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Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER
BACKGROUND: Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. T...
| Autores principales: | , |
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| Formato: | Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2005
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1097716/ https://www.ncbi.nlm.nih.gov/pubmed/15831105 http://dx.doi.org/10.1186/1471-2105-6-99 |