Abstract
New computational methodologies are increasingly being demanded in Bioinformatics due to the amount of data provided by high-throughput experiments. One of these approaches is multiple sequence alignment since feature integration is necessary to obtain more accurate and faster alignments. Alignments of nucleotide and protein sequences can help us to understand tasks like biological functions or structures in these molecules. Recent applications tend to use more available data that represent similarity among sequences: homologies, structures, functions, domains, motifs, etc. Thus, we present a review of current methods in multiple sequence alignments and their improvements integrating accurately and efficiently these heterogeneous data.
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Guzman, F.M.O., Rojas, I., Pomares, H., Urquiza, J.M., Florido, J.P. (2011). Emerging Methodologies in Multiple Sequence Alignment Using High Throughput Data. In: Rocha, M.P., Rodríguez, J.M.C., Fdez-Riverola, F., Valencia, A. (eds) 5th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2011). Advances in Intelligent and Soft Computing, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19914-1_25
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DOI: https://doi.org/10.1007/978-3-642-19914-1_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19913-4
Online ISBN: 978-3-642-19914-1
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