Đang chuẩn bị liên kết để tải về tài liệu:
Báo cáo sinh học: "Mimosa: Mixture model of co-expression to detect modulators of regulatory interaction"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: Mimosa: Mixture model of co-expression to detect modulators of regulatory interaction. | Hansen et al. Algorithms for Molecular Biology 2010 5 4 http www.almob.Org content 5 1 4 AMR ALGORITHMS FOR MOLECULAR BIOLOGY RESEARCH Open Access Mimosa Mixture model of co-expression to detect modulators of regulatory interaction Matthew Hansen Logan Everett Larry Singh Sridhar Hannenhalli Abstract Background Functionally related genes tend to be correlated in their expression patterns across multiple conditions and or tissue-types. Thus co-expression networks are often used to investigate functional groups of genes. In particular when one of the genes is a transcription factor TF the co-expression-based interaction is interpreted with caution as a direct regulatory interaction. However any particular TF and more importantly any particular regulatory interaction is likely to be active only in a subset of experimental conditions. Moreover the subset of expression samples where the regulatory interaction holds may be marked by presence or absence of a modifier gene such as an enzyme that post-translationally modifies the TF. Such subtlety of regulatory interactions is overlooked when one computes an overall expression correlation. Results Here we present a novel mixture modeling approach where a TF-Gene pair is presumed to be significantly correlated with unknown coefficient in an unknown subset of expression samples. The parameters of the model are estimated using a Maximum Likelihood approach. The estimated mixture of expression samples is then mined to identify genes potentially modulating the TF-Gene interaction. We have validated our approach using synthetic data and on four biological cases in cow yeast and humans. Conclusions While limited in some ways as discussed the work represents a novel approach to mine expression data and detect potential modulators of regulatory interactions. Background Eukaryotic gene regulation is carried out to a significant extent at the level of transcription. Many functionally related genes e.g. members of a pathway involved in