A practical guide to methods controlling false discoveries in computational biology, bioRxiv, 2018-10-31

In high-throughput studies, hundreds to millions of hypotheses are typically tested. Statistical methods that control the false discovery rate (FDR) have emerged as popular and powerful tools for error rate control. While classic FDR methods use only p-values as input, more modern FDR methods have been shown to increase power by incorporating complementary information as informative covariates to prioritize, weight, and group hypotheses. However, there is currently no consensus on how the modern methods compare to one another. We investigated the accuracy, applicability, and ease of use of two classic and six modern FDR-controlling methods by performing a systematic benchmark comparison using simulation studies as well as six case studies in computational biology. Methods that incorporate informative covariates were modestly more powerful than classic approaches, and did not underperform classic approaches, even when the covariate was completely uninformative. The majority of methods were successful at controlling the FDR, with the exception of two modern methods under certain settings. Furthermore, we found the improvement of the modern FDR methods over the classic methods increased with the informativeness of the covariate, total number of hypothesis tests, and proportion of truly non-null hypotheses. Modern FDR methods that use an informative covariate provide advantages over classic FDR-controlling procedures, with the relative gain dependent on the application and informativeness of available covariates. We present our findings as a practical guide and provide recommendations to aid researchers in their choice of methods to correct for false discoveries.

biorxiv bioinformatics 200-500-users 2018

RAxML-NG A fast, scalable, and user-friendly tool for maximum likelihood phylogenetic inference, bioRxiv, 2018-10-19

AbstractMotivationPhylogenies are important for fundamental biological research, but also have numerous applications in biotechnology, agriculture, and medicine. Finding the optimal tree under the popular maximum like-lihood (ML) criterion is known to be NP-hard. Thus, highly optimized and scalable codes are needed to analyze constantly growing empirical datasets.ResultsWe present RAxML-NG, a from scratch re-implementation of the established greedy tree search algorithm of RAxMLExaML. RAxML- NG offers improved accuracy, flexibility, speed, scalability, and usability compared to RAxMLExaML. On taxon-rich datasets, RAxML-NG typically finds higher-scoring trees than IQTree, an increasingly popular recent tool for ML-based phylogenetic inference (although IQ-Tree shows better stability). Finally, RAxML-NG introduces several new features, such as the detection of terraces in tree space and a the recently introduced transfer bootstrap support metric.AvailabilityThe code is available under GNU GPL at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsgithub.comamkozlovraxml-ng.RAxML-NG>httpsgithub.comamkozlovraxml-ng.RAxML-NG<jatsext-link> web service (maintained by Vital- IT) is available at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsraxml-ng.vital-it.ch>httpsraxml-ng.vital-it.ch<jatsext-link>.Contactalexey.kozlov@h-its.org

biorxiv bioinformatics 200-500-users 2018

 

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