http://orcid.org/0000-0001-6183-3354
Figures
Abstract
Many biological studies involve either (i) manipulating some aspect of a cell or its environment and then simultaneously measuring the effect on thousands of genes, or (ii) systematically manipulating each gene and then measuring the effect on some response of interest. A common challenge that arises in these studies is to explain how genes identified as relevant in the given experiment are organized into a subnetwork that accounts for the response of interest. The task of inferring a subnetwork is typically dependent on the information available in publicly available, structured databases, which suffer from incompleteness. However, a wealth of potentially relevant information resides in the scientific literature, such as information about genes associated with certain concepts of interest, as well as interactions that occur among various biological entities. We contend that by exploiting this information, we can improve the explanatory power and accuracy of subnetwork inference in multiple applications. Here we propose and investigate several ways in which information extracted from the scientific literature can be used to augment subnetwork inference. We show that we can use literature-extracted information to (i) augment the set of entities identified as being relevant in a subnetwork inference task, (ii) augment the set of interactions used in the process, and (iii) support targeted browsing of a large inferred subnetwork by identifying entities and interactions that are closely related to concepts of interest. We use this approach to uncover the pathways involved in interactions between a virus and a host cell, and the pathways that are regulated by a transcription factor associated with breast cancer. Our experimental results demonstrate that these approaches can provide more accurate and more interpretable subnetworks. Integer program code, background network data, and pathfinding code are available at https://github.com/Craven-Biostat-Lab/subnetwork_inference
Author summary
There is a multitude of publicly available databases that contain information about biological entities (i.e., genes, proteins, and other small molecules) as well as information about how these entities interact together. However, these databases are often incomplete. There is a wealth of information present in the text of the scientific literature that is not yet available in these databases. Using tools that mine the scientific literature we are able to extract some of this potentially relevant information. In this work we show how we can use publicly available databases in conjunction with the information extracted from the scientific literature to infer the networks that are involved in specific biological processes, such as viral replication and cancer tumor growth.
Citation: Kiblawi S, Chasman D, Henning A, Park E, Poon H, Gould M, et al. (2019) Augmenting subnetwork inference with information extracted from the scientific literature. PLoS Comput Biol 15(6): e1006758. https://doi.org/10.1371/journal.pcbi.1006758
Editor: Andrey Rzhetsky, University of Chicago, UNITED STATES
Received: May 9, 2018; Accepted: January 4, 2019; Published: June 27, 2019
Copyright: © 2019 Kiblawi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Integer program code, background network data, and pathfinding code are available at https://github.com/Craven-Biostat-Lab/subnetwork_inference.
Funding: This work has been supported by NIH grants U54 AI117924 (MC, DC), T32 HG002760 (SK), and R01 CA077494 (AH, MG), the Howard Hughes Medical Institute (EP, PA), the Morgridge Institute (EP, PA), and the DARPA Big Mechanism Program (MC, HP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: Hoifung Poon is employed by Microsoft Corporation, although this employment had no effect on the design, analysis or conclusions of the study.
Introduction
An important and pervasive type of analysis in systems biology research is to characterize the set of molecular entities and interactions that are involved in a biological process or response of interest. This type of analysis, which we refer to as subnetwork inference, takes as input background knowledge describing potentially relevant entities and interactions (a network), along with experimental data characterizing the relevance of entities to the response of interest. It returns as output a subset of the entities and interactions (a subnetwork) that are predicted to be centrally involved in the response. This approach has been shown to lend insight and make accurate predictions in a wide range of biological applications. However, it suffers from several key limitations that arise due to the immense search space, and the reliance of the approach on curated databases of interactions. In this work, we explore several ways in which the subnetwork inference approach can be augmented with information that is automatically elicited from the scientific literature, and we empirically demonstrate that such literature extracted information can lead to more accurate and interpretable subnetworks.
Fig 1 provides an overview of the subnetwork inference task. One of the inputs to a subnetwork-inference approach is a background network consisting of (i) entities such as genes/proteins and complexes, and (ii) intracellular interactions such as protein-protein interactions, protein-DNA interactions, protein constituents of a complex, etc. The background network is commonly assembled by integrating interactions from publicly accessible, curated databases such as BioGRID [1] and Reactome [2]. Note that although each of these interactions is believed to occur in some cellular context, many of them may not be involved in the response of interest. When representing the background network as a graph, the nodes correspond to entities and the edges correspond to interactions. The other inputs for the task typically are sets of source and target nodes. These sets might be identified by experimental data or they might be defined using background knowledge. The computational task of subnetwork inference is to (i) identify a subset of edges and nodes in the graph that enable the sources to be connected to the targets, while (ii) adhering to constraints that specify required properties of the subnetwork (e.g., there must be at least one path from each source to a target), and (iii) optimizing an objective function that describes desirable properties of the subnetwork (e.g., it must be minimal in some sense).
The first panel shows a background network composed of molecular entities and known interactions among them. A subset of the entities are designated as source nodes (black), and a subset as target nodes (blue). White nodes show elements that are not considered source or target nodes. The second panel displays an inferred subnetwork which includes only a subset of the entities and interactions in the background network. Elements of the background network that are not included in the inferred subnetwork are greyed out.
Here, we consider two applications of subnetwork inference that are representative of the general task. The first application is to infer the host-cell subnetwork that is exploited by HIV during virus replication. In this analysis, the source nodes are host genes that have been identified as playing a significant role in viral replication via RNAi assays that systematically knock the genes down. The target nodes are HIV proteins. The goal of the inference task is to determine a subnetwork that explains how the RNAi-identified genes might be affecting HIV replication [3, 4].
The second application we consider is focused on characterizing a nuclear receptor, NR2F1, which is an important factor mediating the activity of the Mcs1a Mammary Carcinoma Susceptibility locus, and may have therapeutic relevance to triple-negative breast cancer [5]. In this analysis, NR2F1 is the sole source node, and the target nodes correspond to the genes that are differentially expressed when NR2F1 is overexpressed. The goal of the subnetwork inference task in this case is to identify the regulatory interactions that link NR2F1 to its downstream-regulated genes.
Although the subnetwork inference approach has proven to have significant value in a broad range of applications [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], it suffers from a number of key limitations. One of these is that the ability of the approach to identify a subnetwork that provides an accurate characterization of the response of interest is limited by the completeness and accuracy of the interactions represented in the background network. This, in turn, is determined by the completeness and accuracy of the curated data sources from which the background network is assembled. Although curated databases of molecular interactions tend to have high accuracy, it is well known that they capture only a fraction of the interactions that actually occur in cells. Moreover, these data sources are limited by what they have deemed eligible for inclusion, which are typically experimentally verified direct physical interactions. Few resources include indirect interactions, for example. Another limitation of the subnetwork-inference approach is that the putatively relevant entities—the sources and targets—may also be incomplete. Consider, for example, the task of identifying host genes involved in HIV replication from RNAi experiments. We know that RNAi screens in this context are likely to result in many false negatives, and even with multiple screens, we are likely to not detect many of the involved host genes [18]. A third limitation of the approach is that inferred subnetworks may be complex and hard to understand. This may be the case simply because the number of relevant nodes is large. For example, in the HIV application, more than a thousand genes are detected by the experimental screens as being relevant to HIV replication.
We argue that subnetwork inference methods are hindered by failing to take advantage of the wealth of knowledge that is represented only in the scientific literature, as opposed to structured databases. The hypothesis driving the research presented here is that we can more accurately characterize responses of interest by automatically extracting and leveraging information from the scientific literature. In this article, we investigate the use of literature-extracted information to (i) augment the set of nodes identified as sources in a subnetwork inference task, (ii) augment the set of interactions used in the background network, and (iii) support targeted browsing of a large inferred subnetwork by computing views of the subnetwork which consist of nodes and edges that are closely related to concepts of interest. Whereas an inferred subnetwork represents a process of interest (e.g., HIV replication), views can be used to identify and inspect more specific concepts within the inferred subnetwork (e.g., membrane scission). Fig 2 illustrates the first two of these augmentations, while Fig 3 represents the task of generating a view of a subnetwork.
The background network is composed of source nodes (black), target nodes (blue), and interactions (dashed and solid lines). Elements outlined in red represent information extracted from the scientific literature. In this case, the literature has been used to identify another source node (A), an additional target (V), and two additional interactions.
The first panel shows an inferred subnetwork. The second panel shows a view (yellow background) which delineates a set of nodes and interactions that are related to a concept of interest. The inferred subnetwork and view are composed of source nodes (black), target nodes (blue), and interactions (dashed and solid lines). Elements outlined in red represent entitities and interactions used as the basis of the view (i.e., those most closely linked to the concept).
Methods
In this section, we describe two subnetwork-inference applications considered in this article that we address using integer programming (IP). We also describe three ways in which we have augmented this approach by using literature-extracted information.
HIV host-virus interaction task
One approach to characterizing the host cellular machinery that is hijacked by a virus is to systematically suppress host gene products using techniques such as RNA interference [19], mutant libraries [20], or CRISPR/Cas9 [21]. Typically, these genome-wide screens identify a large number of host genes, which we refer to as hits, whose loss has a significant effect on the virus. However, the screens themselves do not reveal how the gene products of these hits are organized into the pathways that modulate the virus. Moreover, they may fail to detect a large number of host genes that are involved in the process [18]. Here, we consider the computational task of inferring directed subnetworks that hypothesize the pathways through which each hit modulates viral replication and also posit additional host genes that are involved in viral replication. Our methodology is based on previous work we have done using an integer program (IP) to infer the relevant subnetwork [3, 4].
In this task, which is illustrated in Fig 4, the source nodes are the genes that are found to be essential to HIV replication when knocked down using RNAi (i.e., the hits from these studies), and the target nodes are viral components which can be reached in the network via host factors that are known to interact directly with them. We refer to these host factors that directly interact with viral components as interfaces. The goal is to infer a subnetwork consisting of paths, each of which is a linear chain of interactions that begins with a hit (a source) and ends with a host gene product that directly interacts with a viral protein (a target).
The first panel displays a background network composed of source RNAi hits (black), interface nodes (green), and interactions (dashed and solid lines). Each interface is connected to a target viral component (blue). We can optionally augment the set of sources by using genes identified by GADGET as being relevant to HIV replication (red outline). The second panel displays an inferred subnetwork. Elements of the background network that are not included in the inferred subnetwork are shown in light grey.
The hits come from five RNAi screening studies that have identified human genes involved in HIV replication [22, 23, 24, 25, 26]. The union of the hit sets contains 1,178 host genes that act as source nodes in our subnetwork inference process. We compile a set of human-HIV interface proteins from the NCBI Human HIV-1 Protein Interaction Database [27] and BioGrid [1]. From these databases, we select as interfaces those host gene products that have a direct physical interaction with an HIV protein. In total, the background network contains 1,693 interfaces, 195 of which are also RNAi hits.
A background network is assembled from publicly accessible databases. We retrieve protein-kinase interactions, post-translational modification, and protein-protein interactions from a variety of sources [1, 28, 29, 30]. Protein complexes and reactions are taken from the Reactome collection of curated pathways [2]. The network is represented as a partially-directed graph. Each protein-protein interaction is represented as an edge in the graph, with directed interactions (such as kinase-substrate) represented as directed edges. Protein complexes are represented as separate nodes, with directed edges linking constituent genes to the complex. The reactions are separate nodes in the graph, with directed edges coming in from their inputs and catalysts, and directed edges going out to their products. The inputs and outputs to the reactions may be molecules, gene products, or protein complexes, and the catalysts may be gene products or protein complexes. The background network contains a total of 197,184 edges among 22,192 nodes, which include 9 HIV genes and 14,534 human genes. The remaining nodes are complexes, reactions, and small molecules.
The first step in our subnetwork inference approach is to generate a set of candidate paths. Using the hits as source nodes, we use a depth-first traversal to find all directed acyclic paths that lead from the source nodes to the targets. The search is conducted to a maximum depth of three interactions, so all paths have at most four nodes. All paths have the same directionality, going from the source node to an interface target node.
We refer to paths, nodes, and edges that are included in an inferred subnetwork as being relevant. The integer program identifies a subnetwork by determining values for a set of binary variables that represent the relevance of paths, edges, and nodes. The relevance of a path p is represented with a binary variable σp, which takes the value of 1 if the particular path is included in the subnetwork, and 0 otherwise. The relevance of an edge e is represented by the binary variable xe, which takes a value of 1 if the edge is in at least one relevant path, and 0 otherwise. The relevance of a node n is represented by the binary variable yn, which takes a value of 1 if the node is present in any relevant paths, and 0 otherwise.
The integer program determines the settings for these variables using the objective functions and constraints shown in Table 1. We denote the set of nodes as , the subset of source nodes (i.e., hits) as
, and the subset of interface genes as
.
is the set of unconfirmed genes—those that are neither hits nor interfaces.
is the set of protein complexes and
represents reactions.
refers to the set of edges,
refers to the edges that touch a particular node n, and
represents the nodes that are involved in edge e. The set of paths is denoted as
.
represents all edges in a given path p,
represents all nodes that are contained within path p,
represents the set of paths involving node n, and
represents the set of paths involving edge e.
The left column describes each objective function and constraint. The right column provides the mathematical formulation for each.
The two objective functions are optimized sequentially. We use the first objective function to select the relevant nodes, and the second to identify all possible paths among those nodes. The rationale for the second objective function is to avoid arbitrarily selecting the paths that are included in the subnetwork. Instead, we opt to include all paths that satisfy the other constraints and connect the selected nodes. We know that the set of hits is incomplete given that RNAi screens typically have many false negatives [18], and thus we would like to predict which other host genes are involved. However, we need a way to limit the size of the inferred subnetwork so as to not include everything. In our IP, we control the size of the inferred subnetwork by constraining the number of unconfirmed genes that can be included in the subnetwork, and use the first objective function to include those that maximize predicted relevance scores which are computed using a diffusion kernel [31]. The intuition behind this method is that each hit carries some amount of weight that is partially diffused out via its neighbors in the background network. Each node in the network thereby receives a weight according to its proximity and connectivity to the set of hits. After selecting which nodes are to be included in the subnetwork, the second objective function then maximizes the inclusion of paths from sources to targets that use these nodes. We solve the IP using a branch-and-cut method [32].
Due to the fact that there are multiple solutions that satisfy all the constraints and maximize the objective functions, we generate an ensemble of solutions and then return a consensus subnetwork characterizing the nodes and edges that occur with high frequency in the ensemble. We first construct 100 subsampled data sets by holding aside 25% of the hits and interfaces in each, thus treating them as unconfirmed genes. To construct the ensemble, we run the IP independently on each subsampled data set.
Augmenting HIV host-virus subnetwork inference with literature-extracted information
As mentioned above, the intersection among the hit sets identified by the RNAi knockdown screens is quite small. Prior research has indicated that this is due to the screens having many false negatives, and thus the number of genes involved in viral replication is likely to be much larger than even the number represented by the union of the HIV RNAi screens [18]. To address this limitation, we explore an approach that augments our hit set with additional genes that are associated with HIV replication in the scientific literature. More generally, we can think of this as an approach for augmenting a set of sources (or targets).
Our approach is based on a web-based tool called GADGET [33] that we have developed. GADGET identifies and ranks genes and metabolites that are associated in the biomedical literature with given queries. The queries may specify phenotypes, disease states, drugs, genes, processes, and other concepts that are expressible in a standard search-engine query language. GADGET ranks the genes/metabolites according to their association with the query. It is able to use several different ranking criteria, but the default criterion is where precision and recall are defined as follows. Let Ag represent the set of abstracts mentioning gene g, Aq be the set of abstracts matching query q, and
be the set of abstracts that both mention gene g and match the query q. We define adjusted precision as
. The adjusted precision criterion includes a “pseudocount” of 10 in the denominator in order to bias the measure towards those genes for which there is more evidence indicating their association with the query. We define recall as
, i.e. the fraction of the abstracts matching the query that also mention gene g. F1 is the default ranking criterion in GADGET since it prefers genes whose associated literature is both specific to the query ands cover many of the query-relevant abstracts.
To augment our set of hits in the HIV subnetwork-inference task, we query GADGET for “HIV” which returns an additional 738 human genes that appear in two or more query-matching abstracts and which were not already in our set of interfaces or RNAi-screen identified hits. Instead of employing GADGET’s ranking functions in this analysis, we simply use all 738 of these genes. In our view-generation experiments, which are described shortly, we make use of GADGET’s ranking capability. We add these 738 genes to the IP as additional sources, and generate an ensemble of subnetworks as described above. This idea is illustrated in Fig 4 where some of the nodes (those with a red border) have been determined to be sources by GADGET.
NR2F1 subnetwork inference task
In the second application we consider, our objective is to connect a nuclear receptor known as NR2F1 (the source node) to a list of genes that are differentially expressed when NR2F1 is over-expressed (the target nodes). Prior studies suggest NR2F1’s potential as a therapeutic agent in triple negative breast cancers (TNBC) since several lines of evidence indicate that NR2F1 may act as a tumor suppressor, given its association with decreased proliferation and less aggressive clinical subtypes [5, 34]. The subnetwork-inference task we consider here is to find paths connecting NR2F1 to as many differentially expressed (DE) genes as possible, while identifying other genes that mediate the regulation of the DE genes. An overview of this task is shown in Fig 5.
The first panel displays a background network composed of the source NR2F1 (black), target DE genes (blue), and interactions (dashed and solid lines). Transcription factors are identified as bright magenta nodes. The background network is augmented by a set of Literome interactions (purple arrows). The second panel displays the inferred subnetwork. Orange nodes show elements that are not considered source, transcription factors, or target nodes; we refer to these as intermediate nodes in the inferred subnetwork.
RNA-Seq was used to identify a set of genes that were differentially expressed when NR2F1 was overexpressed in a TNBC cell line. This set of 340 DE genes served as the targets in our analysis. To assemble the background network, protein-protein interactions were gathered from HPRD [30] and BioGRID [1]. Protein-DNA interactions were gathered from MCF-7 ChIP-chip data collected by Kittler et al. [35]. Although MCF-7 cells do not represent TNBC, this was the closest cell line for which ChIP-chip data was available and our goal was to generously include candidate interactions that might potentially be relevant to TNBC. Additional protein-DNA interactions relevant to the differentially expressed genes were identified using the ENCODE ChIP-Seq Significance Tool (encodeqt.simple-encode.org) [36]. The input to the ENCODE tool consisted of our set of DE genes The tool searches a 5000bp upstream and downstream window in all available cell lines in order to find potential regulatory proteins associated with a given set of genes. The complete background network consisted of 349,149 interactions (171,789 protein-protein and 132,662 protein-DNA interactions) and 14,874 genes/proteins.
Using NR2F1 as the source node, a depth-first traversal was used to find all directed acyclic paths that lead from NR2F1 to the DE genes. Each path is required to end in a direct protein-DNA interaction and the search was conducted to a maximum depth of three interactions, so all paths had at most four nodes. All paths have the same directionality, going from the NR2F1 source node to a DE target node.
Given these paths, we infer an NR2F1 subnetwork by solving a series of integer programs. The role of the IP approach is to select a subset of the paths (and hence interactions and nodes) from the background network that reach as many of the DE genes as possible, while being parsimonious about incorporating non-DE genes, and taking into account RNA-Seq expression levels indicating the relevance of each included node. To incorporate RNA-Seq data into this process, normalized counts per million values were obtained from edgeR [37] and were used to weight nodes for selection, with the requirement that incorporated genes be expressed in MDA-MB-468 cells, which is a triple negative breast cancer cell line. The rationale for the final objective function is to avoid arbitrarily selecting the edges that are included in the subnetwork. Instead, we opt to include all edges that satisfy the other constraints and connect the selected nodes.
A description of the constraints and objective functions used in the IPs is shown in Table 2. The objective functions are optimized in sequence, with the solution to each subsequently being incorporated as a constraint before the next one is optimized. We denote the set of nodes as where each node, n, represents either a protein or a target gene.
is a single element set containing the source node, NR2F1, and
is the set of DE genes.
represents the remaining nodes that are not the source node or targets. The set of edges
represents both the undirected (protein-protein interactions) and directed (protein-DNA interactions) edges.
refers to the edges that touch a particular node n, and
refers to the set of edges directed into node n.
represents the nodes that are involved in edge e. The set of paths is denoted as
.
represents all edges in path p.
represents all nodes that are contained within a specific path.
represents the set of paths involving node n, and
represents the set of paths involving edge e. The RNA-Seq determined score for a node n is given by rn.
The left column describes each objective function and constraint. The right column provides the mathematical formulation for each.
The integer program identifies a subnetwork by determining values for a set of binary variables that represent the relevance of paths, edges, and nodes. The relevance of a path p is represented with a binary variable σp, which takes the value of 1 if the particular path was predicted to be included, and 0 otherwise. The predicted relevance of an edge is represented by the binary variable xe, which takes a value of 1 if the edge was in at least one relevant path, and 0 otherwise. The predicted relevance of a node is represented by the binary variable yn. The variable receives a value of 1 if it is present in any relevant paths, and 0 otherwise.
We use the IP approach to infer an ensemble of subnetworks from subsampled datasets and then construct a consensus subnetwork as we did in the host-virus analysis.
Augmenting NR2F1 subnetwork inference with literature-extracted information
As previously discussed, one of the key limitations of the standard subnetwork-inference approach is that it relies on existing, structured databases of interactions which may be highly incomplete. Although the interactions present in publicly available databases allow us to generate paths connecting NR2F1 to many of the DE genes, we are not able to reach all of them. To address this limitation, we exploit information extracted automatically from the scientific literature. There is a large body of prior work addressing the tasks of extracting binary relations and more complex events from text sources [38, 39, 40], as well as efforts to assemble biological networks from these extracted relationships [41, 42]. However, these information-extraction methods have not been previously used to complement a background network in a subnetwork-inference application.
In order to augment our existing interactions, we use the Literome system [43, 44] which is able to extract regulatory interactions from both abstracts and the full-text of articles in the scientific literature. By querying Literome for our DE genes, we retrieved interactions extracted from the text and added them to our background network. In total, 44,879 additional Literome interactions were added. In our IP, we treat these regulatory interactions in the same way as our protein-DNA interactions and otherwise run the IP in the same way.
Generating a view of an inferred subnetwork with literature extracted information
Given the large number of genes and other entities involved in many biological processes, even the most stringently-defined subnetworks can be large and difficult to manually inspect. In our host-virus study, for example, there are more than two thousand hits and interfaces (i.e., genes that are surely involved in HIV replication). Here we present a methodology for generating a view of a subnetwork, which is a graphical representation of the part of an inferred subnetwork that is highly related to a given concept of interest. The concept might represent, for example, a process, subcellular location, or stage of the viral lifecycle.
Given a subnetwork and a set of genes NQ representing a concept of interest, the view generation process returns a set of nodes, and associated edges and paths, that are enriched for gene set. The set of genes could be specified (i) manually, (ii) by selecting genes that have been annotated with an ontology term of interest, or (iii) by identifying genes associated with the concept of interest in the scientific literature. Here we explore the third approach by using queries to GADGET to define gene sets that are closely related to specific concepts.
To compute a view, we first rank every node n in the inferred subnetwork for its predicted functional similarity to the given query set of genes NQ. Nodes are considered functionally similar if they share relevant paths. Our similarity function, , measures the fraction of paths in an inferred subnetwork that contain both n and at least one query node
, out of all paths that contain either n or any query node q. Let
be the set of paths in the consensus network that contain a node n. Our similarity function is defined as:
After ranking the consensus nodes by this similarity function, we take the top k as predicted additions to the query set. We then extract all of the paths that consist exclusively of query nodes, predicted additions, and targets.
Results/discussion
HIV host-virus subnetwork inference
In the HIV subnetwork application, our baseline set of source nodes were those genes identified as important to HIV replication in RNAi studies. Here consider the effect of augmenting this set of source nodes with human genes returned by GADGET for the query HIV. We evaluated the resulting subnetworks by measuring their ability to predict the relevance of genes to HIV replication as determined by whether they were included in an inferred subnetwork.
We used a methodology in which information about the relevance of 25% of the hits and interfaces (i.e., known relevant genes) was held aside on each iteration. That is, although the genes were still included in the background network, information about whether they were hits, interfaces, or neither was hidden. We can estimate the accuracy of our approach by checking each inferred subnetwork for the presence of the hits and interfaces that have been held aside from their input. Given the absence of a set of genes that known not to be involved in HIV replication, we used the set of all unconfirmed background-network human genes as the set of negatives. For each gene, we calculated a confidence value as its frequency of being included in the inferred subnetworks when held aside. By varying a threshold on these confidence values, we plotted a precision-recall curve characterizing the predictive accuracy of our method. Recall is defined as the fraction of truly relevant genes (hits and interfaces) that are predicted to be relevant, and precision is defined as the fraction of genes predicted to be relevant that truly are relevant. In this context, we consider precision to be the more important of the two measures, as it is better to avoid devoting follow-up experiments to false positives.
We inferred consensus subnetworks and generated precision-recall curves for both the baseline approach and the GADGET-augmented approach. These results are shown in Fig 6. The horizontal green line in the figure represents the prevalence of known hits and interfaces in the background network, and thus represents the default level of precision. Although both approaches demonstrate substantial predictive accuracy, the GADGET-augmented subnetworks show a significant increase in precision at the high-confidence (low recall) end of the curves, demonstrating the value of incorporating literature-extracted information into the process.
The blue line represents results from the consensus network without GADGET genes augmenting the sources. The red line represents results from the consensus network generated with GADGET augmentation. The horizontal light green line in the figure represents the prevalence of known hits and interfaces in the background network.
We also considered augmenting our background networks with interactions extracted from the scientific literature. We queried Literome with our list of relevant genes and returned all interactions that contained at least one of those genes. We then incorporated these interactions into our baseline background network. Comparing the precision-recall curves from the baseline approach and the Literome-augmented approach, there was no significant improvement in precision at any point along the curve. We also added literature-extracted interactions to our GADGET-augmented background network. When comparing the precision-recall curves from the GADGET-augmented approach and the GADGET and Literome augmented approach, there was once again no significant improvement in precision. However, we note that the addition of the Literome interactions did not diminish the accuracy of the inferred subnetworks, suggesting that there is little risk of overfitting when including literature extracted interactions. Fig 7 shows the precision recall curves for all the augmentation experiments performed.
The blue line represents results from the consensus network without GADGET or Literome augmentation. The red line represents results from the consensus network generated with GADGET genes augmenting the sources. The light blue line represents results from the consensus network generated with Literome interactions augmenting the network. The orange line represents results from the consensus network generated with both GADGET genes and Literome interactions augmenting the sources. The horizontal light green line in the figure represents the prevalence of known hits and interfaces in the background network.
NR2F1 subnetwork inference
For the breast cancer task, our goal was to infer a parsimonius subnetwork that connects the nuclear receptor NR2F1 to a set of genes that are differentially expressed (DE) when NR2F1 is overexpressed. Our IP attempts to find the paths connecting NR2F1 to as many differentially expressed genes as possible. With our baseline background network (which does not include edges from Literome), we were able to reach 314 out of 340 differentially expressed genes. In order to reach more DE genes, we used Literome to incorporate additional interactions into the background network. We queried Literome for our 340 DE genes and found all interactions that included one of those genes as a downstream target. We re-ran our IP and discovered that we were subsequently able to reach 326 out of 340 differentially expressed genes from NR2F1. The number of intermediate nodes used to connect the genes also increased when we used Literome due to the fact that more nodes were necessary to reach the additional differentially expressed genes.
We use a set of genes that are essential for basal tumor cell generation [45] to determine the degree to which our IP returns a subnetwork that is biologically relevant. We expect that the inferred subnetwork will be closely related to genes involved in basal tummor cell generation. Although none of these genes is incorporated in our consensus subnetworks, a number of them are neighbors (in the background network) with genes in the inferred subnetworks. Fig 8 displays the cumulative number of subnetwork genes that are neighbors of a gene essential for basal tumor cell generation. We rank each gene included in a subnetwork ensemble by the number of subnetworks in which it occurs, and construct these plots for our baseline subnetwork ensemble and our subnetwork ensemble that used Literome interactions. When they incorporate regulatory interactions from Literome, our inferred subnetworks are more related to the basal tumor cell essential genes. The larger number of genes that are incorporated into some member of the Literome-based ensemble is due to the fact that the constituent subnetworks tend to be larger since they connect to more DE genes.
The magenta line represents genes included the Literome-augmented subnetworks, and the green line represents genes included in the baseline subnetworks which did not contain literature extracted information.
We also use RNA-Seq data to evaluate the subnetworks. Genes that are highly expressed may be more essential for the differences within certain cell types. We obtained RNA-Seq data in counts per million (CPM) for every gene expressed in our cells and ranked the genes based on sequence read abundance. Similar to the analysis above, we varied a threshold on the ranked list of genes and counted the number of genes above the threshold that were in the top 10% of genes in terms of RNA-Seq abundance. In Fig 9, we see that more subnetwork genes are in the top ten percent of the highly expressed genes when we use Literome edges.
The magenta line represents genes included in the Literome-augmented subnetwork, and the green line represents genes included in the baseline subnetwork which did not contain literature extracted information.
The results of this study indicate that literature-extracted interactions may provide value by enabling an inferred subnetwork to “explain” additional data. In this application, we were able to include more DE genes into the inferred subnetwork when including literature-extracted interactions in our background network. Moreover, the augmentation with literature-extracted interactions led to the incorporation of additional relevant genes in the inferred subnetwork.
Generating views of the HIV host-virus inferred subnetwork
To demonstrate the value of our view-generation approach in browsing and understanding a large subnetwork, we consider the case of computing views onto our inferred HIV consensus subnetwork. This subnetwork includes 14,426 edges connecting 948 nodes and thus is quite large and complex to comprehend. The inherent complexity of HIV subnetwork is due to the fact that a large number of host genes and processes are involved across the multiple stages of viral replication. We illustrate the view-generation approach by computing views that focus on genes related to the concepts of membrane scission and intrinsically disordered proteins. Whereas the inferred subnetwork represents the process of HIV replication, the views we generate isolate more specific concepts (membrane scission, intrinsically disordered proteins) represented within the subnetwork.
In order to assemble a set of genes for the membrane scission view, we issued the query HIV AND “membrane scission” to GADGET. This query returns 16 genes which GADGET has determined are associated with the query. We used our similarity function to rank all nodes in the HIV consensus subnetwork based on the frequency with which they are found in the same paths as these GADGET query genes. We took the top 10 of these ranked genes to be used as predicted additions to the membrane scission concept. We then assembled the consensus paths that consisted entirely of our GADGET query genes, predicted additions, and HIV proteins.
Fig 10 shows the complete inferred HIV subnetwork, highlighting the genes that are selected for the membrane scission view. Fig 11 shows the resulting view for this concept. Seven of the 16 genes returned by the GADGET query are present in it. Four of the additional genes included are RNAi hits, one is an interface, and four are both hits and interfaces. As illustrated by this example, views provide a flexible and concise way to comprehend a large subnetwork by selecting conceptually coherent portions of it.
Light grey nodes represent nodes within the subnetwork that are not part of the view. Black nodes represent RNAi hits (source nodes), green nodes represent host interface genes, dark grey nodes represent elements that are both RNAi hits and interfaces, and blue nodes represent viral components (target nodes). Nodes with red borders are the GADGET hits used to anchor the view.
Black nodes represent RNAi hits (source nodes), green nodes represent host interface genes, grey nodes represent elements that are both RNAi hits and interfaces, and blue nodes represent viral components (target nodes). Nodes with red borders are the GADGET hits used to anchor the view.
Although the gene set that served as the basis of this view came from a GADGET query, there are multiple sources which could provide a gene set such as the Gene Ontology [46]. The advantage of GADGET in this context is that it can retrieve a list of genes for a very specific concept (note that our query specified the conjunction of HIV and “membrane scission”), or a concept that is not defined in any ontology.
In order to demonstrate that queries are not limited to terms defined in an ontology, we computed a view based on the concept intrinsically disordered proteins. We queried GADGET for the terms HIV AND “intrinsically disordered.” Using the same methodology as above, we inferred a view that includes of 8 of the 36 genes returned by GADGET. The view, which is shown in Fig 12 consists of 26 nodes connected by 94 edges.
Black nodes represent RNAi hits (source nodes), green nodes represent host interface genes, grey nodes represent elements that are both RNAi hits and interfaces, and blue nodes represent viral components (target nodes). Nodes with red borders are the GADGET hits used to anchor the view.
The experiments presented in this section show how literature-extracted information can be used to explore and gain insight into a large inferred subnetwork by generating different views of the subnetwork. The key idea of a view is to select a subset of the genes in an inferred subnetwork that are enriched for a concept of interest, such as a cellular process or gene products sharing a specific physical property.
Conclusion
We have investigated the use of information automatically extracted from the scientific literature to augment the process of inferring subnetworks characterizing biological responses of interest. Specifically, we have used literature-extracted information to (i) enlarge the set of nodes identified as sources in a subnetwork inference task, (ii) enlarge the set of interactions used in the background network, and (iii) support targeted browsing of a large inferred subnetwork by computing views of the subnetwork that are closely related to concepts of interest.
The empirical studies we present demonstrate that literature-extracted information can improve the explanatory power and accuracy of subnetwork inference in both of the applications considered. However, we argue that our general approaches are relevant to a range of other network analysis tasks, including predicting and ranking genes that are likely to be involved in the same response as a given set of genes [47, 48, 49, 50, 51, 52], and combining multiple network data sets in order to perform classification or collaborative recommendation [53, 54, 55, 56, 57].
Although the specific constraints and objective functions used in subnetwork inference are somewhat application-dependent, the integer programs typically incorporate several common elements. Among these elements are a background network consisting of subcellular entities and interactions, and a procedure that aims to connect source and target nodes in the network in order to optimize various objectives. Our approach is applicable to any network analysis task that shares these common elements. In cases in which either the source or target nodes are believed to be incomplete, tools like GADGET can be used to augment the sources or targets by mining the scientific literature. In cases in which the relevant interactions are believed to be incomplete, tools like Literome can be used to augment the set of interactions in the background network.
We consider this work as an initial foray into exploring the range of ways in which text mining can boost the subnetwork-inference process. For example, we also plan to explore using literature-extracted information to extend the types of relationships that are represented in the background network, and to prioritize the inclusion of entities and interactions into subnetworks.
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