New algorithmic contributions for large scale multiple sequence alignments of protein sequences
In these days of significant changes and the rapid evolution of technology, the amount of datascience has to deal with the growth incredibly fast, and the size of data could be prohibitive.Multiple Sequence Alignments (MSA) are used in various areas of biology, and the increase ofdata has produced a...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/673526 |
| Acceso en línea: | http://hdl.handle.net/10803/673526 |
| Access Level: | acceso abierto |
| Palabra clave: | Multiple sequence alignment Regressive alignment Reproducibility Guide-tree Containers Alineaments Reproducibilitat Escalabilitat Alineament regressiu 577 |
| Sumario: | In these days of significant changes and the rapid evolution of technology, the amount of datascience has to deal with the growth incredibly fast, and the size of data could be prohibitive.Multiple Sequence Alignments (MSA) are used in various areas of biology, and the increase ofdata has produced a degradation of the methods. That is why is proposed a new solution toperform the MSA. This novel paradigm allows the alignment of millions of sequences and theability to modularize the process. Regressive enables the parallelization of the process and thecombination of clustering methods (guide-tree) with whatever aligner is desired. On theclustering side, the guide-tree has to be rethought. A study of the current state of the methodsand their strength and weaknesses have been performed to shed some light on the topic. Theguide-tree cannot be the bottleneck, and it should provide a good starting point for the aligners. |
|---|