Visual methods illustrate how DNA sequences are read along a single DNA strand from the 5' end to the 3' end and they provide the hopes of gaining an understanding of the underlying genomic language. By handling genomic sequence residues as elements of a discrete-time signal, digital signal processing techniques can be employed for the analysis of genomic information. Using these representations and applying frequency domain transformations, it is shown that structures, or seemingly nonrandom behavior, may be readily identified in nucleotide sequences. We review the basic method of DNA walks and we show how these representations can be used to extract useful knowledge from the genomic data; namely long-range correlation information, sequence periodicities, and other sequence characteristics. Further information is elucidated through wavelet transform analysis. This work finally relates a measure of sequence complexity to these visual findings and offers conclusions regarding quantifying DNA sequence behavior or structure. (C) 2003 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Berger, J.a., Mitra, S.k., Carli, M., Neri, A. (2004). Visualization and analysis of DNA sequences using DNA walks. JOURNAL OF THE FRANKLIN INSTITUTE, 341(1-2), 37-53 [10.1016/j.jfranklin.2003.12.002].
Visualization and analysis of DNA sequences using DNA walks
CARLI, Marco;NERI, Alessandro
2004-01-01
Abstract
Visual methods illustrate how DNA sequences are read along a single DNA strand from the 5' end to the 3' end and they provide the hopes of gaining an understanding of the underlying genomic language. By handling genomic sequence residues as elements of a discrete-time signal, digital signal processing techniques can be employed for the analysis of genomic information. Using these representations and applying frequency domain transformations, it is shown that structures, or seemingly nonrandom behavior, may be readily identified in nucleotide sequences. We review the basic method of DNA walks and we show how these representations can be used to extract useful knowledge from the genomic data; namely long-range correlation information, sequence periodicities, and other sequence characteristics. Further information is elucidated through wavelet transform analysis. This work finally relates a measure of sequence complexity to these visual findings and offers conclusions regarding quantifying DNA sequence behavior or structure. (C) 2003 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
