Last modified on 24 August 2012.

Literature Reference:

Shang L., Xu W., Ozer S., and Gutell R.R. (2012).
Structural Constraints Identified with Covariation Analysis in Ribosomal RNA.
PLoS ONE, 7(6):e39383.

Manuscript Figures:

File formats: PDF, Adobe Portable Document Format; TIF, Tagged Image File Format image.
Category Description Links
Figures Figure 1. The highlight and underlying concepts of the PEC based covariation analysis in rCAD. PDF | TIF
Figure 2. The flowchart of analysis in the identification of base-pairs and neighbor effects. PDF | TIF
Figure 3. The precision of top N ranked prediction plot with different covariation methods in the identification of base pairs using different data sets. PDF | TIF
Figure 4. The number of true positives and false positives identified in different methods. PDF | TIF
Figure 5. The base pairs (true positives) identified by PEC/JN-Best and MI/JN-Best are plotted onto the T. thermophilus 16S rRNA secondary structure diagram. PDF | TIF
Figure 6. For each method, the number of true positives and false positives identified in the Joint N-Best calculation (nucleation pairs), following helix extension procedure (extended pairs), and sum of them are shown as a stacked histogram. PDF | TIF
Figure 7. Base pairs in the Bacterial 16S rRNA structure model that are identified with the helix extension method. PDF | TIF
Figure 8. The distribution of purity score and average conservation (or informational entropy) for the two nucleotides that form a base pair. PDF | TIF
Figure 9. The secondary structural diagram of T. thermophilus 16S rRNA reveals all identified neighbor effects. PDF | TIF

Supplemental Figures and Tables:

File formats: FASTA, Fasta alignment format; TIF, Tagged Image File Format image; XLS, Microsoft Excel 97-2003 Workbook; ZIP, ZIP archive.
Category Description Links
Data Supplemental Dataset S1. The bacterial 16S rRNA sequence alignment used in this analysis. FASTA
Supplemental Dataset S2. The bacterial 23S rRNA sequence alignment used in this analysis. FASTA
Supplemental Dataset S3. The bacterial 5S rRNA sequence alignment used in this analysis. FASTA
Figures Supplemental Figure S1. Pseudo code of phylogenetic event counting algorithm. PDF | TIF
Supplemental Figure S2. Variation/covariation analysis of the secondary structure of the bacterial 16S rRNA sequence alignment. PDF | TIF
Supplemental Figure S3. Graphical representation of N-Best method. PDF | TIF
Supplemental Figure S4. The secondary (A) and three-dimensional structure (B) of S. cerevisiae Phe tRNA with neighbor effect identified in 1992. PDF | TIF
Supplemental Figure S5. The underlying principle of coarse filter that reduce the number of pairwise comparison. PDF | TIF
Supplemental Figure S6. Base pairs in the Bacterial 16S rRNA structure model that are identified with the helix extension method using different nucleation pairs. PDF | TIF
Supplemental Figure S7. Example of the determination of a purity score. PDF | TIF
Supplemental Figure S8. The maximal distance between the positions defined to be a neighbor effect is determined from a comparison of the number of phylogenetic events. PDF | TIF
Supplemental Figure Legends. HTML
Tables Supplemental Table 1. The phylogenetic distribution and sequence similarities of the 16S, 5S and 23S rRNA datasets used in analysis. XLS
Supplemental Table 2. Detail information about all 14276 pairs of columns process in Phylogenetic Event Counting analysis on 16S rRNA data set. XLS
Supplemental Table 3. The unique and common pairs identified by PEC/JN-Best, MIxy/JN-Best and MIp/JN-Best using the 16S, 5S and 23S rRNA data sets. XLS
Supplemental Table 4. The complete list of nucleation pairs and extended pairs involved in the Helix Extension analysis on the 16S, 5S and 23S rRNA data sets. XLS
Supplemental Table 5. A complete list of neighbor effects identified with our analysis. XLS
Supplemental Table 6. The evaluation of the "new putative interactions in 16S rRNA" discovered by 2 other groups. XLS