Leave-One-Out Cross-Validation (LOO-CV) scores of alphaproteobacterial genomes under two different binary phyloclassifiers.

<p>A. Score distribution of genomes under the binary tRNA-CIF-based phyloclassifier as sketched in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi-1003454-g002" target="_blank">Figure 2</a>. The score of a genome in this classifier is the summation of differences in heights of the features of its tRNAs in the RRCH and RSR function logos in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi-1003454-g002" target="_blank">Figure 2</a>. B. Scores under the “zero” total tRNA sequence-based phyloclassifer defined in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#s4" target="_blank">Materials and Methods</a> and conducted as a control. Here the score of a genome is just the sum of log-odds of its tRNA sequences in two class-specific sequence profiles from the RRCH and RSR clades. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi.1003454.s010" target="_blank">Figure S2</a> for alternative treatments of missing data under other methods. Complete source code and data to reproduce these results and those in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi.1003454.s010" target="_blank">Figure S2</a> are in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi.1003454.s002" target="_blank">Dataset S2</a>.</p

Function logos of structurally aligned tRNA data as calculated by LOGOFUN [36] for two groups of Alphaproteobacteria and overview of tRNA-CIF-based binary phyloclassification.

<p>Function logos generalize sequence logos. They are the sole means by which we predict tRNA Class-Informative Features (CIFs), which form the basis of the scoring schemes of the classifiers reported in this work. A full derivation of the mathematics of function logos is provided in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi.1003454-Freyhult1" target="_blank">[36]</a>. The tRNA-CIF-based phyloclassifier shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi-1003454-g003" target="_blank">Figure 3A</a> sums differences in heights of features between two function logos for a set of genomically derived tRNAs. Complete source code and data to reproduce the function logos in this figure are in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi.1003454.s001" target="_blank">Dataset S1</a>.</p

Seven-way tRNA-CIF-based phyloclassification of alphaproteobacterial genomes by the default multilayer perceptron in WEKA.

<p>Each test genome classified is assigned a probability of classification into each of the seven alphaproteobacterial clades indicated. Bootstrap support values under resampling of tRNA sites against (left) all tRNA CIFs and (right) CIFs with heights bits and model retraining (100 replicates). All support values correspond to most probable clade as shown except for <i>Stappia</i> and <i>Labrenzia</i> for which they correspond to Rhizobiales. Complete source code and data to produce this figure, including the full WEKA model for classification of other alphaproteobacterial genomes and code to produce such models from scratch, is provided in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi.1003454.s004" target="_blank">Dataset S4</a>.</p

FastUniFrac-based phylogenetic tree of alphaproteobacteria using tRNA data computed according to the methods of [51].

<p>The FastUniFrac algorithm was recently adapted as a phylogenomic method using tRNA genes. Like the supermatrix phylogenomic approach on tRNAs with results shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi.1003454.s011" target="_blank">Figures S3</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi.1003454.s012" target="_blank">S4</a>, this method uses unfiltered total sequence information of tRNAs. In contrast to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi-1003454-g005" target="_blank">Figure 5</a>, both in this figure and in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi.1003454.s011" target="_blank">Figures S3</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi.1003454.s012" target="_blank">S4</a>, all SAR11 strains are affiliated with Rickettsiales. For reasons shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi-1003454-g007" target="_blank">Figure 7</a>, we argue these results are artifacts of convergence in tRNA base contents. Complete source code and data to reproduce these results are in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi.1003454.s005" target="_blank">Dataset S5</a>.</p

Breakout of class contributions to scores under the tRNA CIF-based binary phyloclassifier.

<p>Contributions of each functional variety of tRNA, or class, to the tRNA-CIF-based phyloclassifier scores in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi-1003454-g003" target="_blank">Figure 3A</a>. Different SAR11 strain tRNAs are plotted separately by genome of origin. Complete source code and data to reproduce these results are in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi.1003454.s003" target="_blank">Dataset S3</a>.</p

A universal schema for tRNA interaction networks.

<p>tRNAs interact to varying degrees of specificity within a strongly conserved network of protein and RNA complexes. The simultaneous and conflicting requirements of “identity” and “conformity” on tRNAs create potential deleterious pleiotropic effects when components of the network mutate or are transferred to foreign cells by HGT. They also facilitate the bioinformatic prediction of Class-Informative Features (CIFs) from tRNAs that function together in the same or similar networks.</p

The First Complete Plastid Genome from Joinvilleaceae (<i>J</i>. <i>ascendens</i>; Poales) Shows Unique and Unpredicted Rearrangements - Fig 2

<p><b>I</b>–<b>II.</b> Diagram of the inversions that occurred within the large single-copy subregion of the plastome of the Joinvilleaceae-Poaceae lineage and <i>Joinvillea</i> lineage respectively. ‘AP’ denotes the ancestral plastome, which signifies the pre-inversion state (as observed in <i>Typha latifolia</i>) and the red circle signifies the ancestral plastome before the divergence between Joinvilleaceae and Poaceae. The arrows (A–D) represent large-scale (750–23,000 bases) inversion events. Triangular markers are placed on each colored region to demonstrate orientation. Subregions are not drawn to scale. <b>III.</b> A simplified cladogram representing the relationships between Joinvilleaceae, Poaceae, and Typhaceae. Arrows indicate the hypothesized relative position of each of the mutations (A–D) and one 300 base inversion exclusive to the grass lineage. The ‘ancestral plastome’ indication and red circle represent the positions of the hypothesized starting points from I and II. Branch lengths are not to scale.</p

Map of BP, showing the five sample locations used in this study.

<p>Modified from Havig et al. (2011) and originally drafted by G.R. Osburn.</p

BPEG binning and consensus genome statistics.

*<p>Read assignment. ¹With the exception of homology binning information, all other statistics shown use phylum-level tetranucleotide binning data.</p

BPEG sequence statistics.

<p>BPEG sequence statistics.</p