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NETAL / Network Alignment
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An algorithm for the global alignment of protein-protein interaction networks. NETAL uses a greedy method, based on the alignment scoring matrix, which is derived from both biological and topological information of input networks to find the best global network alignment. It outperforms other global alignment methods in terms of several measurements, such as Edge Correctness, Largest Common Connected Subgraphs and the number of common Gene Ontology terms between aligned proteins. As the running time of NETAL is much less than other available methods, NETAL can be easily expanded to multiple alignment algorithm. Furthermore, it overpowers all other existing algorithms in term of performance so that the short running time of NETAL allowed us to implement it as the first server for global alignment of protein-protein interaction networks.
SMAL / Scaffold-Based Multiple Network Aligner
A public, open-source, web-based application for determining multiple network alignments (MNAs) from existing pairwise network alignments (PNAs) that addresses all the aforementioned challenges. With SMAL, PNAs can be combined rapidly to obtain an MNA. The software also supports visualization and user-data interactions to facilitate exploratory analysis and sensemaking. SMAL is especially useful when multiple alignments relative to a particular protein-protein interaction network (PPIN) are required; furthermore, SMAL alignments are persistent in that existing correspondences between networks (obtained during PNA or MNA) are not lost as new networks are added.
GraphCrunch
Finds well-fitting network models by comparing large real-world networks against random graph models according to various network structural similarity measures. GraphCrunch 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. GraphCrunch 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.
MAGNA / Maximizing Accuracy in Global Network Alignment
A program to directly "optimize" edge conservation while the alignment is constructed, without decreasing the quality of node mapping. MAGNA uses a genetic algorithm and a novel function for 'crossover' of two 'parent' alignments into a superior 'child' alignment to simulate a 'population' of alignments that 'evolves' over time; the 'fittest' alignments survive and proceed to the next 'generation', until the alignment accuracy cannot be optimized further.
HubAlign
A global network alignment algorithm that makes use of both network topology and sequence homology information, based upon the observation that topologically important proteins in a PPI network usually are much more conserved and thus, more likely to be aligned. HubAlign uses a minimum-degree heuristic algorithm to estimate the topological and functional importance of a protein from the global network topology information. Then HubAlign aligns topologically important proteins first and gradually extends the alignment to the whole network.
L-GRAAL / Lagrangian GRAphlet-based network Aligner
A global network alignment tool, which combines an efficient solver based on Lagrangian relaxation with a scoring function based on the statistics of small induced subgraphs called graphlets. Unlike previous aligners, which either do not take into account the mapped interactions (e.g., the previous GRAAL aligners, ISORANK, etc), or use naive interaction mapping scoring schemes (e.g., NATALIE), L-GRAAL optimizes a novel objective function that takes into account both sequence-based protein conservation and graphlet-based interaction conservation, by using a novel alignment search heuristic based on integer programming and Lagrangian relaxation.
MAGNA++
It is an extension of MAGNA. MAGNA++ introduces several novelties: 1) It simultaneously maximizes any one of three different measures of edge conservation (including our recent superior S3 measure) and any desired node conservation measure, which further improves alignment quality compared to maximizing only node conservation or only edge conservation. 2) It speeds up the original MAGNA algorithm by parallelizing it to automatically use all available resources, as well as by reimplementing the edge conservation measures more efficiently. 3) It provides a friendly graphical user interface for easy use by domain (e.g., biological) scientists. 4) At the same time, MAGNA++ offers source code for easy extensibility by computational scientists.
ModuleAlign
A method for pairwise global alignment of protein–protein interaction (PPI) networks. Its novel scoring scheme integrates sequence information and both local and global network topology. Based on a hierarchical clustering of the input networks, we compute a homology score between proteins. We propose an iterative approach to find an alignment that scores high in our model while trying to preserve interactions. In our experiments on a diverse set of benchmarks, ModuleAlign outperforms state-of-the-art methods such as GHOST, MAGNA ++, NETAL, HubAlign and L-GRAAL in terms of both alignment accuracy and functional consistency.
CytoGEDEVO / Graph Edit Distance + EVOlution
A Cytoscape app for visual and user-assisted network alignment. CytoGEDEVO extends the previous GEDEVO methodology for global pairwise network alignments with new graphical and functional features. It aligns pairs of networks, where the result is a one-to-one node mapping. Alignments are fully resumable, i.e. parameters can be adjusted anytime and the alignment resumed with the new parameters. Expert knowledge can be incorporated by the user by fixating node pairs. CytoGEDEVO is not limited to network topology and can make use of an unlimited number of externally supplied scores, without imposing restrictions on the score type (that means you can just throw BLAST E-values or Bit-scores or whatever at it and it will work). It also allows importing result files generated by other aligners.
SANA / Simulated Annealing Network Aligner
Explores the space of alignments looking for ones scoring well according to M (an objective function for alignment quality). SANA is based on a metaheuristic search algorithm with a rich history of successful applications to many optimization problems across a wide variety of domains. It was applied to protein-protein interaction networks using Symmetric Substructure Score (S3) as the topological measure. The tool significantly outperforms all other aligners in S3 score, for every pair of networks tested.
Fuse
A global multiple network aligner. First, Fuse computes novel similarity scores between proteins by fusing sequence similarities and network wiring patterns over all proteins in all PPI networks being aligned, using non-negative matrix tri-factorization (NMTF). Second, it construct a one-to-one global multiple network alignment by using an approximate maximum weight k-partite matching solver. We compare the alignments of Fuse to the ones of the state-of-the art aligners, Beams, Smetana, CSRW and NH. We find that even when using solely protein sequence similarity, Fuse already outperforms all other network aligners by producing a larger number of functionally homogeneous clusters that cover all aligned networks.
MNAligner / Molecular Network Aligner
Aligns general molecular networks based on both the node similarity and the network architecture similarity. MNAligner is an algorithm that uses integer quadratic programming (IQP) with a log-probability like criterion. In addition to simple topological substructures such as chains and trees, it can reveal biologically meaningful units or subnetworks with loops or network motifs. It can also be applied to weighted or unweighted networks and to directed or undirected networks.
SUMONA / Supervised Method for Optimizing Network Alignment
Provides an improvement over the OptNetAlign methodology. SUMONA main contribution is increasing the performance of achieving multiple alignment objectives by supervising the optimization process and prioritizing some objectives above others. SUMONA approach uses yet another generated alignment as input of OptNetAlign at each iteration. The performance of SUMONA depends on many factors such as alignment objectives, network characteristics of the aligned species and quality of input data that is generated by other prominent aligners.
multiMAGNA++
Maximizes an alignment quality measure by evolving a population of alignments over time. multiMAGNA++ can be used for biological network alignment, i.e., to align molecular networks of different species and consequently allows for the transfer of biological knowledge from well-studied to poorly-studied species between conserved (aligned) network regions. multiMAGNA++ is a multiple network alignment (MNA) extension of a state-of-the-art PNA MAGNA++ that can directly optimize both node and edge conservation. multiMAGNA++ scales well to larger network sizes and a larger number of networks and can be parallelized effectively.
CytoMCS
Computes the maximum common edge subgraph problem for two or more graphs. CytoMCS is a heuristic maximum common edge subgraph detection tool for the Cytoscape network analysis and visualization platform. The software uses an iterative local search algorithm to search for an alignment that maximizes the number of edges conserved between all graphs. The input and output of the app is provided through Cytoscapes standard data types, and edges are annotated with conservation scores for visualization and analysis.
SIGNALIGN
Aligns pathways using integrated database information from SCOP, CATH, EC number and UniProt. SIGNALIGN is a web-based tool which provides a search engine. This search engine mines the related inbuilt information of the proteins and their classification schemes both in CATH and SCOP along with the relevant UniProt and Protein Data Bank (PDB) information. The software allows structure based alignment of proteins and prediction of linear biochemical pathways and thus the understanding of their evolutionary relationship.
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