Beardohlsen3000

With these metabolic and transcriptional changes, bglu28/30 mutants displayed obvious growth retardation under -S. Overall, our results indicate that BGLU28 and BGLU30 are required for -S-induced GSL catabolism and contribute to sustained plant growth under -S by recycling sulfate to primary S metabolism. © The Author(s) 2020. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email journals.permissions@oup.com.MOTIVATION Metagenomics refers to the study of complex samples containing of genetic contents of multiple individual organisms, and thus, has been used to elucidate the microbiome and resistome of a complex sample. The microbiome refers to all microbial organisms in a sample, and the resistome refers to all of the antimicrobial resistance (AMR) genes in pathogenic and non-pathogenic bacteria. Single nucleotide polymorphisms (SNPs) can be effectively used to "fingerprint" specific organisms and genes within the microbiome and resistome, and trace their movement accross various samples. However, in order to effectively use these SNPs for this traceability, a scalable and accurate metagenomics SNP caller is needed. Moreover, such a SNP caller should not be reliant on reference genomes since 95% of microbial species is unculturable, making the determination of a reference genome extremely challenging. In this paper, we address this need. RESULTS We present LueVari, a reference-free SNP caller based on the read colored de Bruijn graph, an extension of the traditional de Bruijn graph that allows repeated regions longer than the k-mer length and shorter than the read length to be identified unambiguously. LueVari is able to identify SNPs in both AMR genes and chromosomal DNA from shotgun metagenomics data with reliable sensitivity (between 91% to 99%) and precision (between 71% to 99%) as the performance of competing methods varies widely. Furthermore, we show that LueVari constructs sequences containing the variation which span up to 97.8% of genes in datasets which can be helpful in detecting distinct AMR genes in large metagenomic datasets. AVAILABILITY Code and datasets are publicly available at https//github.com/baharpan/cosmo/tree/LueVari. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.SUMMARY ShinySOM offers a user-friendly interface for reproducible, high-throughput analysis of high-dimensional flow and mass cytometry data guided by self-organizing maps. The software implements a FlowSOM-style workflow, with improvements in performance, visualizations and data dissection possibilities. The outputs of the analysis include precise statistical information about the dissected samples, and R-compatible metadata useful for batch processing of large sample volumes. AVAILABILITY AND IMPLEMENTATION ShinySOM is free and open-source, available online at gitlab.com/exaexa/ShinySOM. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) 2020. Published by Oxford University Press.MOTIVATION As the density of sampled population increases, especially as studies incorporate aspects of the spatial landscape to study evolutionary processes, efficient simulation of genetic data under the coalescent becomes a primary challenge. Beyond the computational demands, coalescence-based simulation strategies have to be reconsidered because traditional assumptions about the dynamics of coalescing lineages within local populations may be violated (e.g., more than two daughter lineages may coalesce to a parent at low population densities). Specifically, to efficiently assign n lineages to m parents, the order relation between n and m strongly affects the relevant algorithm for the coalescent simulator (e.g., only when $$ n \lt \sqrt2m $$, is it reasonable to assume that two lineages, at most, can be assigned to the same parent). Controlling the details of the simulation model as a function of n and m is then crucial to represent accurately and efficiently the assignment process, but current implementcs are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.MOTIVATION Imputation procedures in biomedical fields have turned into statistical practice, since further analyses can be conducted ignoring the former presence of missing values. In particular, non-parametric imputation schemes like the Random Forest have shown favorable imputation performance compared to the more traditionally used MICE procedure. However, their effect on valid statistical inference has not been analyzed so far. This paper closes this gap by investigating their validity for inferring mean differences in incompletely observed pairs while opposing them to a recent approach that only works with the given observations at hand. RESULTS Our findings indicate that machine learning schemes for (multiply) imputing missing values may inflate type-I-error or result in comparably low power in small to moderate matched pairs, even after modifying the test statistics using Rubin's multiple imputation rule. In addition to an extensive simulation study, an illustrative data example from a breast cancer gene study has been considered. learn more AVAILABILITY The corresponding R-code can be accessed through the authors and the gene expression data can be downloaded at www.gdac.broadinstitute.org. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.Importance Biologics revolutionized the treatment of psoriasis. Biologics are given in a fixed dose, but lower doses might be possible. Objective To investigate whether dose reduction (DR) of biologics in patients with stable psoriasis is noninferior to usual care (UC). Design, Setting, and Participants This pragmatic, open-label, prospective, controlled, noninferiority randomized clinical trial was conducted from March 1, 2016, to July 22, 2018, at 6 dermatology departments in the Netherlands. A total of 120 patients with plaque psoriasis and stable low disease activity who were receiving treatment with adalimumab, etanercept, or ustekinumab were studied. Interventions Patients were randomized 11 to DR (n = 60) or UC (n = 60). In the DR group, injection intervals were prolonged stepwise, leading to 67% and 50% of the original dose. Main Outcomes and Measures The primary outcome was between-group difference in disease activity corrected for baseline at 12 months compared with the predefined noninferiority margin of 0.