alignments_and_trees

This zip file contains the alignments and trees, as well as a ReadMe file

baits-120-60

The probe sequences to target phytochrome, phototropin and neochrome genes, with a special focus on those of hornworts and ferns

Statistical significance of explanatory variables in the best-fitting models for the data set with OD ratios and without OD ratios.

<p>The best-fitting models were determined by comparing AIC values among models that considered all possible combinations of explanatory variables. Statistical significance was determined using an ANOVA model with type III sums-of-squares (SS). Variables with <i>P<</i>0.05 are shown in bold. Partial r² values (coefficient of determination) were determined by dividing SS values of each factor by total SS.</p>1<p>Numerator (first number) and denominator (second number) degrees of freedom (df) for F-test.</p>2<p>RNA integrity number (RIN).</p>3<p>Mass of total RNA sequenced.</p><p>Other abbreviations as per <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050226#pone-0050226-t001" target="_blank">Table 1</a>.</p

Effects of tissue type and age on metrics of RNA quality and sequencing.

<p>Significant effects (<i>P<</i>0.05) are shown in bold.</p>1<p>Measured as µg of total RNA isolated from a given tissue.</p>2<p>Numerator degrees of freedom (ndf) of F-statistic.</p>3<p>Denominator degrees of freedom (ddf) of F-statistic. ddf are low for RNA mass because an unequal variance model was used to account for heteroscedasticity in residuals among tissues.</p>4<p>F-statistic from analysis of variance (ANOVA).</p>5<p>P-value of F-statistic given ndf and ddf.</p

Factors that significantly predicted the number of large scaffolds.

<p>Among our measures of RNA quality, (A) RNA integrity number (RIN) and (B) OD 260/230 ratio were the strongest predictors of the number of scaffolds ≥1000 bp. (C) Sequencing platform also had a strong effect on number of large scaffolds (<i>P</i><0.001, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050226#pone-0050226-t002" target="_blank">Table 2</a>; numbers at the base of bars show sample size), and (D) mass of RNA sequenced had a weak but detectable effect (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050226#pone-0050226-t002" target="_blank">Table 2</a>). Note, for most samples we used 20, 30 or 40 µg of total RNA for sequencing, but a few samples used intermediate or lower amounts.</p

Variation among tissue types in RNA quality and transcriptome size.

<p>We observed differences among tissue types for (A) total RNA mass (µg) isolated, (B) RIN, (C) sequencing depth and (D) number of scaffolds. For each tissue, we show the mean +1 SE and sample size at the base of columns. A posteriori pairwise contrasts among means corrected for multiple comparisons are shown in Supplemental Tables 3 and 5.</p

Schematic representation of the method used to assemble Illumina reads into contigs, and contigs into scaffolds.

<p>All reads were initially assembled into contigs using the de Bruijn graph method without using information about paired-end reads (shown by blue dashed lines). A contig’s sequence was resolved at every base. Contigs were then assembled into longer scaffolds by connecting contigs that contained paired-end reads assembled into separate contigs. Assembling scaffolds in this way allowed us to create longer sequences of known length, but sometimes there were gaps of unknown sequence. These gaps were constrained to represent <5% of total sequence length.</p