MicroRNAs (miRNAs) are among the most important regulatory elements of gene expression in animals and plants. However, their origin and evolutionary dynamics have not been studied systematically. In this paper, we identified putative miRNA genes in 11 plant species using the bioinformatic technique and examined their evolutionary changes. Our homology search indicated that no miRNA gene is currently shared between green algae and land plants. The number of miRNA genes has increased substantially in the land plant lineage, but after the divergence of eudicots and monocots, the number has changed in a lineage-specific manner. We found that miRNA genes have originated mainly by duplication of preexisting miRNA genes or protein-coding genes. Transposable elements also seem to have contributed to the generation of species-specific miRNA genes. The relative importance of these mechanisms in plants is quite different from that in Drosophila species, where the formation of hairpin structures in the genomes seems to be a major source of miRNA genes. This difference in the origin of miRNA genes between plants and Drosophila may be explained by the difference in the binding to target mRNAs between plants and animals. We also found that young miRNA genes are less conserved than old genes in plants as well as in Drosophila species. Yet, nearly half of the gene families in the ancestor of flowering plants have been lost in at least one species examined. This indicates that the repertoires of miRNA genes have changed more dynamically than previously thought during plant evolution.

MicroRNAs (miRs) regulate gene expression at the posttranscriptional level. To obtain some insights into the origins and evolutionary patterns of miR genes, we have identified miR genes in the genomes of 12 Drosophila species by bioinformatics approaches and examined their evolutionary changes. The results showed that the extant and ancestral Drosophila species had more than 100 miR genes and frequent gains and losses of miR genes have occurred during evolution. Although many miR genes appear to have originated from random hairpin structures in intronic or intergenic regions, duplication of miR genes has also contributed to the generation of new miR genes. Estimating the rate of nucleotide substitution of miR genes, we have found that newly arisen miR genes have a substitution rate similar to that of synonymous nucleotide sites in protein-coding genes and evolve almost neutrally. This suggests that most new miR genes have not acquired any important function and would become inactive. By contrast, old miR genes show a substitution rate much lower than the synonymous rate. Moreover, paired and unpaired nucleotide sites of miR genes tend to remain unchanged during evolution. Therefore, once miR genes acquired their functions, they appear to have evolved very slowly, maintaining essentially the same structures for a long time.

Global sequencing of genomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continued to reveal new genetic variants that are the key to unraveling its early evolutionary history and tracking its global spread over time. Here we present the heretofore cryptic mutational history and spatiotemporal dynamics of SARS-CoV-2 from an analysis of thousands of high-quality genomes. We report the likely most recent common ancestor of SARS-CoV-2, reconstructed through a novel application and advancement of computational methods initially developed to infer the mutational history of tumor cells in a patient. This progenitor genome differs from genomes of the first coronaviruses sampled in China by three variants, implying that none of the earliest patients represent the index case or gave rise to all the human infections. However, multiple coronavirus infections in China and the United States harbored the progenitor genetic fingerprint in January 2020 and later, suggesting that the progenitor was spreading worldwide months before and after the first reported cases of COVID-19 in China. Mutations of the progenitor and its offshoots have produced many dominant coronavirus strains that have spread episodically over time. Fingerprinting based on common mutations reveals that the same coronavirus lineage has dominated North America for most of the pandemic in 2020. There have been multiple replacements of predominant coronavirus strains in Europe and Asia as well as continued presence of multiple high-frequency strains in Asia and North America. We have developed a continually updating dashboard of global evolution and spatiotemporal trends of SARS-CoV-2 spread (http://sars2evo.datamonkey.org/).

Motivation: Analyses of data generated from bulk sequencing of tumors have revealed extensive genomic heterogeneity within patients. Many computational methods have been developed to enable the inference of genotypes of tumor cell populations (clones) from bulk sequencing data. However, the relative and absolute accuracy of available computational methods in estimating clone counts and clone genotypes is not yet known.Results: We have assessed the performance of nine methods, including eight previously-published and one new method (CloneFinder), by analyzing computer simulated datasets. CloneFinder, LICHeE, CITUP and cloneHD inferred clone genotypes with low error (<5% per clone) for a majority of datasets in which the tumor samples contained evolutionarily-related clones. Computational methods did not perform well for datasets in which tumor samples contained mixtures of clones from different clonal lineages. Generally, the number of clones was underestimated by cloneHD and overestimated by PhyloWGS, and BayClone2, Canopy and Clomial required prior information regarding the number of clones. AncesTree and Canopy did not produce results for a large number of datasets. Overall, the deconvolution of clone genotypes from single nucleotide variant (SNV) frequency differences among tumor samples remains challenging, so there is a need to develop more accurate computational methods and robust software for clone genotype inference.Availability and implementation: CloneFinder is implemented in Python and is available from https://github.com/gstecher/CloneFinderAPI.Supplementary information: Supplementary data are available at Bioinformatics online.

O1 Regulation of genes by telomere length over long distances

Motivation: Tumor sequencing has entered an exciting phase with the advent of single-cell techniques that are revolutionizing the assessment of single nucleotide variation (SNV) at the highest cellular resolution. However, state-of-the-art single-cell sequencing technologies produce data with many missing bases (MBs) and incorrect base designations that lead to false-positive (FP) and false-negative (FN) detection of somatic mutations. While computational methods are available to make biological inferences in the presence of these errors, the accuracy of the imputed MBs and corrected FPs and FNs remains unknown.Results: Using computer simulated datasets, we assessed the robustness performance of four existing methods (OncoNEM, SCG, SCITE and SiFit) and one new method (BEAM). BEAM is a Bayesian evolution-aware method that improves the quality of single-cell sequences by using the intrinsic evolutionary information in the single-cell data in a molecular phylogenetic framework. Overall, BEAM and SCITE performed the best. Most of the methods imputed MBs with high accuracy, but effective detection and correction of FPs and FNs is a challenge, especially for small datasets. Analysis of an empirical dataset shows that computational methods can improve both the quality of tumor single-cell sequences and their utility for biological inference. In conclusion, tumor cells descend from pre-existing cells, which creates evolutionary continuity in single-cell sequencing datasets. This information enables BEAM and other methods to correctly impute missing data and incorrect base assignments, but correction of FPs and FNs remains challenging when the number of SNVs sampled is small relative to the number of cells sequenced.Availability and implementation: BEAM is available on the web at https://github.com/SayakaMiura/BEAM.