Interactome and Gene Ontology provide congruent yet subtly different views of a eukaryotic cell

15 pages, 6 figures.-- 19604360 [PubMed]BACKGROUND: The characterization of the global functional structure of a cell is a major goal in bioinformatics and systems biology. Gene Ontology (GO) and the protein-protein interaction network offer alternative views of that structure. RESULTS: This study presents a comparison of the global structures of the Gene Ontology and the interactome of Saccharomyces cerevisiae. Sensitive, unsupervised methods of clustering applied to a large fraction of the proteome led to establish a GO-interactome correlation value of +0.47 for a general dataset that contains both high and low-confidence interactions and +0.58 for a smaller, high-confidence dataset. CONCLUSION: The structures of the yeast cell deduced from GO and interactome are substantially congruent. However, some significant differences were also detected, which may contribute to a better understanding of cell function and also to a refinement of the current ontologiesResearch supported by grant BIO2008-05067 (Programa Nacional de Biotecnología; Ministerio de Ciencia e Innovación. Spain), awarded to IM. AM was a FPI fellow from Ministerio de Educación y Ciencia (Spain).Peer reviewe

Jerarca: Efficient Analysis of Complex Networks Using Hierarchical Clustering

Background: How to extract useful information from complex biological networks is a major goal in many fields, especially in genomics and proteomics. We have shown in several works that iterative hierarchical clustering, as implemented in the UVCluster program, is a powerful tool to analyze many of those networks. However, the amount of computation time required to perform UVCluster analyses imposed significant limitations to its use. Methodology/Principal Findings: We describe the suite Jerarca, designed to efficiently convert networks of interacting units into dendrograms by means of iterative hierarchical clustering. Jerarca is divided into three main sections. First, weighted distances among units are computed using up to three different approaches: a more efficient version of UVCluster and two new, related algorithms called RCluster and SCluster. Second, Jerarca builds dendrograms based on those distances, using well-known phylogenetic algorithms, such as UPGMA or Neighbor-Joining. Finally, Jerarca provides optimal partitions of the trees using statistical criteria based on the distribution of intra- and intercluster connections. Outputs compatible with the phylogenetic software MEGA and the Cytoscape package are generated, allowing the results to be easily visualized. Conclusions/Significance: The four main advantages of Jerarca in respect to UVCluster are: 1) Improved speed of a novel UVCluster algorithm; 2) Additional, alternative strategies to perform iterative hierarchical clustering; 3) Automatic evaluatio

Semantic integration to identify overlapping functional modules in protein interaction networks

<p>Abstract</p> <p>Background</p> <p>The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms.</p> <p>Results</p> <p>We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches.</p> <p>Conclusion</p> <p>The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.</p

Diffusion Model Based Spectral Clustering for Protein-Protein Interaction Networks

BACKGROUND: A goal of systems biology is to analyze large-scale molecular networks including gene expressions and protein-protein interactions, revealing the relationships between network structures and their biological functions. Dividing a protein-protein interaction (PPI) network into naturally grouped parts is an essential way to investigate the relationship between topology of networks and their functions. However, clear modular decomposition is often hard due to the heterogeneous or scale-free properties of PPI networks. METHODOLOGY/PRINCIPAL FINDINGS: To address this problem, we propose a diffusion model-based spectral clustering algorithm, which analytically solves the cluster structure of PPI networks as a problem of random walks in the diffusion process in them. To cope with the heterogeneity of the networks, the power factor is introduced to adjust the diffusion matrix by weighting the transition (adjacency) matrix according to a node degree matrix. This algorithm is named adjustable diffusion matrix-based spectral clustering (ADMSC). To demonstrate the feasibility of ADMSC, we apply it to decomposition of a yeast PPI network, identifying biologically significant clusters with approximately equal size. Compared with other established algorithms, ADMSC facilitates clear and fast decomposition of PPI networks. CONCLUSIONS/SIGNIFICANCE: ADMSC is proposed by introducing the power factor that adjusts the diffusion matrix to the heterogeneity of the PPI networks. ADMSC effectively partitions PPI networks into biologically significant clusters with almost equal sizes, while being very fast, robust and appealing simple

Evaluation of clustering algorithms for protein-protein interaction networks

BACKGROUND: Protein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism). In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods (mass spectrometry, two-hybrid methods, genetic studies). High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions. The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Super Paramagnetic Clustering (SPC), and Molecular Complex Detection (MCODE). RESULTS: A test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions. Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes. We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values. We also evaluated their robustness to alterations of the test graph. We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes. CONCLUSION: This analysis shows that MCL is remarkably robust to graph alterations. In the tests of robustness, RNSC is more sensitive to edge deletion but less sensitive to the use of suboptimal parameter values. The other two algorithms are clearly weaker under most conditions. The analysis of high-throughput data supports the superiority of MCL for the extraction of complexes from interaction networks

Measurement of the Bs0→J/ψηB_{s}^{0} \rightarrow J/\psi \eta lifetime

Using a data set corresponding to an integrated luminosity of $3 fb^{-1}$, collected by the LHCb experiment in $pp$ collisions at centre-of-mass energies of 7 and 8 TeV, the effective lifetime in the $B^0_s \rightarrow J/\psi \eta$ decay mode, $\tau_{\textrm{eff}}$, is measured to be $\tau_{\textrm{eff}} = 1.479 \pm 0.034~\textrm{(stat)} \pm 0.011 ~\textrm{(syst)}$ ps. Assuming $CP$ conservation, $\tau_{\textrm{eff}}$ corresponds to the lifetime of the light $B_s^0$ mass eigenstate. This is the first measurement of the effective lifetime in this decay mode.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://lhcbproject.web.cern.ch/lhcbproject/Publications/LHCbProjectPublic/LHCb-PAPER-2016-017.htm

Bose-Einstein correlations of same-sign charged pions in the forward region in pp collisions at √s=7 TeV

Bose-Einstein correlations of same-sign charged pions, produced in protonproton collisions at a 7 TeV centre-of-mass energy, are studied using a data sample collected by the LHCb experiment. The signature for Bose-Einstein correlations is observed in the form of an enhancement of pairs of like-sign charged pions with small four-momentum difference squared. The charged-particle multiplicity dependence of the Bose-Einstein correlation parameters describing the correlation strength and the size of the emitting source is investigated, determining both the correlation radius and the chaoticity parameter. The measured correlation radius is found to increase as a function of increasing charged-particle multiplicity, while the chaoticity parameter is seen to decreas

Study of charmonium production in b -hadron decays and first evidence for the decay Bs0

Using decays to φ-meson pairs, the inclusive production of charmonium states in b-hadron decays is studied with pp collision data corresponding to an integrated luminosity of 3.0 fb−1, collected by the LHCb experiment at centre-of-mass energies of 7 and 8 TeV. Denoting byBC ≡ B(b → C X) × B(C → φφ) the inclusive branching fraction of a b hadron to a charmonium state C that decays into a pair of φ mesons, ratios RC1C2 ≡ BC1 /BC2 are determined as Rχc0ηc(1S) = 0.147 ± 0.023 ± 0.011, Rχc1ηc(1S) =0.073 ± 0.016 ± 0.006, Rχc2ηc(1S) = 0.081 ± 0.013 ± 0.005,Rχc1 χc0 = 0.50 ± 0.11 ± 0.01, Rχc2 χc0 = 0.56 ± 0.10 ± 0.01and Rηc(2S)ηc(1S) = 0.040 ± 0.011 ± 0.004. Here and below the first uncertainties are statistical and the second systematic.Upper limits at 90% confidence level for the inclusive production of X(3872), X(3915) and χc2(2P) states are obtained as RX(3872)χc1 < 0.34, RX(3915)χc0 < 0.12 andRχc2(2P)χc2 < 0.16. Differential cross-sections as a function of transverse momentum are measured for the ηc(1S) andχc states. The branching fraction of the decay B0s → φφφ is measured for the first time, B(B0s → φφφ) = (2.15±0.54±0.28±0.21B)×10−6. Here the third uncertainty is due to the branching fraction of the decay B0s → φφ, which is used for normalization. No evidence for intermediate resonances is seen. A preferentially transverse φ polarization is observed.The measurements allow the determination of the ratio of the branching fractions for the ηc(1S) decays to φφ and p p asB(ηc(1S)→ φφ)/B(ηc(1S)→ p p) = 1.79 ± 0.14 ± 0.32

Model-independent evidence for J/ψpJ/\psi p contributions to Λb0→J/ψpK−\Lambda_b^0\to J/\psi p K^- decays

The data sample of $\Lambda_b^0\to J/\psi p K^-$ decays acquired with the LHCb detector from 7 and 8~TeV $pp$ collisions, corresponding to an integrated luminosity of 3 fb$^{-1}$, is inspected for the presence of $J/\psi p$ or $J/\psi K^-$ contributions with minimal assumptions about $K^- p$ contributions. It is demonstrated at more than 9 standard deviations that $\Lambda_b^0\to J/\psi p K^-$ decays cannot be described with $K^- p$ contributions alone, and that $J/\psi p$ contributions play a dominant role in this incompatibility. These model-independent results support the previously obtained model-dependent evidence for $P_c^+\to J/\psi p$ charmonium-pentaquark states in the same data sample.Comment: 21 pages, 12 figures (including the supplemental section added at the end

Study of J /ψ production in Jets

The production of J/ψ mesons in jets is studied in the forward region of proton-proton collisions using data collected with the LHCb detector at a center-of-mass energy of 13 TeV. The fraction of the jet transverse momentum carried by the J/ψ meson, z(J/ψ)≡pT(J/ψ)/pT(jet), is measured using jets with pT(jet)>20 GeV in the pseudorapidity range 2.5<η(jet)<4.0. The observed z(J/ψ)distribution for J/ψ mesons produced in b-hadron decays is consistent with expectations. However, the results for prompt J/ψ production do not agree with predictions based on fixed-order nonrelativistic QCD. This is the first measurement of the pT fraction carried by prompt J/ψ mesons in jets at any experiment