Noël Malod-Dognin

A global genetic interaction network maps a wiring diagram of cellular function

INTRODUCTION Genetic interactions occur when mutations in two or more genes combine to generate an unexpected phenotype. An extreme negative or synthetic lethal genetic interaction occurs when two mutations, neither lethal individually, combine to cause cell death. Conversely, positive genetic interactions occur when two mutations produce a phenotype that is less severe than expected. Genetic interactions identify functional relationships between genes and can be harnessed for biological discovery and therapeutic target identification. They may also explain a considerable component of the undiscovered genetics associated with human diseases. Here, we describe construction and analysis of a comprehensive genetic interaction network for a eukaryotic cell. RATIONALE Genome sequencing projects are providing an unprecedented view of genetic variation. However, our ability to interpret genetic information to predict inherited phenotypes remains limited, in large part due to the extensive buffering of genomes, making most individual eukaryotic genes dispensable for life. To explore the extent to which genetic interactions reveal cellular function and contribute to complex phenotypes, and to discover the general principles of genetic networks, we used automated yeast genetics to construct a global genetic interaction network. RESULTS We tested most of the ~6000 genes in the yeast Saccharomyces cerevisiae for all possible pairwise genetic interactions, identifying nearly 1 million interactions, including ~550,000 negative and ~350,000 positive interactions, spanning ~90% of all yeast genes. Essential genes were network hubs, displaying five times as many interactions as nonessential genes. The set of genetic interactions or the genetic interaction profile for a gene provides a quantitative measure of function, and a global network based on genetic interaction profile similarity revealed a hierarchy of modules reflecting the functional architecture of a cell. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections associated with defects in cell cycle progression or cellular proteostasis. Importantly, the global network illustrates how coherent sets of negative or positive genetic interactions connect protein complex and pathways to map a functional wiring diagram of the cell. CONCLUSION A global genetic interaction network highlights the functional organization of a cell and provides a resource for predicting gene and pathway function. This network emphasizes the prevalence of genetic interactions and their potential to compound phenotypes associated with single mutations. Negative genetic interactions tend to connect functionally related genes and thus may be predicted using alternative functional information. Although less functionally informative, positive interactions may provide insights into general mechanisms of genetic suppression or resiliency. We anticipate that the ordered topology of the global genetic network, in which genetic interactions connect coherently within and between protein complexes and pathways, may be exploited to decipher genotype-to-phenotype relationships. A global network of genetic interaction profile similarities. (Left) Genes with similar genetic interaction profiles are connected in a global network, such that genes exhibiting more similar profiles are located closer to each other, whereas genes with less similar profiles are positioned farther apart. (Right) Spatial analysis of functional enrichment was used to identify and color network regions enriched for similar Gene Ontology bioprocess terms. We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell.

Revealing the Hidden Language of Complex Networks

Sophisticated methods for analysing complex networks promise to be of great benefit to almost all scientific disciplines, yet they elude us. In this work, we make fundamental methodological advances to rectify this. We discover that the interaction between a small number of roles, played by nodes in a network, can characterize a networks structure and also provide a clear real-world interpretation. Given this insight, we develop a framework for analysing and comparing networks, which outperforms all existing ones. We demonstrate its strength by uncovering novel relationships between seemingly unrelated networks, such as Facebook, metabolic, and protein structure networks. We also use it to track the dynamics of the world trade network, showing that a countrys role of a broker between non-trading countries indicates economic prosperity, whereas peripheral roles are associated with poverty. This result, though intuitive, has escaped all existing frameworks. Finally, our approach translates network topology into everyday language, bringing network analysis closer to domain scientists.

Integrative methods for analyzing big data in precision medicine

We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of “Big Data” in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration‐based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever‐growing nature of these big data, we highlight key issues that big data integration methods will face.

L-GRAAL: Lagrangian graphlet-based network aligner

Motivation: Discovering and understanding patterns in networks of protein–protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. A few methods have been proposed for global PPI network alignments, but because of NP-completeness of underlying sub-graph isomorphism problem, producing topologically and biologically accurate alignments remains a challenge. Results: We introduce a novel global network alignment tool, Lagrangian GRAphlet-based ALigner (L-GRAAL), which directly optimizes both the protein and the interaction functional conservations, using a novel alignment search heuristic based on integer programming and Lagrangian relaxation. We compare L-GRAAL with the state-of-the-art network aligners on the largest available PPI networks from BioGRID and observe that L-GRAAL uncovers the largest common sub-graphs between the networks, as measured by edge-correctness and symmetric sub-structures scores, which allow transferring more functional information across networks. We assess the biological quality of the protein mappings using the semantic similarity of their Gene Ontology annotations and observe that L-GRAAL best uncovers functionally conserved proteins. Furthermore, we introduce for the first time a measure of the semantic similarity of the mapped interactions and show that L-GRAAL also uncovers best functionally conserved interactions. In addition, we illustrate on the PPI networks of bakers yeast and human the ability of L-GRAAL to predict new PPIs. Finally, L-GRAALs results are the first to show that topological information is more important than sequence information for uncovering functionally conserved interactions. Availability and implementation: L-GRAAL is coded in C++. Software is available at: http://bio-nets.doc.ic.ac.uk/L-GRAAL/. Contact: n.malod-dognin@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

GR-Align: fast and flexible alignment of protein 3D structures using graphlet degree similarity

MOTIVATION Protein structure alignment is key for transferring information from well-studied proteins to less studied ones. Structural alignment identifies the most precise mapping of equivalent residues, as structures are more conserved during evolution than sequences. Among the methods for aligning protein structures, maximum Contact Map Overlap (CMO) has received sustained attention during the past decade. Yet, known algorithms exhibit modest performance and are not applicable for large-scale comparison. RESULTS Graphlets are small induced subgraphs that are used to design sensitive topological similarity measures between nodes and networks. By generalizing graphlets to ordered graphs, we introduce GR-Align, a CMO heuristic that is suited for database searches. On the Proteus_300 set (44 850 protein domain pairs), GR-Align is several orders of magnitude faster than the state-of-the-art CMO solvers Apurva, MSVNS and AlEigen7, and its similarity score is in better agreement with the structural classification of proteins. On a large-scale experiment on the Gold-standard benchmark dataset (3 207 270 protein domain pairs), GR-Align is several orders of magnitude faster than the state-of-the-art protein structure comparison tools TM-Align, DaliLite, MATT and Yakusa, while achieving similar classification performances. Finally, we illustrate the difference between GR-Aligns flexible alignments and the traditional ones by querying a flexible protein in the Astral-40 database (11 154 protein domains). In this experiment, GR-Aligns top scoring alignments are not only in better agreement with structural classification of proteins, but also that they allow transferring more information across proteins.

Network analytics in the age of big data

How can we holistically mine big data? We live in a complex world of interconnected entities. In all areas of human endeavor, from biology to medicine, economics, and climate science, we are flooded with large-scale data sets. These data sets describe intricate real-world systems from different and complementary viewpoints, with entities being modeled as nodes and their connections as edges, comprising large networks. These networked data are a new and rich source of domain-specific information, but that information is currently largely hidden within the complicated wiring patterns. Deciphering these patterns is paramount, because computational analyses of large networks are often intractable, so that many questions we ask about the world cannot be answered exactly, even with unlimited computer power and time (1). Hence, the only hope is to answer these questions approximately (that is, heuristically) and prove how far the approximate answer is from the exact, unknown one, in the worst case. On page 163 of this issue, Benson et al. (2) take an important step in that direction by providing a scalable heuristic framework for grouping entities based on their wiring patterns and using the discovered patterns for revealing the higher-order organizational principles of several real-world networked systems.

Fuse: multiple network alignment via data fusion

MOTIVATION Discovering patterns in networks of protein-protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. However, the complexity of the multiple network alignment problem grows exponentially with the number of networks being aligned and designing a multiple network aligner that is both scalable and that produces biologically relevant alignments is a challenging task that has not been fully addressed. The objective of multiple network alignment is to create clusters of nodes that are evolutionarily and functionally conserved across all networks. Unfortunately, the alignment methods proposed thus far do not meet this objective as they are guided by pairwise scores that do not utilize the entire functional and evolutionary information across all networks. RESULTS To overcome this weakness, we propose Fuse, a new multiple network alignment algorithm that works in two steps. First, it computes our novel protein functional similarity scores by fusing information from wiring patterns of all aligned PPI networks and sequence similarities between their proteins. This is in contrast with the previous tools that are all based on protein similarities in pairs of networks being aligned. Our comprehensive new protein similarity scores are computed by Non-negative Matrix Tri-Factorization (NMTF) method that predicts associations between proteins whose homology (from sequences) and functioning similarity (from wiring patterns) are supported by all networks. Using the five largest and most complete PPI networks from BioGRID, we show that NMTF predicts a large number protein pairs that are biologically consistent. Second, to identify clusters of aligned proteins over all networks, Fuse uses our novel maximum weight k-partite matching approximation algorithm. We compare Fuse with the state of the art multiple network aligners and show that (i) by using only sequence alignment scores, Fuse already outperforms other aligners and produces a larger number of biologically consistent clusters that cover all aligned PPI networks and (ii) using both sequence alignments and topological NMTF-predicted scores leads to the best multiple network alignments thus far. AVAILABILITY AND IMPLEMENTATION Our dataset and software are freely available from the web site: http://bio-nets.doc.ic.ac.uk/Fuse/ CONTACT natasha@imperial.ac.uk SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Graphlet-based Characterization of Directed Networks

We are flooded with large-scale, dynamic, directed, networked data. Analyses requiring exact comparisons between networks are computationally intractable, so new methodologies are sought. To analyse directed networks, we extend graphlets (small induced sub-graphs) and their degrees to directed data. Using these directed graphlets, we generalise state-of-the-art network distance measures (RGF, GDDA and GCD) to directed networks and show their superiority for comparing directed networks. Also, we extend the canonical correlation analysis framework that enables uncovering the relationships between the wiring patterns around nodes in a directed network and their expert annotations. On directed World Trade Networks (WTNs), our methodology allows uncovering the core-broker-periphery structure of the WTN, predicting the economic attributes of a country, such as its gross domestic product, from its wiring patterns in the WTN for up-to ten years in the future. It does so by enabling us to track the dynamics of a country’s positioning in the WTN over years. On directed metabolic networks, our framework yields insights into preservation of enzyme function from the network wiring patterns rather than from sequence data. Overall, our methodology enables advanced analyses of directed networked data from any area of science, allowing domain-specific interpretation of a directed network’s topology.

Unified Alignment of Protein-Protein Interaction Networks

Paralleling the increasing availability of protein-protein interaction (PPI) network data, several network alignment methods have been proposed. Network alignments have been used to uncover functionally conserved network parts and to transfer annotations. However, due to the computational intractability of the network alignment problem, aligners are heuristics providing divergent solutions and no consensus exists on a gold standard, or which scoring scheme should be used to evaluate them. We comprehensively evaluate the alignment scoring schemes and global network aligners on large scale PPI data and observe that three methods, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. We study the collective behaviour of network aligners and observe that PPI networks are almost entirely aligned with a handful of aligners that we unify into a new tool, Ulign. Ulign enables complete alignment of two networks, which traditional global and local aligners fail to do. Also, multiple mappings of Ulign define biologically relevant soft clusterings of proteins in PPI networks, which may be used for refining the transfer of annotations across networks. Hence, PPI networks are already well investigated by current aligners, so to gain additional biological insights, a paradigm shift is needed. We propose such a shift come from aligning all available data types collectively rather than any particular data type in isolation from others.

Rebuttal to the Letter to the Editor in response to the paper: proper evaluation of alignment-free network comparison methods

Motivation: Network comparison is a computationally intractable problem with important applications in systems biology and other domains. A key challenge is to properly quantify similarity between wiring patterns of two networks in an alignment-free fashion. Also, alignment-based methods exist that aim to identify an actual node mapping between networks and as such serve a different purpose. Various alignment-free methods that use different global network properties (e.g. degree distribution) have been proposed. Methods based on small local subgraphs called graphlets perform the best in the alignment-free network comparison task, due to high level of topological detail that graphlets can capture. Among different graphlet-based methods, Graphlet Correlation Distance (GCD) was shown to be the most accurate for comparing networks. Recently, a new graphlet-based method called NetDis was proposed, which was claimed to be superior. We argue against this, as the performance of NetDis was not properly evaluated to position it correctly among the other alignment-free methods. Results: We evaluate the performance of available alignment-free network comparison methods, including GCD and NetDis. We do this by measuring accuracy of each method (in a systematic precision-recall framework) in terms of how well the method can group (cluster) topologically similar networks. By testing this on both synthetic and real-world networks from different domains, we show that GCD remains the most accurate, noise-tolerant and computationally efficient alignmentfree method. That is, we show that NetDis does not outperform the other methods, as originally claimed, while it is also computationally more expensive. Furthermore, since NetDis is dependent on the choice of a network null model (unlike the other graphlet-based methods), we show that its performance is highly sensitive to the choice of this parameter. Finally, we find that its performance is not independent on network sizes and densities, as originally claimed. Contact: natasha@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.