Gapped Alignment based Network Inference


Inferring gene regulatory networks is very crucial in decoding various complex mechanisms in the biological system. Building a fully functional transcriptional factor/protein from DNA involves various reactions, leading to a delay in gene regulation. The complexity increases with the dynamic delay induced by other small molecules involved in gene regulation, and noisy cellular environment. The dynamic delay in gene regulation is quite evident in high temporal live cell lineage imaging data. Although a number of gene network inference methods are proposed, most of them ignore the associated dynamic time delay. Here, we present DDGni, a novel gene network inference algorithm based on the gapped local alignment of gene expression profiles. The local alignment can detect short term gene regulations, that are usually overlooked by traditional correlation and Mutual Information based methods. DDGni uses ‘gaps?to handle the dynamic delay and non-uniform sampling frequency in high temporal data, like live cell imaging data. Our algorithm is evaluated on both, synthetic and C. elegans live cell imaging data, against other prominent methods. The area under the curve (AUC) of our method is significantly higher when compared to other methods, on both simulated, Yeast time course and real time C. elegans data. Besides the well-established regulatory relationships, we also predicted few new regulatory relationships that are worthy of subsequent experimental investigation.

Supporting Files and Codes

Supporting files and codes can be downloaded here


This work was supported by the Research Grants Council (781511M) of Hong Kong and NSFC (91229105) of China.


*Correspondence should be addressed to Junwen Wang
(Tel: +852 2819 2809; Fax: +852 2855 1254)
(Email: ).
(Office: L3-80, Laboratory Block, 21 Sassoon Road, Hong Kong).