ReadTools: A universal toolkit for handling sequence data from different sequencing platforms

Sequencing whole genomes has become a standard research tool in many disciplines including Molecular Ecology, but the rapid technological advances in combination with several competing platforms have resulted in a confusing diversity of formats. This lack of standard formats causes several problems, such as undocumented preprocessing steps or the loss of information in downstream software tools, which do not account for the specifics of the different available formats. ReadTools is an open‐source Java toolkit designed to standardize and preprocess read data from different platforms. It manages FASTQ‐ and SAM‐formatted inputs while dealing with platform‐specific peculiarities and provides a standard SAM compliant output. The code and executable are available at https://github.com/magicDGS/ReadTools .     

Naturalgwas: An R package for evaluating genomewide association methods with empirical data

Association studies of polygenic traits are notoriously difficult when those studies are conducted at large geographic scales. The difficulty arises as genotype frequencies often vary in geographic space and across distinct environments. Those large‐scale variations are known to yield false positives in standard association testing approaches. Although several methods alleviate this problem, no tools have been proposed to evaluate the power that association tests could achieve for a specific study design and set of genotypes. Our goal here is to present an R program fulfilling this objective, by allowing users to simulate phenotypes from observed genotypes and to estimate upper bounds on achievable power. The simulation model can incorporate realistic features such as population structure and gene‐by‐environment interactions, and the package implements a gold‐standard test that evaluates power using information on confounders. We illustrated the use of the program with example studies based on data for the plant species Arabidopsis thaliana. Simulated phenotypes were used to compare the ability of two recent association methods to correctly remove confounding factors, to evaluate power to detect causal variants, and to assess the influence various parameters. For the simulated data, the new tests reached performances close to the gold‐standard test and could be reasonably used with measured phenotypes. Power to detect causal variants was influenced by the number of variants and by the strength of their effect sizes, and specific thresholds were obtained from the simulation study. In conclusion, our program provides guidance on methodological choice of association tests, as well as useful knowledge on test performances in a user‐specific context.     

mvmapper: Interactive spatial mapping of genetic structures

Characterizing genetic structure across geographic space is a fundamental challenge in population genetics. Multivariate statistical analyses are powerful tools for summarizing genetic variability, but geographic information and accompanying metadata are not always easily integrated into these methods in a user‐friendly fashion. Here, we present a deployable Python‐based web‐tool, mvmapper , for visualizing and exploring results of multivariate analyses in geographic space. This tool can be used to map results of virtually any multivariate analysis of georeferenced data, and routines for exporting results from a number of standard methods have been integrated in the R package adegenet , including principal components analysis (PCA), spatial PCA, discriminant analysis of principal components, principal coordinates analysis, nonmetric dimensional scaling and correspondence analysis. mvmapper 's greatest strength is facilitating dynamic and interactive exploration of the statistical and geographic frameworks side by side, a task that is difficult and time‐consuming with currently available tools. Source code and deployment instructions, as well as a link to a hosted instance of mvmapper , can be found at https://popphylotools.github.io/mvMapper/ .     

ClinMine: Optimizing the Management of Patients in Hospital

Context

A better understanding of “patient pathway” thanks to data analysis can lead to better treatments for patients. The ClinMine project, supported by the French National Research Agency (ANR), aims at proposing, from various case studies, algorithmic and statistical models able to handle this type of pathway data, focusing primarily on hospital data.

Count data in biology—Data transformation or model reformation?

Statistical analyses are an integral component of scientific research, and for decades, biologists have applied transformations to data to meet the normal error assumptions for F and t tests. Over the years, there has been a movement from data transformation toward model reformation—the use of non‐normal error structures within the framework of the generalized linear model (GLM). The principal advantage of model reformation is that parameters are estimated on the original, rather than the transformed scale. However, data transformation has been shown to give better control over type I error, for simulated data with known error structures. We conducted a literature review of statistical textbooks directed toward biologists and of journal articles published in the primary literature to determine temporal trends in both the text recommendations and the practice in the refereed literature over the past 35 years. In this review, a trend of increasing use of reformation in the primary literature was evident, moving from no use of reformation before 1996 to >50% of the articles reviewed applying GLM after 2006. However, no such trend was observed in the recommendations in statistical textbooks. We then undertook 12 analyses based on published datasets in which we compared the type I error estimates, residual plot diagnostics, and coefficients yielded by analyses using square root transformations, log transformations, and the GLM. All analyses yielded acceptable residual versus fit plots and had similar p‐values within each analysis, but as expected, the coefficient estimates differed substantially. Furthermore, no consensus could be found in the literature regarding a procedure to back‐transform the coefficient estimates obtained from linear models performed on transformed datasets. This lack of consistency among coefficient estimates constitutes a major argument for model reformation over data transformation in biology.     

Ecobat: An online resource to facilitate transparent,evidence‐based interpretation of bat activity data

Acoustic surveys of bats are one of the techniques most commonly used by ecological practitioners. The results are used in Ecological Impact Assessments to assess the likely impacts of future developments on species that are widely protected in law, and to monitor developments’ postconstruction. However, there is no standardized methodology for analyzing or interpreting these data, which can make the assessment of the ecological value of a site very subjective. Comparisons of sites and projects are therefore difficult for ecologists and decision‐makers, for example, when trying to identify the best location for a new road based on relative bat activity levels along alternative routes. Here, we present a new web‐based, data‐driven tool, Ecobat, which addresses the need for a more robust way of interpreting ecological data. Ecobat offers users an easy, standardized, and objective method for analyzing bat activity data. It allows ecological practitioners to compare bat activity data at regional and national scales and to generate a numerical indicator of the relative importance of a night's worth of bat activity. The tool is free and open‐source; because the underlying algorithms are already developed, it could easily be expanded to new geographical regions and species. Data donation is required to ensure the robustness of the analyses; we use a positive feedback mechanism to encourage ecological practitioners to share data by providing in return high quality, contextualized data analysis, and graphical visualizations for direct use in ecological reports.     

Heritability estimates for callus growth and regeneration in desmodium

The F₂ and F₃ generations of two crosses (6123×13083 and 6123×144, with 6123 the regenerating parent) were evaluated for callus growth and regeneration capacity. Based on joint scaling tests and variance partitioning, neither callus growth nor regeneration fitted a simple additive-dominant genetic model. Heritability estimates obtained from parent-offspring regression analyses ranged from 0.65 to 0.77 for callus growth and from 0.19 to 0.46 for regeneration, with the range in both influenced by the cross and numerical scale employed. Members of two F₃ families exhibited much more vigorous and prolific regeneration than the regenerating parental genotype. Because many individuals in the segregating generations showed no evidence of regeneration, population distributions for this trait were severely truncated, or censored. Regression-order analysis was used to estimate means and variances of these censored populations. The association between poor callus growth and high regeneration capacity observed in the parental lines was absent from the F₂ and F₃ generations, indicating that no association between callus growth and regeneration was present.     

一种基于数学期望的亲子与同胞相关系数的估计方法

本文在对亲本平方和、子代同胞间平方和、同胞内平方和与亲子间乘积和求数学期望的基础上,估计亲子与同胞相关系数,导出了两套不同的亲子与同胞相关系数的估计公式,其中之一与Srivastava（1984）提出的完全一致,但估计方法较之具有直接性,推导过程得到了简化．