lurks in the shadows of a disaster, evident only once the worst is past and the bodies have been counted. The COVID-19 pandemic is a notable exception to this historical pattern: from the beginning of the pandemic, it has been clear that the virus poses different risks to different people, depending on vulnerability variables. This most recent pandemic experience thus provides a useful vantage point for analyzing vulnerability. Drawing on empirical data from the pandemic and experiences from past disasters, this Article identifies and discusses the policy implications of three dimensions of disaster vulnerability: the geography of vulnerability, competing or conflicting vulnerabilities, and political vulnerability. First, it explores the geography of vulnerability, using statistical analysis and geographic information system (GIS) mapping. The Article presents an innovative COVID-19 vulnerability index that identifies the country's most vulnerable counties and the leading driver of vulnerability for each county. It demonstrates how this index could have informed voter accommodations during the 2020 elections and mask mandates throughout the pandemic. The Article also shows how, going forward, similar modeling could make disaster management more proactive and better able to anticipate needs and prioritize disaster mitigation and response resources. Second, this Article explores competing or conflicting vulnerabilities-situations where policy-makers must prioritize one vulnerable group or one aspect of vulnerability over another. To illustrate this, it considers two other policy challenges: school closures and vaccine distribution. Finally, the Article explores political vulnerability, analyzing how disasters make already-vulnerable groups even more vulnerable to certain harms, including political neglect, stigmatization, disenfranchisement, and displacement. In sum, this Article draws upon the costly lessons of COVID-19 to suggest a more robust framework for policy-makers to assess and respond to vulnerability in future disasters.

The anthrax outbreak in the US in fall 2001 resulted from the intentional dissemination of Bacillus anthracis spores. As of Jan 2002, about 10,000 persons nationwide had been recommended to undergo the 60-day regimen of antimicrobial prophylaxis.

In the fall of 2001 an outbreak of inhalational anthrax occurred in the United States that was the result of bioterrorism. Letters contaminated with anthrax spores were sent through the postal system. In response to the outbreak, public health officials treated over 10,000 persons with antibiotic prophylaxis in the hopes of preventing further morbidity and mortality. No persons receiving the antibiotics subsequently developed disease. The question arises as to how many cases of disease may actually have been prevented by the public health intervention of antibiotic prophylaxis. A statistical model is developed to answer this question by relating to the incubation period distribution the dates of disease onset, dates of initiation of antibiotic prophylaxis, and dates of exposure to the anthrax spores. An important complication is that the date of exposure to the anthrax spores was unknown for a cluster of cases in Florida because the contaminated letter was never found. A general likelihood function for a multicommon source outbreak is developed where the dates of exposure to the source (e.g., anthrax spores) may or may not be known. Estimates of the incubation period distribution are derived from an outbreak in Sverdlovsk, Russia. The methods are applied to the 2001 U.S. outbreak. The sensitivity of the estimates to the assumed incubation period is investigated. Properties of the estimators, particularly when the outbreak sizes are small, are evaluated by simulation. In the absence of antibiotic prophylaxis, the outbreak could have been about twice as large but unlikely would have been more than 50 cases. The results underscore the importance of early detection of outbreaks together with targeted and effective public health control measures.

Blades reviews The Cambridge Dictionary of Statistics, Fourth Edition by B. S. Everitt and A. Skrondal.