Local network alignment software tools | Protein interaction data analysis
Network alignment (NA) aims to find regions of similarities between species’ molecular networks. There exist two NA categories: local (LNA) and global (GNA). LNA finds small highly conserved network regions and produces a many-to-many node mapping. Source text: Meng at al., 2016.
A fast and scalable local network alignment tool for the identification of functionally conserved modules in multiple networks. To evaluate the performance and the statistical significance, LocalAli were tested on 26 real datasets and 1040 randomly generated datasets. The results suggest that LocalAli outperforms all existing algorithms in terms of coverage, consistency and scalability, meanwhile retains a high precision in the identification of functionally coherent subnetworks.
Allows simultaneous visualization and comparison of multiple networks. In addition to computing generic graph properties for individual networks, CompNet allows multigraph comparisons and similarity based grouping of networks. It also allows visual identification and selection of sub-graphs/communities of interest, enabling a general user to work with and compare between sufficiently complex and large networks. CompNet is a valuable tool for biologists and other researchers working in the field of visual data mining.
A web system for managing, comparing and mining multiple networks, both directed and undirected. tYNA provides five main types of functionality: (1) network management: storing, retrieving and categorizing networks. A comprehensive set of widely used network datasets is preloaded, put into standard form, and categorized with a set of tags; (2) network visualization: displaying networks in an interactive graphical interface; (3) network comparison and manipulation: various kinds of filtering and multiple network operations; (4) network analysis: computing various statistics for the whole network and subsets, and finding motifs and defective cliques; (5) network mining: predicting one network based on the information in another.
Provides several tools for bioinformatics analysis. Corbi is an R package that contains functions for network querying and alignment, subnetwork extraction, search and biomarker identification. The software includes the network querying method named CNetQ, CNetA, which extended CNetQ to the pairwise network alignment problem with one-to-one mapping and MarkRank, an enhanced biomarker ranking method.
A network alignment algorithm that performs a dual alignment strategy, in which both region-to-region alignment and protein-to-protein alignment are performed to achieve superior-quality network alignment. Dual network alignment is achieved in DualAligner via background information provided by a combination of Gene Ontology annotation information and protein interaction network data.
Allows identifying protein complexes in protein-protein interaction (PPI) networks. NetworkBlast-M analyzes networks from multiple species and outputs a set of complexes that are evolutionarily conserved across the networks. NetworkBLAST-M is available for download. It is an implementation of the web-server platform NetworkBLAST for multiple analysis.
The identification of protein complexes is a fundamental challenge in interpreting protein-protein interaction data. Cross-species analysis allows coping with the high levels of noise that are typical to these data. The NetworkBLAST web-server provides a platform for identifying protein complexes in protein-protein interaction networks. It can analyze a single network or two networks from different species. In the latter case, NetworkBLAST outputs a set of putative complexes that are evolutionarily conserved across the two networks.
A protein-protein interaction networks (PPINs) alignment method which combines information from protein sequence, function and network topology. Alignment of human and yeast PPINs reveals several conserved subnetworks between them that participate in similar biological processes, notably the proteasome and transcription related processes. PINALOG has been tested for its power in protein complex prediction as well as function prediction. Comparison with PSI-BLAST in predicting protein function in the twilight zone also shows that PINALOG is valuable in predicting protein function.
A web-based tool designed to enable comparative analysis of protein interaction networks (PINs). NetAlign compares a query PIN with a target PIN by combining interaction topology and sequence similarity to identify conserved network substructures (CoNSs), which may derive from a common ancestor and disclose conserved topological organization of interactions in evolution.
Adds a functionality to PathVisio software, allowing the creation of bioregulatory networks according to MIM notation. PathVisio-MIM is an open source standalone application that integrates a Molecular Interaction Maps (MIM) Glyphs panel into PathVision’ interface that authorizes to use MIM glyphs as well as annotate interactions on diagrams with comments and literature reference information. Besides, it allows users to import and export in MIMML exchange format.
Enables network phylogeny reconstruction. Netdis consists of a topology-based distance measure between networks. The software compares the subgraph content not of the networks themselves but instead of the ensemble of all protein neighborhoods (ego-networks) in each network, through an averaging many-to-many approach. It can separate different random graph model types independent of network size and density.
Provides one-to-many alignments of reactions in a pair of metabolic pathways. When compared with a state-of-the-art alternative, the CAMPways algorithm provides better alignment results on metabolic networks as far as measures based on same-pathway inclusion and biochemical significance are concerned. The execution speed of CAMPways constitutes yet another important improvement over alternative algorithms.
Adds a functionality to PathVisio software allowing to report errors detected on pathway diagrams against the targeted notation that the platform is able to draw. PathVisio-Validator provides a set of standard rule sets for languages such as Groovy and authorizes the use of customized rulesets. Besides, the module had been developed to be easily extended to various notations added to PathVisio.
An algorithm that solves the simultaneous prediction and alignment of networks problem in accordance with its simultaneous nature. Bearing the same name as the defined problem itself, the SiPAN algorithm employs state-of-the-art alignment and topology-based interaction confidence construction algorithms, which are used as benchmark methods for comparison purposes as well.
Evaluates local network alignment (LNA) against global network alignment (GNA). LNA_GNA method provides guidelines for researchers to properly demonstrate the superiority of a newly proposed network alignment (NA) method. LNA aims to find small highly conserved network regions and produces many-to-many mapping between nodes of the compared networks, while GNA aims to find large conserved subgraphs and produces one-to-one node mapping. Given the different outputs of LNA and GNA, when a new NA method is proposed, it is compared against existing methods from the same category.
Allows users to study local similarity analysis (LSA). fastLSA is an algorithm using a novel asymptotic upper bound algorithm for calculating the LSA p-value. It replaces a computationally intensive permutation test to compute significance of LSA statistics with a dramatic increase in the size of datasets that can be analyzed on a single desktop machine. It uses real-world and simulated time series datasets from different fields of inquiry, for visualizing the resulting clusters of local similarity.
An algorithm that, given the networks of two organisms, uncovers subnetworks of proteins that relate in biological function and topology of interactions. The discovered conserved subnetworks have a general topology and need not to correspond to specific interaction patterns, so that they more closely fit the models of functional complexes proposed in the literature. The algorithm is able to handle sparse interaction data with an expansion process that at each step explores the local topology of the networks beyond the proteins directly interacting with the current solution.
Detects molecular systems in genome data. MacSyFinder identifies the presence of a given system according to specifications of the input biological model. It determines the presence of a given component by similarity search with hidden Markov models (HMM) protein profiles using program Hmmer. This tool ignores phylogenetic information when putting together components of systems scattered in a replicon or in unordered datasets.
An algorithm for multiple local network alignment based on statistical iterative sampling in connection to a greedy strategy. GASOLINE can compute and visualize local alignments in a user-friendly way, without requiring postprocessing operations. Alignments can also be analyzed, by annotating proteins with corresponding GO categories, provided as input by the user. GASOLINE overcomes the limits of current approaches by producing biologically significant alignments within a feasible running time, even for very large input instances. The method has been extensively tested on a database of real and synthetic biological networks. A comprehensive comparison with state-of-the art algorithms clearly shows that GASOLINE yields the best results in terms of both reliability of alignments and running time on real biological networks and results comparable in terms of quality of alignments on synthetic networks.
A network synthesis model that can generate families of evolutionarily related synthetic protein-protein interaction (PPI) networks. The proposed model can serve as an effective framework for generating comprehensive benchmark datasets that can be used for reliable performance assessment of comparative network analysis algorithms.
Handles information contained in Molecular Interaction Map (MIM) diagrams. MIM API is an open source software providing an interface compatible with constructs of the MIMML format including the ancillary constructs, such as comments, and generic properties. The platform is able to interact with external APIs for other formats or libraries providing other functionalities. Moreover, it also permits to generate and manage MIMML datasets.
Offers a flexible and efficient graph-matching tool for comparing complex biological pathways. The main features of MIMO are: (i) easy-to-use: MIMO takes as input biological networks encoded with the Systems Biology Markup Language (SBML) standard. The SBML standard is widely adopted for biological network modeling and is flexible enough to allow the encoding of quite complex molecular interactions. Most importantly, the choice to adopt a standard format as input avoids the pre-processing phase needed to convert molecular interaction maps in a non-standard format removing all the consequent burden; (ii) flexibility: MIMO implements a flexible procedure for sub-graph matching, which naturally allows the introduction of gaps and mismatches and permits (if required) supervised queries incorporating a priori biological information; (iii) computational efficiency: while the subgraph matching problem is computationally intractable, MIMO implementation is fast enough to allow multiple queries on graph databases.
A visualization system for aligned biological networks in 3D space that naturally embeds existing 2D layouts. VANLO provides an intuitive global understanding of aligned PPI networks and it allows the investigation of key biological questions.
Compares protein-protein interaction (PPI) networks and predicts the correspondence between proteins belonging to conserved functional modules. CUFID-align is a network alignment algorithm that, given a pair of PPI networks, constructs an integrated network and a Markov random walk model on the resulting network. The software can align proteins with identical functional annotations at a relatively low computational. It provides a means of computationally annotating the functions of proteins through comparative analysis of PPI networks.
Permits alignment of vertices that descend from a common ancestor. GraphAlignment is based on a hybrid method. It uses an explicit model of network evolution to infer alignment parameters from the data. This tool has been applied to gene co-expression networks and small protein-protein interaction (PPIs) networks. It is able to align a large proportion of the analogous vertices. GraphAlignment can be useful for global alignment of networks.
Molecular interactions need to be taken into account to adequately model the complex behavior of biological systems. NatalieQ is a web server for aligning two protein-interaction networks in order to highlight conserved subnetworks. It is an interface to the more general network alignment method Natalie.
A comprehensive alignment tool for PPI networks, which is inspired by duplication/divergence models that focus on understanding the evolution of protein interactions. MaWISh is based on a mathematical model that extends the concepts of match, mismatch, and gap in sequence alignment to that of match, mismatch, and duplication in network alignment and evaluates the similarity between graph structures through a scoring function that accounts for evolutionary events. By relying on evolutionary models, it facilitates interpretation of resulting alignments in terms of not only conservation but also divergence of modularity in PPI networks. Furthermore, as in the case of sequence alignment, MaWISh allows flexibility in adjusting parameters to quantify underlying evolutionary relationships.
It can identify biologically relevant mappings that are missed by traditional alignment methods. SubMAP is scalable for metabolic pathways of arbitrary topology, including searching for a query pathway of size 70 against the complete KEGG database of 1,842 pathways.
Identifies conserved subnetworks in different species using a local alignment algorithm. AlignMCL is based on the idea of first merging two protein interaction networks (PINs) in a single alignment graph, and then mine it to identify potentially conserved subnetworks. AlignMCL is implemented in two components. The first (pyAligner) processes input PINs and orthologies to create the alignment graph. The second (MCL) mines the alignment graph produced by pyAligner to identify conserved modules.
A network alignment method based on the conditional random ﬁeld model. The method is compared with other four methods on three real protein-protein interaction (PPI) network pairs by using four structural and five biological criteria. Compared with structure-dominated methods, larger biological conserved subnetworks are found, while compared with the node-dominated methods, larger connected subnetworks are found. CNetA preferably balances the biological and topological similarities.
Furnishes a method for incorporating proteins from different species into a shared vector space. HANDL is a standalone software based on a diffusion kernel algorithm. It aims to facilitate the detection of functional similarity across species and provides an alternative to standard sequence homology. The application can be used in conjunction with other kernels to link proteins or for evaluating network properties.
Allows identification of common organizational principles in networks. NetEMD is a network comparison methodology that can identify similar networks at multiple scales, including when networks differ significantly in terms of size and density. The software is suitable for settings ranging from the functional classification of proteins to track the evolution of a world trade network.
Allows users to build Molecular Interaction Maps diagrams according to MIM notation. MIMTool provides a graphic interface divided into three mains components: (i) a drawing tool related to a toolbox that displays the symbols used in MIM notation; (ii) interfaces for text-reading files in SBML or MIMML; (iii) and a general toolbar for managing import and export for the processed files.
A network alignment tool that allows the identification of conserved protein complexes and pathways across organisms, providing valuable hints as to how those interaction networks evolved. NetAligner includes the prediction of likely conserved interactions, based on evolutionary distances, to counter the high number of missing interactions in current interactome networks, and a fast assessment of the statistical significance of individual alignment solutions, which increases its performance with respect to existing tools.
A network alignment and search tool for comparing protein interaction networks across species to identify protein pathways and complexes that have been conserved by evolution. The basic method searches for high-scoring alignments between pairs of protein interaction paths, for which proteins of the first path are paired with putative orthologs occurring in the same order in the second path. This technique discriminates between true- and false-positive interactions and allows for functional annotation of protein interaction pathways based on similarity to the network of another, well-characterized species.
A conceptual framework and computational system that allows the retrieval of metabolic pathway information and the processing of alignments to reveal the similarities between metabolic pathways. PathAligner extracts metabolic information from biological databases via the Internet and builds metabolic pathways with data sources of genes, sequences, enzymes, metabolites etc. It provides an easy-to-use interface to retrieve, display and manipulate metabolic information. PathAligner also provides an alignment method to compare the similarity between metabolic pathways.
Dr. Yashwanth Subbannayya obtained his M.Sc. degree in Medical Biochemistry from Manipal University. He qualified the competitive CSIR-UGC National Eligibility Test and joined the Institute of Bioinformatics, Bangalore as a UGC Junior Research Fellow. As part of his Ph.D. work, he studied the molecular mechanisms of gastric cancer in clinical specimens using quantitative proteomic technologies. This study, the results of which were published in Cancer Biology and Therapy, yielded a novel therapeutic target for gastric cancer- CAMKK2. Further, he also studied the serum proteome of gastric cancer patients and developed assays for potential markers using the revolutionary multiple reaction monitoring approach. The results of this study were published in Journal of Proteomics. In addition to his research work, he also trained extensively in sample preparation for mass spectrometry, fractionation techniques and gained expertise in quantitative proteomic techniques and data analysis. In addition, he also trained extensively in various validation platforms including immunohistochemsitry, multiple reaction monitoring and Western blot. He has also worked as a curator for several biological databases including NetPath, Human Protein Reference Database (HPRD) and Breast cancer database. His work in various research projects have yielded him 23 publications either as lead author or co-author in peer reviewed journals. He is a reviewer for the journal Proteomics.
Dr. Yashwanth Subbannayya joined the YU-IOB Center for Systems Biology and Molecular Medicine in June, 2015. During the initial period, his job consisted of assisting other personnel of the university in the establishment of YU-IOB Center for Systems Biology and Molecular Medicine. He was also involved in training of Ph.D. students in biological aspects. After the establishment of the center, he trained in cell culture techniques and metabolomics analysis. At YU-IOB CSBMM, he is studying the molecular mechanisms in various cancers including oral cancer. In addition, he is studying the molecular mechanisms as well as the metabolic constituents of traditional medicine formulations using mass spectrometry technologies. In June 2016, he convened the national symposium “Genomics in clinical practice: Future of precision medicine” held at Yenepoya University on June 1 and 2, 2016. The resource persons included 16 individuals from various academic organizations as well as industry. The symposium was attended by 218 participants from 24 institutions around India. He is a member of the Scientific Review Board of Yenepoya Research Centre where he facilitates timely scientific review of research projects.
akhilesh Head of Genomics and Bioinformatics
3i Molecular Solutions & Healthcare Services