Global network alignment in the context of aging

Abstract

Analogous to sequence alignment, network alignment (NA) can be used to transfer biological knowledge across species between conserved network regions. NA faces two algorithmic challenges: 1) Which cost function to use to capture "similarities" between nodes in different networks? 2)Which alignment strategy to use to rapidly identify "high-scoring" alignments from all possible alignments? We "break down" existing state-of-the-art methods that use both different cost functions and different alignment strategies to evaluate each combination of their cost functions and alignment strategies. We find that a combination of the cost function of one method and the alignment strategy of another method beats the existing methods. Hence, we propose this combination as a novel superior NA method. Then, since human aging is hard to study experimentally due to long lifespan, we use NA to transfer aging-related knowledge from well annotated model species to poorly annotated human. By doing so, we produce novel human aging-related knowledge, which complements currently available knowledge about aging that has been obtained mainly by sequence alignment. We demonstrate significant similarity between topological and functional properties of our novel predictions and those of known aging-related genes.We are the first to use NA to learn more about aging.This work was published as a full paper in Proceedings of ACM BCB 2013 [2] and an extended journal version was published in IEEE/ACM Transactions on Computational Biology and Bioinformatics [1].

Fair evaluation of global network aligners

“…Also, we note that although we already showed on the example of MI-GRAAL and IsoRankN that combining NCF of one method and AS of another method can lead to a new superior method [2,3], testing whether the same holds for MI-GRAAL and GHOST, and in particular identifying the superior of the two NCFs, is of importance. First, validating that this also holds for MI-GRAAL and GHOST would only further stress out the need to carefully design a strategy for evaluating a novel approach against existing ones.…”

“…Unlike LNA, GNA typically results in one-to-one node mapping between different networks (though some exceptions exist that result in one-to-many or many-to-many node mapping [24,32]). In this study, we focus on one-to-one GNA due to its recent popularity [2,3,31], but all concepts and ideas can also be applied to one-to-many or many-to many GNA, as well as to LNA.…”

“…For this reason, in our recent study [2,3], we combined NCFs and ASs of MI-GRAAL [7] and IsoRankN [24], two stateof-the-art methods at the time, as a proof of concept that it is important to fairly evaluate the contribution of each component to alignment quality. In the process, we showed that NCF of MI-GRAAL is superior to that of IsoRankN, and importantly, we proposed the combination of MI-GRAAL's NCF and IsoRankN's AS as a new superior method for multiple GNA, i.e., for GNA of more than two networks at a time [2,3].…”

“…For these reasons, we fairly evaluate MI-GRAAL against GHOST by mixing and matching their NCFs and ASs. We use MI-GRAAL in this study because we already demonstrated the superiority of its NCF, as discussed above [2,3]. At the same time, we ask several additional important questions regarding the choice of appropriate GNA parameters, which have surprisingly been neglected thus far.…”

“…A more advanced evaluation strategy, such as our mix-and-match approach, is more appropriate. Second, MI-GRAAL's NCF is a graphlet-based measure of topological node similarity [37] that is also used by many other network aligners [2,3,5,6,38] or even network clustering methods [37,39,40] to link network topology with biological function. When a new measure of topological similarity appears that is also argued to successfully capture biological function, such as GHOST's NCF, it is extremely important to fairly compare it against the graphlet-based node similarity measure (which has not been done to date).…”