SMD data sets used for co-expression links (total 500 experiments)

<p>N: the number of experiments in the set</p><p>R: minimum absolute value of log base 2 ratio of expression between treated experiment and control</p><p>M: minimum number of microarray experiments that exceed the R threshold</p

Illustrative examples of network dichotomy.

<p>Two broad classes of networks naturally occur in real world settings based on whether interactions between entities are intrinsically pairwise (left panel) or based on groups (right panel). Examples in human social networks might include (a) dating or sexual relationships, and (b) board memberships or co-authorships of scientific papers. (c and d) indicate small toy examples for these two types of human social networks, each composed of 11 individuals (nodes) organized into three groups (indicated by node colors). Edges in (c) indicate direct contacts between persons; edges in (d) indicate participation in shared tasks. Numbers in (c) denote the degree connectivity (count of associated edges) and clustering coefficient [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121248#pone.0121248.ref018" target="_blank">18</a>] for each node. Individuals with high node degree are marked as squares. In spite of their simplicity, the two toy networks show distinct topological features. Their cumulative probability distributions of nodes with ><i>k</i> degree (<i>Pc</i>(<i>k</i>)) differ; (e) is scale-free, (f) is single- or broad-scale. They differ in being (g) disassortative or (h) assortative networks, as seen by heat-map representations of the enrichment for connections between nodes of varying degrees or measured by Pearson correlation coefficient (<i>r</i>) of the degrees at either ends of an edge. Finally, they exhibit (i) hierarchical or (j) nonhierarchical network topologies, as judged by their relationships between node degree connectivity (<i>k</i>) and node clustering coefficients (<i>C</i>(<i>k</i>)).</p

YeastNet v. 2 shows improved correlation between gene centrality and lethality.

<p>Each plot presents the correlation (for a given network) between network centrality, calculated as the number of interactions per gene normalized by the maximum observed value, versus the essentiality of the genes, calculated as the fraction of essential yeast genes <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000988#pone.0000988-Giaever1" target="_blank">[27]</a> for each successive bin (open circle) of 100 genes ranked by decreasing degree centrality. (A) shows the trend for a high quality protein-protein physical interaction network derived from DIP <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000988#pone.0000988-Xenarios1" target="_blank">[30]</a> and bioGRID <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000988#pone.0000988-Reguly1" target="_blank">[29]</a>, (B) shows the trend for YeastNet version 1 (34,000 most confident linkages only), and (C) shows the trend for YeastNet version 2. For both functional networks, degree centrality is weighted by the interaction LLS scores (<i>i.e</i>., calculated as the sum of LLS scores for a gene, divided by the maximum sum of LLS scores observed in the network). The degree of correlation is measured as the Spearman rank correlation coefficient (<i>r</i>_s).</p

Dichotomy of the same entities by alternative network representations.

<p>Alternate representations of the same network can lead to different topologies, especially for networks with natural hierarchical organization. We illustrate this tendency for (a) the Internet and (b) the yeast cell proteome. Each can be modeled by networks at two different granularities, representing nodes either as upper level components (internet domains or protein processes) or lower level components (internet routers or individual proteins). For the internet, previous Internet mapping studies provide both a router-level network and a domain-level network [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121248#pone.0121248.ref024" target="_blank">24</a>]; each domain is composed on multiple routers, and domains are connected <i>via</i> between-domain routers. For the protein network, we defined protein processes by hierarchically clustering proteins based on their pairwise interactions as in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121248#pone.0121248.ref012" target="_blank">12</a>]. A total of 333 biological processes were defined and connections between processes were defined based on pairwise interactions between proteins within each process. The networks’ hierarchical structure was analyzed and plotted as in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121248#pone.0121248.g002" target="_blank">Fig. 2</a></b>, marking the mean clustering coefficient for each entire network as a horizontal solid line in the plot. The non-hierarchical router and protein networks generally exhibited clustering coefficients near this average regardless of node degree, although for the Internet router-level network, routers with >300 connections showed exceptionally low clustering coefficients primarily due to a small number of between-domain routers located at a few top-level domains of the Internet.</p

Assigning confidence scores to physical or genetic interactions.

<p>Performance of the hypergeometric probabilistic score is shown for gene functional associations inferred from (A) protein-protein physical interactions measured by the high-throughput yeast two hybrid (Y2H) screen of Ito <i>et al</i>. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000988#pone.0000988-Ito1" target="_blank">[20]</a>, (B) affinity-purified complexes identified by mass spectrometry by Gavin <i>et al</i>. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000988#pone.0000988-Gavin1" target="_blank">[52]</a>, and (C) genetic interactions <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000988#pone.0000988-Mewes1" target="_blank">[43]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000988#pone.0000988-Tong2" target="_blank">[74]</a>. Performance with the probability score is measured cumulatively for each successive bin of 200 interactions (A–C, red filled triangles), ranked by probability score. Recall and precision are calculated using the reference linkages derived from Gene Ontology “biological process” annotation masking the term “protein biosynthesis”. The Y2H core model described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000988#pone.0000988-Ito1" target="_blank">[20]</a> (A, filled circle) is more precise than the complete data set (A, open circle), but with reduced recall. Similarly, two different ways of inferring binary linkages from mass spectrometry-derived protein complexes <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000988#pone.0000988-Bader2" target="_blank">[22]</a>—the spoke (B, filled circle) and matrix models (B, open circle)—show differing trade-offs between precision and recall. The set of binary genetic interactions (C, open circle) shows very low precision for functional inferences, although the false positive rate of genetic interactions is generally perceived to be low; in contrast, the hypergeometric probability identifies a functionally informative subset of linkages. In general, the hypergeometric probability scores provide an excellent ranking of interactions in each of the data sets consistent with the linkages' functional informativeness.</p

Auto-processing is essential for the functions of MYRF.

<p>(A) The transcriptional activity of various MYRF constructs was estimated by their ability to activate the transcription of <i>Edn2</i> in HeLa cells. Values are means ± SEM. (B) Examples of transfected CG4 cells that matured to express MBP or O1. (C) Quantification of the proportion of transfected CG4 cells expressing MBP or O1. Values are means ± SEM. *<i>p</i><0.05, **<i>p</i><0.01, and ***<i>p</i><0.001. Scale bar, 10 µm.</p

The effect of functionally biased Gene Ontology annotation on network training.

<p>(A) Frequency histograms of the usage of 1,067 Gene Ontology “biological process” annotations, ranked by the number of genes annotated with each term (black bars) and by the number of reference linkages derived using that term (white bars). Functional annotation is highly biased towards genes with the term “protein biosynthesis”. This functional bias becomes more severe in the reference linkages, given the combinatorial increase after linking all genes sharing a given term. As a result, linkages among protein biosynthesis genes compose >27% of total reference linkages. By contrast, the second most frequent term accounts for <5% of total reference linkages. (B) The likelihood of functional association between genes on the basis of the co-expression of their mRNAs across DNA microarray experiments (here, following heat-shock <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000988#pone.0000988-Gasch1" target="_blank">[19]</a>) is significantly affected by the dominant reference term “protein biosynthesis”. For example, for the 1,000 most strongly co-expressed gene pairs, the likelihood of functional association between co-expressed genes is ∼30 fold higher than random chance (LLS∼3.4) (empty circles), but drops to ∼6 fold (LLS∼1.8) after masking the term “protein biosynthesis” in the reference set (filled circles). Thus, the high likelihood score from the biased reference set cannot be generalized to other functions. The black and red lines indicate sigmoid curve fits to the unbiased and biased reference analyses, respectively.</p

Dichotomy of real-world networks.

<p>Topological analysis of three real world networks (<b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121248#pone.0121248.s006" target="_blank">S1 Table</a></b>). We analyzed contact-centric networks (indicated by the letter C) and task-centric networks (indicated by the letter T) for their (a, d, g) degree distribution, (b, e, h) assortativity, and (c, f, i) hierarchical structure. We tested two different human social networks, a contact-centric online dating network (Dating) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121248#pone.0121248.ref014" target="_blank">14</a>] and a task-centric network of shared membership on US company boards of directors (Director board) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121248#pone.0121248.ref016" target="_blank">16</a>]. Similarly, we analyzed the Internet at two different levels, the router-level (Router) and domain level (Domain). The router-level Internet is task-centric and the domain-level Internet is contact-centric. Finally, we analyzed two alternate protein networks, testing pairwise protein interactions (CCSB-YI1) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121248#pone.0121248.ref022" target="_blank">22</a>] and functional protein interactions (YeastNet core) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121248#pone.0121248.ref023" target="_blank">23</a>], as examples of contact-centric and task-centric protein networks, respectively. We measured the cumulative probability of nodes with degree ><i>k</i> (<i>Pc</i>(<i>k</i>)) for the full range of node degree [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121248#pone.0121248.ref019" target="_blank">19</a>] to test scale-freeness of networks. We measured hierarchical connectivity by testing for decreasing clustering coefficients (<i>C</i>(<i>k</i>)) as a function of increasing node degree (<i>k</i>). Network assortativities were visualized as heat maps of the enrichment of connections between various ranges of degrees compared to permuted networks [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121248#pone.0121248.ref009" target="_blank">9</a>]; red indicates enrichment and blue indicates suppression of connectivity. In every case, real-world networks showed topological properties consistent with being either contact- or task-centric, as appropriate.</p

The N-terminal trimer is formed by the ICA domain and enters the nucleus.

<p>(A) Predicted sequence features of MYRF and sequence diagrams of various MYRF constructs used for experiments. (B) Western blots showing co-immunoprecipitation results for the MYRF constructs. “Input” was incubated with FLAG antibody-coated beads and then spun down to separate “Sup” from “Bead” fractions. The failure of MYRF-1:577 to homo-oligomerize demonstrated the importance of the ICA domain for the N-terminal trimer formation. (C) When the NLSs (NLS1 and NLS2) were deleted, the nuclear translocation of the N-terminal trimer was partially blocked. Scale bar, 20 µm.</p

Full-length MYRF is generated as a membrane protein.

<p>(A) Predicted sequence features of MYRF and sequence diagrams of various MYRF constructs used for IF microscopy. Stars in blue indicate predicted NLSs at K₂₄₅KRK₂₄₈ and K₄₈₂KGK₄₈₅. (B) IF images of GFP-MYRF, MYRF-GFP, MYRFΔTM-GFP, and MYRF-1:756-GFP in HeLa cells. (C) IF image of 3F-MYRF-GFP in HeLa cells. Scale bar, 10 µm.</p