The frequency distribution of core generic concepts shared between PPIs (open bars) is more uniform than is the distribution for randomly chosen protein pairs (grey solid bars).

<p>Since the core generic network exists of 735 concepts the number of shared concepts between two profiles can be maximum 735.</p

The retrieval power of individual generic concepts.

<p>Plotted are the AuC values for 735 individual concepts when retrieving PPI (x-axis) and gene-disease associations (y-axis).</p

The impact of three different filtering methods on the retrieval performance of the weighted semantic network.

<p>A–C, PPI retrieval performance (true positive rate or recall) is measured as the Area under the ROC Curve (ordinate). Panels D–F retrieval performance for known gene-disease associations. An AuC value of 0.5 indicates no retrieval power above random expectations. The weighted semantic network is filtered by incrementally removing generic information (heavy curve) from right to left or by incrementally removing specific information (inverse filtering, light curve) from left to right. Filter Threshold is indicated on the abscissa. Panels A, B, D, and E represent node filtering approaches while panel C and F represent edge filtering (see Method section for details). The red circle in panel A indicates the PPI retrieval performance (0.83) for a network where 99.52% of the nodes have been removed (i.e., all concepts occurring in 100,000 abstracts or fewer).</p

The 30 highest ranking concepts in the thesaurus by the number of abstracts in which they appear in the MEDLINE text corpus (1980–2009).

<p>These top-ranking concepts appear to be generic.</p

The 20 highest and 20 lowest ranking shared concepts between the proteins CAPN3 and PARVB with the percent contribution of each concept to the overall match score.

<p>The contribution is calculated as a percentage of an individual product between 2 concepts divided by the inner product (which is the sum of all individual products). This inferred association from text-mining was subsequently validated as a physical protein-protein interaction <i>in vitro</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0078665#pone.0078665-VanHaagen2" target="_blank">[7]</a>.</p

Concepts in the thesaurus ranked by the number of abstracts in which they appear in the MEDLINE text corpus.

<p>Generic concepts appear in a large number of abstracts while specific concepts, such as proteins (red points below log 5) tend to occur in a smaller number of abstracts. Not plotted are the 308,656 concepts having no occurrence in MEDLINE abstracts.</p

Gene expression datasets.

<p>Gene expression datasets.</p

Flowchart of analysis.

<p>Marks in italics - databases, programs and analysis types. Red marks - tables (“T”), figures (“F”) and supplementary tables (“S”). <b>A.</b> Search in the GEO database: selection of data pertaining to brain, airways and reproductive tracts. <b>B.</b> Construction of coexpression networks in each dataset using WGCNA algorithm: identification of coexpression modules. <b>C.</b> Generation of a consensus ciliary module: identification of genes shared by the tissues. D to G: validation and characterization of genes in the consensus signature. <b>D.</b> Discrimination between known, candidate and novel ciliary genes (CilDB and PubMed databases). <b>E.</b> Determination which genes from the consensus signature are up-regulated in ‘ciliated’ tissues compared to ‘non-ciliated’ tissues (GEO database). <b>F.</b> Determination which proteins from the consensus signature have characteristic patterns of subcellular localization in ciliated cells (Protein Atlas database). <b>G.</b> Linking genes to cellular functions and human diseases using literature mining (Anni 2.1 program). <b>H.</b> Differential coexpression analysis: identification of genes which represent members of the ciliary module in only a subset of ciliated tissues.</p

Exploring the Transcriptome of Ciliated Cells Using In Silico Dissection of Human Tissues

<div><p>Cilia are cell organelles that play important roles in cell motility, sensory and developmental functions and are involved in a range of human diseases, known as ciliopathies. Here, we search for novel human genes related to cilia using a strategy that exploits the previously reported tendency of cell type-specific genes to be coexpressed in the transcriptome of complex tissues. Gene coexpression networks were constructed using the noise-resistant WGCNA algorithm in 12 publicly available microarray datasets from human tissues rich in motile cilia: airways, fallopian tubes and brain. A cilia-related coexpression module was detected in 10 out of the 12 datasets. A consensus analysis of this module's gene composition recapitulated 297 known and predicted 74 novel cilia-related genes. 82% of the novel candidates were supported by tissue-specificity expression data from GEO and/or proteomic data from the Human Protein Atlas. The novel findings included a set of genes (DCDC2, DYX1C1, KIAA0319) related to a neurological disease dyslexia suggesting their potential involvement in ciliary functions. Furthermore, we searched for differences in gene composition of the ciliary module between the tissues. A multidrug-and-toxin extrusion transporter MATE2 (SLC47A2) was found as a brain-specific central gene in the ciliary module. We confirm the localization of MATE2 in cilia by immunofluorescence staining using MDCK cells as a model. While MATE2 has previously gained attention as a pharmacologically relevant transporter, its potential relation to cilia is suggested for the first time. Taken together, our large-scale analysis of gene coexpression networks identifies novel genes related to human cell cilia.</p> </div

Heatmaps of consensus ciliary signature in 10 contributing datasets.

<p>Red - high, green - low level of expression. Columns – samples, rows – genes. Samples were clustered separately in each dataset. Genes were ordered by the number of datasets in which they belonged to the ciliary module: the gene order is constant across the datasets. Genes, that lacked measurements in a subset of the experiments, were excluded from the heatmaps.</p