TY - GEN
T1 - Identification of Transcriptional promoter sequence based on statistical filter bank model
AU - Huang, Lun
AU - Al Bataineh, Mohammad
AU - Acedo, Alicia Fuente
AU - Atkin, G. E.
AU - Deng, Xiangyu
AU - Zhang, Wei
PY - 2010
Y1 - 2010
N2 - This paper describes a new approach for locating transcription related signals, such as promoter sequence in nucleic acid sequences. Transcription Factor (TF) and corresponding polymerase binding to their DNA target site is a fundamental regulatory interaction. The most common model used to represent TF and polymerase binding specificities is a position weight matrix (PWM) [1], which assumes independence between binding positions. However, in many cases, this simplifying assumption does not hold. In this paper, we present a statistical filter model based on Chi-Square (Χ2) distance [2], which is a statistical distance metric between the profiles of component vectors. It is a novel statistical method for modeling TF-DNA and polymerase-DNA interactions. Our approach also uses a generalized correlation algorithm to evaluate the combination coefficients for the filter bank. Simulation results show that the proposed approach identifies promoter sequences better than the PWM model method and Chi-Square (Χ2) distance model.
AB - This paper describes a new approach for locating transcription related signals, such as promoter sequence in nucleic acid sequences. Transcription Factor (TF) and corresponding polymerase binding to their DNA target site is a fundamental regulatory interaction. The most common model used to represent TF and polymerase binding specificities is a position weight matrix (PWM) [1], which assumes independence between binding positions. However, in many cases, this simplifying assumption does not hold. In this paper, we present a statistical filter model based on Chi-Square (Χ2) distance [2], which is a statistical distance metric between the profiles of component vectors. It is a novel statistical method for modeling TF-DNA and polymerase-DNA interactions. Our approach also uses a generalized correlation algorithm to evaluate the combination coefficients for the filter bank. Simulation results show that the proposed approach identifies promoter sequences better than the PWM model method and Chi-Square (Χ2) distance model.
KW - Chi-square distance
KW - Transcription factor binding sites
KW - Transcriptional promoter sequence
UR - http://www.scopus.com/inward/record.url?scp=78649295025&partnerID=8YFLogxK
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U2 - 10.1109/EIT.2010.5612085
DO - 10.1109/EIT.2010.5612085
M3 - Conference contribution
AN - SCOPUS:78649295025
SN - 9781424468751
T3 - 2010 IEEE International Conference on Electro/Information Technology, EIT2010
BT - 2010 IEEE International Conference on Electro/Information Technology, EIT2010
T2 - 2010 IEEE International Conference on Electro/Information Technology, EIT2010
Y2 - 20 May 2010 through 22 May 2010
ER -