Construction and Deciphering of Human Phosphorylation-Mediated Signaling Transduction Networks

Protein phosphorylation is the most abundant reversible covalent modification. Human protein kinases participate in almost all biological pathways, and approximately half of the kinases are associated with disease. PhoSigNet was designed to store and display human phosphorylation-mediated signal transduction networks, with additional information related to cancer. It contains 11 976 experimentally validated directed edges and 216 871 phosphorylation sites. Moreover, 3491 differentially expressed proteins in human cancer from dbDEPC, 18 907 human cancer variation sites from CanProVar, and 388 hyperphosphorylation sites from PhosphoSitePlus were collected as annotation information. Compared with other phosphorylation-related databases, PhoSigNet not only takes the kinase–substrate regulatory relationship pairs into account, but also extends regulatory relationships up- and downstream (e.g., from ligand to receptor, from G protein to kinase, and from transcription factor to targets). Furthermore, PhoSigNet allows the user to investigate the impact of phosphorylation modifications on cancer. By using one set of in-house time series phosphoproteomics data, the reconstruction of a conditional and dynamic phosphorylation-mediated signaling network was exemplified. We expect PhoSigNet to be a useful database and analysis platform benefiting both proteomics and cancer studies

Construction and Deciphering of Human Phosphorylation-Mediated Signaling Transduction Networks

Protein phosphorylation is the most abundant reversible covalent modification. Human protein kinases participate in almost all biological pathways, and approximately half of the kinases are associated with disease. PhoSigNet was designed to store and display human phosphorylation-mediated signal transduction networks, with additional information related to cancer. It contains 11 976 experimentally validated directed edges and 216 871 phosphorylation sites. Moreover, 3491 differentially expressed proteins in human cancer from dbDEPC, 18 907 human cancer variation sites from CanProVar, and 388 hyperphosphorylation sites from PhosphoSitePlus were collected as annotation information. Compared with other phosphorylation-related databases, PhoSigNet not only takes the kinase–substrate regulatory relationship pairs into account, but also extends regulatory relationships up- and downstream (e.g., from ligand to receptor, from G protein to kinase, and from transcription factor to targets). Furthermore, PhoSigNet allows the user to investigate the impact of phosphorylation modifications on cancer. By using one set of in-house time series phosphoproteomics data, the reconstruction of a conditional and dynamic phosphorylation-mediated signaling network was exemplified. We expect PhoSigNet to be a useful database and analysis platform benefiting both proteomics and cancer studies

Construction and Deciphering of Human Phosphorylation-Mediated Signaling Transduction Networks

Protein phosphorylation is the most abundant reversible covalent modification. Human protein kinases participate in almost all biological pathways, and approximately half of the kinases are associated with disease. PhoSigNet was designed to store and display human phosphorylation-mediated signal transduction networks, with additional information related to cancer. It contains 11 976 experimentally validated directed edges and 216 871 phosphorylation sites. Moreover, 3491 differentially expressed proteins in human cancer from dbDEPC, 18 907 human cancer variation sites from CanProVar, and 388 hyperphosphorylation sites from PhosphoSitePlus were collected as annotation information. Compared with other phosphorylation-related databases, PhoSigNet not only takes the kinase–substrate regulatory relationship pairs into account, but also extends regulatory relationships up- and downstream (e.g., from ligand to receptor, from G protein to kinase, and from transcription factor to targets). Furthermore, PhoSigNet allows the user to investigate the impact of phosphorylation modifications on cancer. By using one set of in-house time series phosphoproteomics data, the reconstruction of a conditional and dynamic phosphorylation-mediated signaling network was exemplified. We expect PhoSigNet to be a useful database and analysis platform benefiting both proteomics and cancer studies

Functional Classifications from GO, Focused on Plant-Specific Categories Outlined by Gramene

<p>(A) compares predicted genes from <i>Arabidopsis</i> and Beijing <i>indica</i>. (B) compares predicted genes from Beijing <i>indica</i> with nr-KOME cDNAs. We ignore categories with less than 0.1% of the genes.</p

A Region on Beijing <i>indica</i> Chromosome 2, Showing Three Gene Islands Separated by Two Intergenic Repeat Clusters of High 20-mer Copy Number

<p>Transposable elements identified by RepeatMasker are classified based on the nomenclature of <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030038#st002" target="_blank">Table S2</a>. Depicted genes include both nr-KOME cDNAs and FGENESH predictions.</p

A View of All Duplications Found on Rice Chromosome 2

<p>In contrast to <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030038#pbio-0030038-g006" target="_blank">Figure 6</a>, where we featured those cDNAs with one and only one TBlastN homolog, here we show all detectable TBlastN homologs, up to a maximum of 1,000 per cDNA.</p

Basic Algorithm for Construction of Scaffolds and Super-Scaffolds

<p>We start with the smallest plasmids and progressively work our way up to the largest BACs. Only links with two or more pieces of supporting evidence are made. These include 34,190 “anchor points” constructed from a comparison of <i>indica</i> and <i>japonica</i>. Each anchor is a series of high-quality BlastN hits (typically 98.5% identity) put together by a dynamic programming algorithm that allows for small gaps to accommodate the polymorphic intergenic repeats. Typical anchor points contain four BlastN hits at a total size of 9 kb (including gaps). Notice how in the beginning <i>indica</i> and <i>japonica</i> are processed separately, to construct what we called scaffolds. Only at the end do we use data from one subspecies to link scaffolds in the other subspecies, and these are what we called super-scaffolds.</p

Graphical View of All Duplicated Segments

<p>The 12 chromosomes are depicted along the perimeter of a circle, not in order but slightly rearranged so as to untangle the connections between segments. Overall, we cover 65.7% of the genome.</p

Distribution of Substitutions per Silent Site (Ks) for Homolog Pairs in Segmental, Tandem, and Background Duplications

<p>In (A), contributions from the recent segmental duplication on Chromosomes 11 and 12 are colored in red. The tandem duplication data are shown on two different scales, one to emphasize the magnitude of the zero peak (B) and another to highlight the exponential decay (C). Background duplications are shown in (D).</p

A Sample Bioverse-Predicted Interaction Network for Defense Proteins and Their Direct Neighbors

<p>The symbols are colored to indicate some of the major GO categories under “molecular function.” We draw a cross over the symbol for an NH gene. Rectangles indicate proteins that are manually classified as being R-genes. They appear on genes that are not colored as defense, because some genes have multiple functions, not because of an annotation error. The white circles with green outline are unannotated genes that might also belong to this network, at a lower confidence.</p