Automated Author ProfileCraig, Catherine Waggoner
Washington State University
Craig, Catherine Waggoner
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 2.2 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This study presents a manually constructed alignment of nearly complete rRNA genes from most animal clades (371 taxa from ∼33 of the ∼36 metazoan phyla), expanded from the 197 sequences in a previous study. This thorough, taxon-rich alignment, available at http://www.wsu.edu/≃jmallatt/research/rRNAalignment.html and in the Dryad Repository (doi: http://dx.doi.org/10.5061/dryad.1v62kr3q), is based rigidly on the secondary structure of the SSU and LSU rRNA molecules, and is annotated in detail, including labeling of the erroneous sequences (contaminants). The alignment can be used for future studies of the molecular evolution of rRNA. Here, we use it to explore if the larger number of sequences produces an improved phylogenetic tree of animal relationships. Disappointingly, the resolution did not improve, neither when the standard maximum-likelihood method was used, nor with more sophisticated methods that partitioned the rRNA into paired and unpaired sites (stem, loop, bulge, junction), or accounted for the evolution of the paired sites. For example, no doublet model of paired-site substitutions (16-state, 16A and 16B, 7A–F, or 6A–C models) corrected the placement of any rogue taxa or increased resolution. The following findings are from the simplest, standard, ML analysis. The 371-taxon tree only imperfectly supported the bilaterian clades of Lophotrochozoa and Ecdysozoa, and this problem remained after 17 taxa with unstably positioned sequences were omitted from the analysis. The problem seems to stem from base-compositional heterogeneity across taxa and from an overrepresentation of highly divergent sequences among the newly added taxa (e.g., sequences from Cephalopoda, Rotifera, Acoela, and Myxozoa). The rogue taxa continue to concentrate in two locations in the rRNA tree: near the base of Arthropoda and of Bilateria. The approximately uncertain (AU) test refuted the monophyly of Mollusca and of Chordata, probably due to long-branch attraction of the highly divergent cephalopod and urochordate sequences out of those clades. Unlikely to be correct, these refutations show for the first time that rRNA phylogeny can support some ‘wrong’ clades. Along with its weaknesses, the rRNA tree has strengths: It recovers many clades that are supported by independent evidence (e.g., Metazoa, Bilateria, Hexapoda, Nonoculata, Ambulacraria, Syndermata, and Thecostraca with Malacostraca) and shows good resolution within certain groups (e.g., in Platyhelminthes, Insecta, Cnidaria). As another strength, the newly added rRNA sequences yielded the first rRNA-based support for Carnivora and Cetartiodactyla (dolphin + llama) in Mammalia, for basic subdivisions of Bryozoa (‘Gymnolaemata + Stenolaemata’ versus Phylactolaemata), and for Oligostraca (ostracods + branchiurans + pentastomids + mystacocarids). Future improvement could come from better sequence-evolution models that account for base-compositional heterogeneity, and from combining rRNA with protein-coding genes in phylogenetic reconstruction.
Authors
- Mallatt, Jon M. ;
- Craig, Catherine Waggoner ;
- Yoder, Matthew J.