DDGni: Dynamic delay gene-network inference from high-temporal data using gapped local alignment

Hari Krishna Yalamanchili, Bin YAN, Mulin Jun Li, Jing Qin, Zhongying Zhao, Francis Y.L. Chin, Junwen Wang*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

23 Citations (Scopus)


Motivation: Inferring gene-regulatory networks is very crucial in decoding various complex mechanisms in biological systems. Synthesis of a fully functional transcriptional factor/protein from DNA involves series of 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.Results: Here, we propose DDGni (dynamic delay gene-network inference), 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 synthetic and yeast cell cycle data, and Caenorhabditis elegans live cell imaging data against other prominent methods. The area under the curve of our method is significantly higher when compared to other methods on all three datasets.Availability: The program, datasets and supplementary files are available at http://www.jjwanglab.org/DDGni/.Contact: Supplementary Information: Supplementary data are available at Bioinformatics online.

Original languageEnglish
Pages (from-to)377-383
Number of pages7
Issue number3
Early online date27 Nov 2013
Publication statusPublished - 1 Feb 2014

Scopus Subject Areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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