Fair Evaluation of Global Network Aligners

Biological network alignment identifies topologically and functionally conserved regions between networks of different species. It encompasses two algorithmic steps: node cost function (NCF), which measures similarities between nodes in different networks, and alignment strategy (AS), which uses these similarities to rapidly identify high-scoring alignments. Different methods use both different NCFs and different ASs. Thus, it is unclear whether the superiority of a method comes from its NCF, its AS, or both. We already showed on MI-GRAAL and IsoRankN that combining NCF of one method and AS of another method can lead to a new superior method. Here, we evaluate MI-GRAAL against newer GHOST to potentially further improve alignment quality. Also, we approach several important questions that have not been asked systematically thus far. First, we ask how much of the node similarity information in NCF should come from sequence data compared to topology data. Existing methods determine this more-less arbitrarily, which could affect the resulting alignment(s). Second, when topology is used in NCF, we ask how large the size of the neighborhoods of the compared nodes should be. Existing methods assume that larger neighborhood sizes are better. We find that MI-GRAAL's NCF is superior to GHOST's NCF, while the performance of the methods' ASs is data-dependent. Thus, the combination of MI-GRAAL's NCF and GHOST's AS could be a new superior method for certain data. Also, which amount of sequence information is used within NCF does not affect alignment quality, while the inclusion of topological information is crucial. Finally, larger neighborhood sizes are preferred, but often, it is the second largest size that is superior, and using this size would decrease computational complexity. Together, our results give several general recommendations for a fair evaluation of network alignment methods.Comment: 19 pages. 10 figures. Presented at the 2014 ISMB Conference, July 13-15, Boston, M

GraphCrunch: A tool for large network analyses

<p>Abstract</p> <p>Background</p> <p>The recent explosion in biological and other real-world network data has created the need for improved tools for large network analyses. In addition to well established <it>global </it>network properties, several new mathematical techniques for analyzing <it>local </it>structural properties of large networks have been developed. Small over-represented subgraphs, called network <it>motifs</it>, have been introduced to identify simple building blocks of complex networks. Small induced subgraphs, called <it>graphlets</it>, have been used to develop "network signatures" that summarize network topologies. Based on these network signatures, two new highly sensitive measures of network local structural similarities were designed: the <it>relative graphlet frequency distance </it>(<it>RGF-distance</it>) and the <it>graphlet degree distribution agreement </it>(<it>GDD-agreement</it>).</p> <p>Finding adequate null-models for biological networks is important in many research domains. Network properties are used to assess the fit of network models to the data. Various network models have been proposed. To date, there does not exist a software tool that measures the above mentioned local network properties. Moreover, none of the existing tools compare real-world networks against a series of network models with respect to these local as well as a multitude of global network properties.</p> <p>Results</p> <p>Thus, we introduce GraphCrunch, a software tool that finds well-fitting network models by comparing large real-world networks against random graph models according to various network structural similarity measures. It has unique capabilities of finding computationally expensive RGF-distance and GDD-agreement measures. In addition, it computes several standard global network measures and thus supports the largest variety of network measures thus far. Also, it is the first software tool that compares real-world networks against a series of network models and that has built-in parallel computing capabilities allowing for a user specified list of machines on which to perform compute intensive searches for local network properties. Furthermore, GraphCrunch is easily extendible to include additional network measures and models.</p> <p>Conclusion</p> <p>GraphCrunch is a software tool that implements the latest research on biological network models and properties: it compares real-world networks against a series of random graph models with respect to a multitude of local and global network properties. We present GraphCrunch as a comprehensive, parallelizable, and easily extendible software tool for analyzing and modeling large biological networks. The software is open-source and freely available at <url>http://www.ics.uci.edu/~bio-nets/graphcrunch/</url>. It runs under Linux, MacOS, and Windows Cygwin. In addition, it has an easy to use on-line web user interface that is available from the above web page.</p

Izazovi i mogućnosti transformacije grada u "Zero Waste City"

Fenomen nultog otpada koji se razvija obuhvata teoriju, praksu i učenje pojedinaca, porodica, preduzeća, zajednica i vladinih organizacija, koji odgovaraju na percepcije krize i neuspeha oko konvencionalnog upravljanja otpadom. Trenutno, naše društvo vođeno potrošnjom doprinosi proizvodnji velike količine otpada svakog dana u urbanim sredinama. Niske stope reciklaže vrše pritisak na gradske vlasti da se sa otpadom ponašaju na održiviji način. Uprkos ovom pritisku, sistemi upravljanja otpadom su posvetili malo pažnje procesima urbanog planiranja. Evidentno je da značajan broj globalno neobnovljivih resursa kao što su kadmijum, živa i telur će doživeti trajni nedostatak globalne ponude u naredne dve do tri decenije. Trenutna stopa recikliranja ovih vrlo oskudnih metala je značajno niska u svim gra- dovima širom sveta. Koncept grada bez otpada uključuje 100 procentnu reciklažu čvrstog komunalnog otpada i 100 procentni oporavak svih resurse iz otpadnih materijala. Shodno tome, postoje uočljive značajne praznine u pogledu upravljanja otpadom, pa je neophodno strateško upravljanje otpadom i preterano održiv model potrošnje, posebno u zemljama u razvoju koje su prilično ranjive na klimatske promene. Međutim, transformacija gradova koji trenutno previše troše u gradove bez otpada je izazov. Istraživanje ima za cilj da razume ključne faktore sistema upravljanja otpadom u gradovima kao što su potrošnja, iscrpljivanje resursa I moguća prilika za razdvajanje kroz implementaciju koncepta „ Zero waste city “. Istraživanje doprinosi tumačenju, kako se kroz koncept i implementaciju cilja nultog otpada može povećati angažovanje zajednice koja je katalizator dizajna pri upravljanju cirkularnijim urbanim metabolizmom

Dominating Biological Networks

Proteins are essential macromolecules of life that carry out most cellular processes. Since proteins aggregate to perform function, and since protein-protein interaction (PPI) networks model these aggregations, one would expect to uncover new biology from PPI network topology. Hence, using PPI networks to predict protein function and role of protein pathways in disease has received attention. A debate remains open about whether network properties of “biologically central (BC)” genes (i.e., their protein products), such as those involved in aging, cancer, infectious diseases, or signaling and drug-targeted pathways, exhibit some topological centrality compared to the rest of the proteins in the human PPI network

Optimal Network Alignment with Graphlet Degree Vectors

Important biological information is encoded in the topology of biological networks. Comparative analyses of biological networks are proving to be valuable, as they can lead to transfer of knowledge between species and give deeper insights into biological function, disease, and evolution. We introduce a new method that uses the Hungarian algorithm to produce optimal global alignment between two networks using any cost function. We design a cost function based solely on network topology and use it in our network alignment. Our method can be applied to any two networks, not just biological ones, since it is based only on network topology. We use our new method to align protein-protein interaction networks of two eukaryotic species and demonstrate that our alignment exposes large and topologically complex regions of network similarity. At the same time, our alignment is biologically valid, since many of the aligned protein pairs perform the same biological function. From the alignment, we predict function of yet unannotated proteins, many of which we validate in the literature. Also, we apply our method to find topological similarities between metabolic networks of different species and build phylogenetic trees based on our network alignment score. The phylogenetic trees obtained in this way bear a striking resemblance to the ones obtained by sequence alignments. Our method detects topologically similar regions in large networks that are statistically significant. It does this independent of protein sequence or any other information external to network topology

Optimized Null Model for Protein Structure Networks

Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by comparing various RIG definitions against a series of network models