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Exploratory visual analysis of conserved domains on multiple sequence alignments

BACKGROUND: Multiple alignment of protein sequences can provide insight into sequence conservation across many species and thus allow identification of those sections of the sequence most critical to protein function. This insight can be augmented by joint display of conserved domains along the sequ...

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Detalles Bibliográficos
Autores principales: Jankun-Kelly, TJ, Lindeman, Andrew D, Bridges, Susan M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3226196/
https://www.ncbi.nlm.nih.gov/pubmed/19811691
http://dx.doi.org/10.1186/1471-2105-10-S11-S7
Descripción
Sumario:BACKGROUND: Multiple alignment of protein sequences can provide insight into sequence conservation across many species and thus allow identification of those sections of the sequence most critical to protein function. This insight can be augmented by joint display of conserved domains along the sequences. By fusing this metadata visually, biologists can analyze sequence conservation and functional motifs simultaneously and efficiently. RESULTS: We present MSAVis, a new approach combining luminance and hue for simultaneous visualization of conserved motifs and sequence alignment. Input for the algorithm is a multiple sequence alignment in a standard format. The NCBI Conserved Domain Database (CDD) is used for finding conserved domains along the alignment. The visualization quickly identifies conserved domains, and allows both macro (sequence-long) and micro (small amino-acid neighborhood) views. CONCLUSION: MSAVis utilizes two visual cues, luminance and hue, to facilitate at-a-glance summary of the conservation of a user-provided protein alignment while enabling multiple comparisons among functional domains. These visual cues are preattentive and separable so that the relationship between conservation strength and domain membership can be understood. The MSAVis software, written in Python and using BioPython and OpenGL, can be found at http://agbase.msstate.edu/tools/MSAVis.html.