Dollo Parsimony Overestimates Ancestral Gene Content Reconstructions.

Ancestral reconstruction is a widely used technique that has been applied to understand the evolutionary history of gain and loss of gene families. Ancestral gene content can be reconstructed via different phylogenetic methods, but many current and previous studies employ Dollo parsimony. We hypothesize that Dollo parsimony is not appropriate for ancestral gene content reconstruction inferences based on sequence homology, as Dollo parsimony is derived from the assumption that a complex character cannot be regained. This premise does not accurately model molecular sequence evolution, in which false orthology can result from sequence convergence or lateral gene transfer. The aim of this study is to test Dollo parsimony's suitability for ancestral gene content reconstruction and to compare its inferences with a maximum likelihood-based approach that allows a gene family to be gained more than once within a tree. We first compared the performance of the two approaches on a series of artificial data sets each of 5,000 genes that were simulated according to a spectrum of evolutionary rates without gene gain or loss, so that inferred deviations from the true gene count would arise only from errors in orthology inference and ancestral reconstruction. Next, we reconstructed protein domain evolution on a phylogeny representing known eukaryotic diversity. We observed that Dollo parsimony produced numerous ancestral gene content overestimations, especially at nodes closer to the root of the tree. These observations led us to the conclusion that, confirming our hypothesis, Dollo parsimony is not an appropriate method for ancestral reconstruction studies based on sequence homology.

Systematic errors in orthology inference and their effects on evolutionary analyses.

The availability of complete sets of genes from many organisms makes it possible to identify genes unique to (or lost from) certain clades. This information is used to reconstruct phylogenetic trees; identify genes involved in the evolution of clade specific novelties; and for phylostratigraphy-identifying ages of genes in a given species. These investigations rely on accurately predicted orthologs. Here we use simulation to produce sets of orthologs that experience no gains or losses. We show that errors in identifying orthologs increase with higher rates of evolution. We use the predicted sets of orthologs, with errors, to reconstruct phylogenetic trees; to count gains and losses; and for phylostratigraphy. Our simulated data, containing information only from errors in orthology prediction, closely recapitulate findings from empirical data. We suggest published downstream analyses must be informed to a large extent by errors in orthology prediction that mimic expected patterns of gene evolution.

Lack of support for Deuterostomia prompts reinterpretation of the first Bilateria.

The bilaterally symmetric animals (Bilateria) are considered to comprise two monophyletic groups, Protostomia (Ecdysozoa and the Lophotrochozoa) and Deuterostomia (Chordata and the Xenambulacraria). Recent molecular phylogenetic studies have not consistently supported deuterostome monophyly. Here, we compare support for Protostomia and Deuterostomia using multiple, independent phylogenomic datasets. As expected, Protostomia is always strongly supported, especially by longer and higher-quality genes. Support for Deuterostomia, however, is always equivocal and barely higher than support for paraphyletic alternatives. Conditions that cause tree reconstruction errors-inadequate models, short internal branches, faster evolving genes, and unequal branch lengths-coincide with support for monophyletic deuterostomes. Simulation experiments show that support for Deuterostomia could be explained by systematic error. The branch between bilaterian and deuterostome common ancestors is, at best, very short, supporting the idea that the bilaterian ancestor may have been deuterostome-like. Our findings have important implications for the understanding of early animal evolution.

Computational discovery of hidden breaks in 28S ribosomal RNAs across eukaryotes and consequences for RNA Integrity Numbers.

In some eukaryotes, a 'hidden break' has been described in which the 28S ribosomal RNA molecule is cleaved into two subparts. The break is common in protostome animals (arthropods, molluscs, annelids etc.), but a break has also been reported in some vertebrates and non-metazoan eukaryotes. We present a new computational approach to determine the presence of the hidden break in 28S rRNAs using mapping of RNA-Seq data. We find a homologous break is present across protostomes although it has been lost in a small number of taxa. We show that rare breaks in vertebrate 28S rRNAs are not homologous to the protostome break. A break is found in just 4 out of 331 species of non-animal eukaryotes studied and, in three of these, the break is located in the same position as the protostome break suggesting a striking instance of convergent evolution. RNA Integrity Numbers (RIN) rely on intact 28S rRNA and will be consistently underestimated in the great majority of animal species with a break.

Phylogenomics investigation of sparids (Teleostei: Spariformes) using high-quality proteomes highlights the importance of taxon sampling.

Sparidae (Teleostei: Spariformes) are a family of fish constituted by approximately 150 species with high popularity and commercial value, such as porgies and seabreams. Although the phylogeny of this family has been investigated multiple times, its position among other teleost groups remains ambiguous. Most studies have used a single or few genes to decipher the phylogenetic relationships of sparids. Here, we conducted a thorough phylogenomic analysis using five recently available Sparidae gene-sets and 26 high-quality, genome-predicted teleost proteomes. Our analysis suggested that Tetraodontiformes (puffer fish, sunfish) are the closest relatives to sparids than all other groups used. By analytically comparing this result to our own previous contradicting finding, we show that this discordance is not due to different orthology assignment algorithms; on the contrary, we prove that it is caused by the increased taxon sampling of the present study, outlining the great importance of this aspect in phylogenomic analyses in general.

StarSeeker: an automated tool for mature duplex microRNA sequence identification based on secondary structure modeling of precursor molecule.

BACKGROUND: MicroRNAs (miRNAs) are small, non-coding RNA molecules that play a key role in gene regulation in both plants and animals. MicroRNA biogenesis involves the enzymatic processing of a primary RNA transcript. The final step is the production of a duplex molecule, often designated as miRNA:miRNA*, that will yield a functional miRNA by separation of the two strands. This miRNA will be incorporated into the RNA-induced silencing complex, which subsequently will bind to its target mRNA in order to suppress its expression. The analysis of miRNAs is still a developing area for computational biology with many open questions regarding the structure and function of this important class of molecules. Here, we present StarSeeker, a simple tool that outputs the putative miRNA* sequence given the precursor and the mature sequences. RESULTS: We evaluated StarSeeker using a dataset consisting of all plant sequences available in miRBase (6992 precursor sequences and 8496 mature sequences). The program returned a total of 15,468 predicted miRNA* sequences. Of these, 2650 sequences were matched to annotated miRNAs (~ 90% of the miRBase-annotated sequences). The remaining predictions could not be verified, mainly because they do not comply with the rule requiring the two overhanging nucleotides in the duplex molecule. CONCLUSIONS: The expression pattern of some miRNAs in plants can be altered under various abiotic stress conditions. Potential miRNA* molecules that do not degrade can thus be detected and also discovered in high-throughput sequencing data, helping us to understand their role in gene regulation.