Name
Abstract | ||
|---|---|---|
Background Many important applications in bioinformatics, including sequence alignment and protein family profiling, employ sequence weighting schemes to mitigate the effects of non-independence of homologous sequences and under- or over-representation of certain taxa in a dataset. These schemes aim to assign high weights to sequences that are 'novel' compared to the others in the same dataset, and low weights to sequences that are over-represented. Results We formalise this principle by rigorously defining the evolutionary 'novelty' of a sequence within an alignment. This results in new sequence weights that we call 'phylogenetic novelty scores'. These scores have various desirable properties, and we showcase their use by considering, as an example application, the inference of character frequencies at an alignment column-important, for example, in protein family profiling. We give computationally efficient algorithms for calculating our scores and, using simulations, show that they are versatile and can improve the accuracy of character frequency estimation compared to existing sequence weighting schemes. Conclusions Our phylogenetic novelty scores can be useful when an evolutionarily meaningful system for adjusting for uneven taxon sampling is desired. They have numerous possible applications, including estimation of evolutionary conservation scores and sequence logos, identification of targets in conservation biology, and improving and measuring sequence alignment accuracy. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1186/s12859-021-04183-8 | BMC BIOINFORMATICS |
Keywords | DocType | Volume |
Phylogenetics, Sequence weights, Alignment, Protein profile, Conservation scores | Journal | 22 |
Issue | ISSN | Citations |
1 | 1471-2105 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicola De Maio | 1 | 2 | 1.76 |
| Alexander V. Alekseyenko | 2 | 37 | 9.10 |
| William J Coleman-Smith | 3 | 0 | 0.34 |
| Fabio Pardi | 4 | 17 | 5.58 |
| Marc A Suchard | 5 | 213 | 27.83 |
| Asif U Tamuri | 6 | 0 | 0.34 |
| Jakub Truszkowski | 7 | 0 | 0.34 |
| Nick Goldman | 8 | 0 | 0.68 |