Aligning dynamic networks with DynaWAVE

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Abstract

Motivation:

Network alignment (NA) aims to find similar (conserved) regions between networks, such as cellular networks of different species. Until recently, existing methods were limited to aligning static networks. However, real-world systems, including cellular functioning, are dynamic. Hence, in our previous work, we introduced the first ever dynamic NA method, DynaMAGNA++, which improved upon the traditional static NA. However, DynaMAGNA++ does not necessarily scale well to larger networks in terms of alignment quality or runtime.

Results:

To address this, we introduce a new dynamic NA approach, DynaWAVE. We show that DynaWAVE complements DynaMAGNA++: while DynaMAGNA++ is more accurate yet slower than DynaWAVE for smaller networks, DynaWAVE is both more accurate and faster than DynaMAGNA++ for larger networks. We provide a friendly user interface and source code for DynaWAVE.

Availability and implementation:

https://www.nd.edu/˜cone/DynaWAVE/.

Contact:

tmilenko@nd.edu

Supplementary information:

Supplementary data are available at Bioinformatics online.

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