5. Lecture SS 20005Cell Simulations1 V5: Functional inference from interaction networks.

5. Lecture SS 20005

Cell Simulations 1

V5: Functional inference from interaction networks

5. Lecture SS 20005

Cell Simulations 2

Aim: associated protein function at the proteomic scale

Even the best-studied model organisms contain a large number of proteins whose

functions are currently unknown. E.g. about one-third of the proteins in

Saccharomyces cerevisiae remain uncharacterized.

Traditionally, computational methods to assign protein function have relied largely

on sequence homology. The recent emergence of high-throughput experimental

datasets have led to a number of alternative, non-homology based methods for

functional annotation. These methods have generally exploited the concept of

guilt by association, where proteins are functionally linked through either

experimental or computational means.

Nabieva et al., Bioinformatics 21, i1 (2005)

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Associating protein function from proteomic data

Large-scale experiments have linked proteins that

- physically interact (Ito et al., 2001; Uetz et al., 2000; Gavin et al., 2002; Ho et

al., 2002; Rain et al., 2001; Giot et al., 2003; Li et al., 2004),

- are synthetic lethals (Tong et al., 2001, 2004),

- are coexpressed (Edgar et al., 2002) or

- are coregulated (Lee et al., 2002; Harbison et al., 2004).

In addition, computational techniques linking pairs of proteins include

- phylogenetic profiles (Gaasterland and Ragan, 1998; Pellegrini et al., 1999),

- gene clusters (Overbeek et al., 1999),

- conserved gene neighbors (Dandekar et al., 1998) and

- gene fusion analysis (Enright et al., 1999; Marcotte et al., 1999a).

Perhaps, though not surprisingly, integrating the information from several sources

provides the best method for linking proteins functionally.


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Features of integrated networks

Several groups have attempted to partition interaction networks into functional

modules that correspond to sets of proteins that are part of the same cellular

function or take part in the same protein complex.

These functional modules, or clusters, are useful for annotating uncharacterized

proteins, as the most common functional annotation within a cluster can be

transferred to uncharacterized proteins.

Proteins in experimentally and computationally determined interaction graphs

have been grouped together based on

- shared interactions,

- the similarity between shortest path vectors to all other proteins in the network

- and shared membership within highly connected components or cliques.


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Although such a simple majority vote approach, named Majority, has clear predictive value, it takes

only limited advantage of the underlying graph structure of the network. For example, in the interaction

network given in Figure 1, ‘Majority’ would assign functions to proteins d and f , but not to protein e, even

though our intuition might indicate that protein e has the same function as proteins d and f ; there are

several examples in the yeast proteome similar to this one (Schwikowski et al., 2000). Naturally, one

wishes to generalize this principle to consider functional linkages beyond the immediate neighbors in the

interaction graph, both to provide a systematic framework for analyzing the entirety of physical interaction

data for a given proteome and to make predictions for proteins with no annotated interaction partners.


The strategy described here is more closely related to recent attempts to classify proteins according to the functional annotations of their network neighbors; these methods do not explicitly cluster proteins. Schwikowski et al. (2000) use physical interaction data for baker’s yeast, and predict the biological process for each protein by considering its neighboring interactions and taking the three most frequent annotations.

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However, this approach, named Neighborhood, does not consider any aspect of

network topology within the local neighborhood.

E.g., Figure 2 shows two interaction networks that are treated equivalently when

considering a radius of 2 and annotating protein a; however, in the first case, there

is a single link that connects protein a to the annotated proteins, and in the second

case, there are several independent paths between a and the annotated proteins,

and moreover, two of these proteins are directly adjacent to a.


Hishigaki et al. (2001) extended Majority algorithm by predicting a protein’s function by looking at all proteins within a particular radius and finding over-represented functional annotations.

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Method: physical interaction network

Construct the protein–protein physical interaction network using the protein

interaction dataset compiled by GRID (Breitkreutz et al., 2003).

The resulting network is a simple undirected graph G = (V , E), where there is a

vertex or node v V for each protein, and an edge between nodes u and

v if the corresponding proteins are known to interact physically (as determined by

one or more experiments).

Initially, we consider a graph with unit-weighted edges, and then consider

weighting the edges by our ‘confidence’ in the edge.

The weight of the edge between u and v is denoted by wu,v.

For all reported results, consider only the proteins making up the largest

connected component of the physical interaction map (4495 proteins and 12 531

physical interaction links).


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Method: functional annotations

Several controlled vocabulary systems exist for describing biological function,

including Munich Information Center for Protein Sequences (MIPS) and the Gene

Ontology (GO) project.

We use the MIPS functional hierarchy, and consider the 72 MIPS biological

processes that comprise the second level of hierarchy.

Of the 4495 proteins in the largest connected component of the yeast physical

interaction map, 2946 have MIPS biological process annotations. We also

experimented with GO annotations; the overall conclusions made in this paper are

not affected.


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Method: weighted functional linkages

The reliabilities of different data sources vary, even if they are based on the same

underlying technology.

In the context of network-based algorithms, it is possible to weight edges so as to model the

reliability of each interaction. For physical interactions, this reliability is in turn based on the

experimental sources that contribute to our knowledge

about the existence of the interaction.

To determine these values, separate all experimental sources of physical interaction data into

several groups, placing each high-throughput dataset into a separate group, and allocating

one group for the family of all specific experiments.

For each group of experiments, compute what fraction of its interactions connect proteins with

a known shared function. Assume that the reliabilities of different sources are independent,

and thus conclude by estimating the reliability of an interaction to be the noise (or unreliability)

of the underlying data sources.

If ri is the reliability of experimental group i, compute the reliability of the edge by 1 - i (1-ri ),

where the product runs over all experiments i where this interaction is found. This treats each

ri as a probability and assumes independence.


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Algorithms: Majority

as in Schwikowsky et al. (2000): consider all neighboring proteins and sum up the

number of times each annotation occurs for each protein.

„The annotated functions of all neighbors of P are ordered in a list, from

the most frequent to the least frequent. Functions that occur the same number

of times are ordered arbitrarily. Everything after the third entry in the list

is discarded, and the remaining three or fewer functions are declared as

predictions for the function of P.“

In the case of weighted interaction graphs take a weighted sum.


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Algorithms: Neighborhood algorithm

Adapted from Hishigaki et al. (2001) Protein interaction map (Figure B): each node represents a protein and each edge represents the interaction between two proteins.Predict function of each protein in the map (black circle in Figure C), based on the functions of ‘n-neighbouring proteins’, which are defined as a set of proteins reached via n physical interactions at most (n is an integer parameter).

E.g. all proteins enclosed by the inside dashed circle are ‘1-neighbouring proteins’, and those enclosed by the outside circle are ‘2-neighbouring proteins’. The protein of interest is assigned the function with the highest 2 value among functions of all n-neighbouring proteins. For each member of the function category, the 2 value is calculated using the following formula:

where i denotes a protein function, e.g. ‘Golgi’, ‘DNA repair’ and ‘transcription factor’, ei denotes an expectation number of i in n-neighbouring proteins expected from the distribution on the total map, and ni denotes an observed number of i in n-neighbouring proteins. Then, the function of a query protein is predicted to be the function i with the maximum 2 value. When there are multiple functions with the largest 2 value, both functions are assigned. The optimal n value is determined by a so-called self-consistency test, where the predicted functions of all proteins in the map are compared with their annotated functions for each n.

Here: consider neighborhoods of radius 1, 2 and 3.

This method does not extend naturally to the case of weighted interaction graphs.


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Algorithms: GenMultiCut

It was suggested that functional annotations on interaction networks should be

made in order to minimize the number of times different annotations are

associated with neighboring proteins.

Some authors used simulated annealing in an attempt to minimize this objective

function and aggregate results from multiple runs, whereas others used a

deterministic approximation, and consider the case where edges are weighted

using gene expression information.

The formulation in these two studies is similar to the minimum multiway k-cut

problem. In multiway k-cut, the task is to partition a graph in such a way that each

of k terminal nodes belongs to a different subset of the partition and so that the

(weighted) number of edges that are ‘cut’ in the process is minimized. In the more

general version of the multiway k-cut problem considered here, the goal is to

assign a unique function to all the unannotated nodes so as to minimize the sum

of the costs of the edges joining nodes with no function in common.


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Although minimum multiway k-cut is NP-hard (Dahlhaus et al., 1994), it was

found that the particular instances of minimum multiway cut arising here can, in

practice, be solved exactly when stated as an ILP.

Introduce a node variable xu,a for each protein u and function a.

Set xu,a = 1 if protein u is predicted to have function a.

If a protein u has known functional annotations, variable xu,a is fixed as 1 for its

known annotations a and as 0 for all other annotations.

We also introduce an edge variable xu,v,a for each function a and each pair of

adjacent proteins u and v. This variable is set to 1 if both proteins u and v are

annotated with function a.

Minimizing the weighted number of neighboring proteins with different annotations

is the same as maximizing the number with the same annotation, and so we have

the following ILP:Nabieva et al., Bioinformatics 21, i1 (2005)

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The first constraint specifies that exactly one functional annotation is made for any

protein.

The second and third constraints ensure that if protein u is annotated with function

a, xu,a is set as a constant to 1, and if protein u is annotated but not with function a,

xu,a is set as a constant to 0.

The third and fourth constraints ensure that a particular function is picked for an

edge only if it is also chosen for the corresponding proteins.


annot(u) : set of known annotations for protein u,

FUNC = Uu annot(u) : set of all functional annotations.

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considering multiple GenMultiCut optimal solutions

An important consideration in this framework is the existence of multiple optimal

solutions. E.g. the network in Figure 3 has seven minimum cuts of value 1, and

while the GenMultiCut criterion does not favor any one cut over the

other, if we find all optimal cuts for this graph, we observe that x2 is in fact

annotated with F1 more often than with F2 in the assignments made by these cuts.

Thus, a sense of distance to annotated nodes is in fact present in the set of all

optimal solutions.


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attempt to sample from the space of optimal solutions:

(1) add constraints to the ILP which require that each consecutive solution is

different from any previous solution in the value it assigns to at least 5% of the

node variables xu,a.

(2) introduce uniform self-weights wu,a for each protein u and function a.

These self-weights are then perturbed randomly by adding a very small offset to

each. Then maximize

a number of times


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Algorithms: FunctionalFlow

The functional flow algorithm generalizes the principle of ‘guilt by association’ to groups of

proteins that may or may not interact with each other physically.

Treating each protein of known functional annotation as a ‘source’ of ‘functional flow’ for that

function. After simulating the spread over time of this functional flow through the

neighborhoods surrounding the sources, the ‘functional score’ is obtained for each protein in

the neighborhood; this score corresponds to the amount of ‘flow’ that the protein has

received for that function, over the course of the simulation.

The functional flow-based model allows us to incorporate a distance effect, i.e. the effect of

each annotated protein on any other protein depends on the distance separating these two

proteins.

Running this process for each biological function in turn, we obtain, for each protein, the

score for each function (the score may be 0 if the ‘flow’ for a function did not reach that

protein during the simulation). Thereupon, for any protein, we take the functions for which

the highest score was obtained as its predicted functions.


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More specifically, for each function in turn, we simulate the spread of functional flow by an

iterative algorithm using discrete time steps. We associate with each node (protein) a

‘reservoir’ which represents the amount of flow that the node can pass on to its neighbors at

the next iteration, and with each edge, a capacity constraint that dictates the amount of flow

that can pass through the edge during one iteration.

The capacity of an edge is taken to be its weight. Each iteration of the algorithm updates

the reservoirs using simple local rules: a node pushes the flow residing in its reservoir to its

neighbors proportionally to the capacities of the respective edges and subject to further

constraints that the amount of flow pushed through an edge during an iteration does not

exceed the capacity of the edge, and that flow only spreads ‘downhill’ (i.e. from proteins with

more filled reservoirs to nodes with less filled reservoirs).

Finally, at each iteration, an ‘infinite’ amount of flow is pumped into the source protein

nodes; thus, the sources always have enough flow in their reservoir to fill the capacity of

their outgoing edges.


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The functional score is the amount of flow that has entered a protein’s reservoir in

the course of all iterations. Since the flow is ‘pumped’ into the sources at each

step, the amount of flow a node receives from each source is greater for nodes

that are closer to that source than for nodes that are farther away from it.

Thus, a source’s immediate neighbor in the graph receives d iterationsworth of

flowfrom the source, whereas a node that is two links away from the source

receives d - 1 iterations worth of flow.

Similarly, the number of iterations for which the algorithm is run determines the

maximum shortest-path distance that can separate a recipient node from a source

in order for the flowto propagate from the source to the recipient. In the context of

protein interaction, a relatively small number of iterations is sufficient. We choose

d = 6, which is half the diameter of the yeast physical interaction network.


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More formally, for each protein u in the interaction network, we define a variable

Rta(u) that corresponds to the amount in the reservoir for function a that node u

has at time t .

For each edge (u, v) in the interaction network, we define variables gta(u, v) and gt

a

(v, u) that represent the flow of function a at time t from protein u to protein v, and

from protein v to protein u. We will run the algorithm for d time steps or iterations.

At time 0, we only have reservoirs of function a at annotated

nodes:


At each subsequent time step, we recompute the reservoir of each protein by

considering the amount of flow that has entered the node and the amount that has

left:

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Initially, at time 0, there is no flow on the edges, and ga

0 (u, v) = 0.

At each subsequent time step, we have the flow proceeding downhill and

satisfying the capacity constraints:


Finally, the functional score for node u and function a over d iterations is

calculated as the total amount of flow that has entered the node:

Why don‘t we simply take the current value of the reservoir?

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Comparison of the four methods

Comparison of four basic methods on theunweighted physical interaction mapWe compare the performance of Majority, Neighborhood, GenMultiCut and FunctionalFlow on the unweighted yeast physical interaction map, using a 2-fold cross-validation. Figure 4 plots as a function of FP the number of TPs eachmethod predicts (i.e. these graphs are obtained by varying the scoring threshold for each of the methods). The Functional-Flow algorithm identifies more TPs over the entire range of FPs than either GenMultiCut or Neighborhood using radius 1, 2 or 3. FunctionalFlow performs better than Majority when proteins are not directly interacting with at least three proteins of the same function; this is evident from Figure 4 since the score for Majority counts up the the most frequent neighboring annotation (e.g. the rightmost point for Majority corresponds to proteins whose highest functional scores are one). Thus, FunctionalFlow is the method of choice when considering proteins that do not interact with many annotated proteins. Even in well-characterized proteomes, such as baker’s yeast, there are ca. 1200 proteins that have fewer than three annotated neighbors.


The Neighborhood algorithm performs similarly with either radius 1 or 2 in the high-confidence region (i.e. corresponding to a low FP rate, given in left-most portion of the ROC curve). However, radius 1 (i.e. considering just direct interactions) has better overall performance than radius 2 or 3, demonstrating that Neighborhood’s strategy of ignoring topology is not optimal. Moreover, comparing Majority with Neighborhood using radius 1 demonstrates that the ÷2-test is not as effective in scoring as just summing up the number of times a particular annotation occurs in the neighboring proteins.

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Reliability and data integration

To evaluate the approach for modeling physical interaction reliability as edge weights, we test

the performance of FunctionalFlow using three ways of assigning physical interaction

weights.

(1) assign each edge a unit weight; this corresponds to the unweighted physical interaction map.

(2) assign each experimental source a reliability score of 0.5; this rewards interactions that are

found by more than one experiment.

(3) assign each experimental source the predictive value (estimated in crossvalidation); here,

edges obtained from multiple, more reliable experiments are given higher weights.


Figure 5 shows that rewarding multiple experimental evidence is beneficial, but that the main advantage comes from taking into account the actual reliability values for the different experiments.

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All methods perform better on weighted map

Figure 6 shows how Majority,

GenMultiCut and FunctionalFlow

perform on the yeast physical interaction

map, where edges are weighted by

individual experimental reliability.

The baseline performance of Majority

on the unweighted physical interaction

graph is also shown. There is substantial

improvement in predictions using all

three methods when incorporating edges

weighted by reliability.


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Is it useful to provide more information?

We further explored whether the

network analysis algorithms would

perform well when other types of

experimental information are added.

As a proof of principle, we explore the

effect of adding genetic linkages to

the graph (synthetic lethals).

As is evident from the figure, adding

genetic interaction data significantly

improves prediction quality.


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Conclusions

Network analysis algorithm FunctionalFlow provides an effective means for

predicting protein function from protein interaction maps.

The algorithm utilizes indirect network interactions, network topology, network

distances and edges weighted by reliability estimated from multiple data sources.

The simplest methods, such as Majority, perform well if there are enough direct

neighbors with known function.

Simple independence assumptions were made for estimating the reliability of

interactions. Although these work reasonably well, it may be even more beneficial

to use a more sophisticated approach for weight assignment and perform more

complete data integration.

Finally, although the method was applied to baker’s yeast, FunctionalFlow is

likely to be especially useful when analyzing largely uncharacterized proteomes

where computational methods are used to infer protein interaction maps.


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Alternative scheme to combine information from various source: Bayesian network

Knowing the correct overall structures of gene networks will be invaluable for characterizing

the complex roles of individual genes and the interplay between the many systems in a cell.

Deriving gene networks from heterogeneous functional genomics data is often difficult,

because experiments such as microarray analyses of gene expression or systematic protein

interaction mapping measure different aspects of gene or protein associations. E.g.

Affinity purification of proteins analyzed by MS: measures the tendency for proteins to be

components of the same physical complex, although not necessarily to contact each other

directly.

Yeast two-hybrid assays: often indicate direct physical interactions (stable or transient)

between proteins.

Synthetic lethal screens: measure the tendency for genes to compensate for the loss of

other genes.

These analyses range considerably in accuracy, and it is not clear a priori which

measurements are correct.

In spite of these differences, these data sets can, in principle, be computationally integrated,

primarily by the reconstruction of network models of the relations between genes.

Lee, ..., Marcotte, Science 306, 1555 (2004)

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Aim: construct complete network of gene association

Network reconstructions have largely focused on physical protein interaction and so

represent only a subset of biologically important relations.

Aim: construct a more accurate and extensive gene network by considering functional,

rather than physical, associations, realizing that each experiment, whether genetic,

biochemical, or computational, adds evidence linking pairs of genes, with associated error

rates and degree of coverage.

In this framework, gene-gene linkages are probabilistic summaries representing functional

coupling between genes. Only some of the links represent direct protein-protein interactions;

the rest are associations not mediated by physical contact, such as regulatory, genetic, or

metabolic coupling, that, nonetheless, represent functional constraints satisfied by the cell

during the course of the experiments.

Working with probabilistic functional linkages allows many diverse classes of

experiments to be integrated into a single coherent network which enables the linkages

themselves to be more reliably


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Method for integrating functional genomics data

Functional genomics data sets are first

benchmarked for their relative accuracies; these

are used as weights in a probabilistic integration of

the data. Several raw data sets already have

intrinsic scoring schemes, indicated in

parentheses (e.g., CC, correlation coefficients; P,

probabilities, and MI, mutual information scores).

These data are rescored with LLS, then integrated

into an initial network (IntNet). Additional linkages

from the genes’ network contexts (ContextNet) are

then integrated to create the final network

(FinalNet) , with È34,000 linkages between 4681

genes (ConfidentNet) scoring higher than the gold

standard (small-scale assays of protein

interactions). Hierarchical clustering of

ConfidentNet defined 627 modules of functionally

linked genes spanning 3285 genes

(‘‘ModularNet’’), approximating the set of cellular

systems in yeast.Lee, ..., Marcotte, Science 306, 1555 (2004)

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Scoring scheme for linkages

Unified scoring scheme for linkages is based on a Bayesian statistics approach.

Each experiment is evaluated for its ability to reconstruct known gene pathways

and systems by measuring the likelihood that pairs of genes are functionally

linked conditioned on the evidence, calculated as a log likelihood score:

P(L|E) and P(L|E) : frequencies of linkages (L) observed in the given

experiment (E) between annotated genes operating in the same pathway and in

different pathways

P(L) and P(L): the prior expectations (i.e., the total frequency of linkages

between all annotated yeast genes operating in the same pathway and operating

in different pathways).

Scores > 0 indicate that the experiment tends to link genes in the same pathway,

with higher scores indicating more confident linkages.


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Benchmarks

As scoring benchmarks, the method was tested against two primary annotation

references: the Kyoto-based KEGG pathway database and the experimentally

observed yeast protein subcellular locations determined by genome-wide green

fluorescent protein (GFP)–tagging and microscopy.

KEGG scores were used for integrating linkages, with the other benchmark

withheld as an independent test of linkage accuracy.

Cross-validated benchmarks and benchmarks based on the Gene Ontology (GO)

and KOG gene annotations provided comparable results.


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Functional inference from interaction networks

Benchmarked accuracy and extent of functional genomics data sets and the integrated networks. A critical point is the comparable performance of the networks on distinct benchmarks, which assess the tendencies for linked genes to share (A) KEGG pathway annotations or (B) protein subcellular locations. Each x axis indicates the percentage of protein-encoding yeast genes provided with linkages by the plotted data; each y axis indicates relative accuracy, measured as the of the linked genes’ annotations on that benchmark. The gold standards of accuracy (red star) for calibrating the benchmarks are smallscale protein-protein interaction data from DIP. Colored markers indicate experimental linkages; gray markers, computational. The initial integrated network (lower black line), trained using only the KEGG benchmark, has measurably higher accuracy than any individual data set on the subcellular localization benchmark; adding context-inferred linkages in the final network (upper black line) further improves the size and accuracy of the network.


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Functional linkages were first inferred on the basis of genes‘ mRNA coexpression across each of 12 sets of DNA microarray experiments (497 microarray experiments in total), then integrated via a rank-weighted sum of log likelihood scores to create the combined set of coexpression-derived linkages. To construct the initial integrated network (BIntNet,[ Fig. 1), we combined 8 categories of data, including the physical and genetic interaction data sets, mRNA coexpression linkages, functional linkages from literature mining, and computational linkages from two comparative genomics methods, Rosetta stone (gene-fusion) linkages and phylogenetic profiles.


Integrating functional genomics data also allowed discovery of additional relations between genes linked, in turn, to a common set of genes [BContextNet]; these linkages were scored and integrated as above to construct the final gene network (BFinalNet, see figure). The final network has 34,000 linkages at an accuracy comparable to the gold standard small-scale interaction assays, which provides linkages (BConfidentNet) for more than 4681 yeast genes (81% of the yeast proteome). The network is reasonably distinct from networks of physical interacting proteins Ee.g., sharing only È16% of linkages with (Jansen et al.).

The final network shows extensive clustering of genes into modules, evident in the ‘‘clumping’’ (A).

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At an intermediate degree of clustering that maximizes cluster size and functional coherence, 564 (of

627) modules are shown connected by the 950 strongest intermodule linkages.

Module colors and shapes indicate associated functions, as defined by Munich Information Center for

Protein Sequencing (MIPS), with sizes proportional to the number of genes, and connections inversely

proportional to the fraction of genes linking the clusters.


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Adding context-inferred linkages increased clustering of genes, which produced a

highly modular gene network with well-defined subnetworks.

We expected these gene clusters to reflect gene systems and modules. We could

therefore generate a simplified view of the major trends in the network (Fig. 3B) by

clustering genes of ConfidentNet according to their connectivities. Of the 4681

genes, 3285 (70.2%) were grouped into 627 clusters, reflecting the high degree of

modularity.

Genes‘ functions within each cluster are highly coherent, and with 2 to 154 genes

per cluster (ca. 5 genes per cluster on average), the clusters effectively capture

typical gene pathways and/or systems.


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Portions of the final, confident gene network are shown for (C) DNA damage

response and/or repair, where modularity gives rise to gene clusters, indicated by

similar colors, and (D) chromatin remodeling, with several uncharacterized

genes (red labels).

Networks are visualized with Large Graph Layout (LGL).


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A region of the modular network centered on the DNA damage response and

repair systems is shown in Fig. 3C. The network is clearly hierarchical: Individual clusters

represent distinct systems related to DNA damage response and/or repair; these clusters

are in turn connected to modules of cell cycle regulatory genes and chromatin silencing,

functionally linked to the DNA damage response and/or repair system.

One can infer individual genes‘ functions on the basis of linked neighbors. (Comment: this simple scheme can be improved by combining approach with Nabieva et al.)

For example, seven uncharacterized genes are implicated in chromatin remodeling (Fig.

3D). All but 1 of the 18 linkages made by these genes arise from the comparative genomics

analysis or from the network context methods, which represent examples of the insights that

arise only after data integration. Three of the uncharacterized proteins are predicted by

sequence homology to have helicase activity, which is reasonable for a relation to chromatin

remodeling; four of these proteins localize to the nucleus, further supporting their

association. After this network‘s construction, one gene, VID21, was implicated in chromatin

modification as a component of the NuA4 histone acetyl transferase.


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Experimental verification of one prediction

The function of the RNA helicase PRP43, previously thought to be involved only in

pre-mRNA splicing and implicated in lariatintron release from the spliceosome, is

also clarified in the network.

PRP43 is linked most strongly to genes of ribosome biogenesis and rRNA

processing. The tightest links are to ERB1, RRB1, NUG1, LHP1, and PWP1, the

first three of which are confirmed ribosome biogenesis factors. These links derive

only from the coexpression and context methods; data integration is therefore

critical.

The association of PRP43 with ribosome biogenesis has now been experimentally

validated: the growth defect conferred by a PRP43 conditional lethal mutation

corresponds to a rapid and major defect in rRNA processing.

These data indicate that rRNA processing is the essential function of PRP43, and

it joins a growing group of RNA helicases with two or more distinct functions.


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Summary

The probabilistic gene network integrates evidence from diverse sources to reconstruct an

accurate network, by estimating the functional coupling among yeast genes, and provides a

view of the relations between yeast proteins distinct from their physical interactions.

The application of this strategy to other organisms, such as to the human genome, is

conceptually straightforward:

(i) assemble benchmarks for measuring the accuracy of linkages between human genes

based on properties shared among genes in the same systems,

(ii) assemble gold standard sets of highly accurate interactions for calibrating the

benchmarks, and

(iii) benchmark functional genomics data for their ability to correctly link human genes, then

integrate the data as described.

New data can be incorporated in a simple manner serving to reinforce the correct linkages.

Thus, the gene network will ultimately converge by successive approximation to the

correct structure simply by continued addition of functional genomics data in this

framework.


5. Lecture SS 20005Cell Simulations1 V5: Functional inference from interaction networks.

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