The mitochondrial genomes of Bilateria are relatively conserved in their protein-coding, rRNA, and tRNA gene complement, but the order of these genes can range from very conserved to very variable depending on the taxon. The supposedly conserved gene order of Annelida has been used to support the placement of some taxa within Annelida. Recently, authors have cast doubts on the conserved nature of the annelid gene order. Various factors may influence gene order variability including, among others, increased substitution rates, base composition differences, structure of noncoding regions, parasitism, living in extreme habitats, short generation times, and biomineralization. However, these analyses were neither done systematically nor based on well-established reference trees. Several focused on only a few of these factors and biological factors were usually explored ad-hoc without rigorous testing or correlation analyses. Herein, we investigated the variability and evolution of the annelid gene order and the factors that potentially influenced its evolution, using a comprehensive and systematic approach. The analyses were based on 170 genomes, including 33 previously unrepresented species. Our analyses included 706 different molecular properties, 20 life-history and ecological traits, and a reference tree corresponding to recent improvements concerning the annelid tree. The results showed that the gene order with and without tRNAs is generally conserved. However, individual taxa exhibit higher degrees of variability. None of the analyzed life-history and ecological traits explained the observed variability across mitochondrial gene orders. In contrast, the combination and interaction of the best-predicting factors for substitution rate and base composition explained up to 30% of the observed variability. Accordingly, correlation analyses of different molecular properties of the mitochondrial genomes showed an intricate network of direct and indirect correlations between the different molecular factors. Hence, gene order evolution seems to be driven by molecular evolutionary aspects rather than by life history or ecology. On the other hand, variability of the gene order does not predict if a taxon is difficult to place in molecular phylogenetic reconstructions using sequence data or not. We also discuss the molecular properties of annelid mitochondrial genomes considering canonical views on gene evolution and potential reasons why the canonical views do not always fit to the observed patterns without making some adjustments. [Annelida; compositional biases; ecology; gene order; life history; macroevolution; mitochondrial genomes; substitution rates.].

During recent decades, many new emerging or re-emerging RNA viruses have been found in plants through the development of deep-sequencing technology and big data analysis. These findings largely changed our understanding of the origin, evolution and host range of plant RNA viruses. There is evidence that their genetic composition originates from viruses, and host populations play a key role in the evolution and host adaptability of plant RNA viruses. In this mini-review, we describe the state of our understanding of the evolution of plant RNA viruses in view of compositional biases and explore how they adapt to the host. It appears that adenine rich (A-rich) coding sequences, low CpG and UpA dinucleotide frequencies and lower codon usage patterns were found in the vast majority of plant RNA viruses. The codon usage pattern of plant RNA viruses was influenced by both natural selection and mutation pressure, and natural selection mostly from hosts was the dominant factor. The codon adaptation analyses support that plant RNA viruses probably evolved a dynamic balance between codon adaptation and deoptimization to maintain efficient replication cycles in multiple hosts with various codon usage patterns. In the future, additional combinations of computational and experimental analyses of the nucleotide composition and codon usage of plant RNA viruses should be addressed.

Due to the low capacity of contemporary position-sensitive detectors in atom probe tomography (APT) to detect multiple events, material analyses that exhibit high numbers of multiple events are the most subject to compositional biases. To solve this limitation, some researchers have developed statistical correction algorithms. However, those algorithms are only efficient when one is confronted with homogeneous materials having nearly the same evaporation field between elements. Therefore, dealing with more complex materials must be accompanied by a better understanding of the signal loss mechanism during APT experiments. By modeling the evaporation mechanism and the whole APT detection system, it may be possible to predict compositional and spatial biases induced by the detection system. This paper introduces a systematic study of the impact of the APT detection system on material analysis through the development of a simulation tool.