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MicroRNAs (miRNAs) are small noncoding RNAs that are key players in the regulation of gene expression. In the past decade, with the increasing accessibility of high-throughput sequencing technologies, different methods have been developed to identify miRNAs, most of which rely on preexisting reference genomes. However, when a reference genome is absent or is not of high quality, such identification becomes more difficult. In this context, we developed BrumiR, an algorithm that is able to discover miRNAs directly and exclusively from small RNA (sRNA) sequencing (sRNA-seq) data. We benchmarked BrumiR with datasets encompassing animal and plant species using real and simulated sRNA-seq experiments. The results demonstrate that BrumiR reaches the highest recall for miRNA discovery, while at the same time being much faster and more efficient than the state-of-the-art tools evaluated. The latter allows BrumiR to analyze a large number of sRNA-seq experiments, from plants or animal species. Moreover, BrumiR detects additional information regarding other expressed sequences (sRNAs, isomiRs, etc.), thus maximizing the biological insight gained from sRNA-seq experiments. Additionally, when a reference genome is available, BrumiR provides a new mapping tool (BrumiR2reference) that performs an a posteriori exhaustive search to identify the precursor sequences. Finally, we also provide a machine learning classifier based on a random forest model that evaluates the sequence-derived features to further refine the prediction obtained from the BrumiR-core. The code of BrumiR and all the algorithms that compose the BrumiR toolkit are freely available at https://github.com/camoragaq/BrumiR. [ABSTRACT FROM AUTHOR]

Adaptation to rapid environmental changes must occur within a short-time scale. In this context, studies of invasive species may provide insights into the underlying mechanisms of rapid adaptation as these species have repeatedly encountered and adapted to novel environmental conditions. We investigated how invasive and noninvasive genotypes of Drosophila suzukii deal with oxidative stress at the phenotypic and molecular levels. We also studied the impact of transposable element (TE) insertions on the gene expression in response to stress. Our results show that flies from invasive areas (France and the United States) live longer in natural conditions than the ones from native Japanese areas. As expected, lifespan for all genotypes was significantly reduced following exposure to paraquat, but this reduction varied among genotypes (genotype-by-environment interaction) with invasive genotypes appearing more affected by exposure than noninvasive ones. A transcriptomic analysis of genotypes upon paraquat treatment detected many genes differentially expressed (DE). Although a small core set of genes were DE in all genotypes following paraquat exposure, much of the response of each genotype was unique. Moreover, we showed that TEs were not activated after oxidative stress and DE genes were significantly depleted of TEs. In conclusion, it is likely that transcriptomic changes are involved in the rapid adaptation to local environments. We provide new evidence that in the decade since the invasion from Asia, the sampled genotypes in Europe and the United States of D. suzukii diverged from the ones from the native area regarding their phenotypic and genomic response to oxidative stress. [ABSTRACT FROM AUTHOR]

Motivation Analysis of differential expression of genes is often performed to understand how the metabolic activity of an organism is impacted by a perturbation. However, because the system of metabolic regulation is complex and all changes are not directly reflected in the expression levels, interpreting these data can be difficult. Results In this work, we present a new algorithm and computational tool that uses a genome-scale metabolic reconstruction to infer metabolic changes from differential expression data. Using the framework of constraint-based analysis, our method produces a qualitative hypothesis of a change in metabolic activity. In other words, each reaction of the network is inferred to have increased, decreased, or remained unchanged in flux. In contrast to similar previous approaches, our method does not require a biological objective function and does not assign on/off activity states to genes. An implementation is provided and it is available online. We apply the method to three published datasets to show that it successfully accomplishes its two main goals: confirming or rejecting metabolic changes suggested by differentially expressed genes based on how well they fit in as parts of a coordinated metabolic change, as well as inferring changes in reactions whose genes did not undergo differential expression. Availability and implementation github.com/htpusa/moomin. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]