Integration of gene expression data with prior knowledge for network analysis and validation

<p>Abstract</p> <p>Background</p> <p>Reconstruction of protein-protein interaction or metabolic networks based on expression data often involves in silico predictions, while on the other hand, there are unspecific networks of in vivo interactions derived from knowledge bases.</p> <p>We analyze networks designed to come as close as possible to data measured in vivo, both with respect to the set of nodes which were taken to be expressed in experiment as well as with respect to the interactions between them which were taken from manually curated databases</p> <p>Results</p> <p>A signaling network derived from the TRANSPATH database and a metabolic network derived from KEGG LIGAND are each filtered onto expression data from breast cancer (SAGE) considering different levels of restrictiveness in edge and vertex selection.</p> <p>We perform several validation steps, in particular we define pathway over-representation tests based on refined null models to recover functional modules. The prominent role of the spindle checkpoint-related pathways in breast cancer is exhibited. High-ranking key nodes cluster in functional groups retrieved from literature. Results are consistent between several functional and topological analyses and between signaling and metabolic aspects.</p> <p>Conclusions</p> <p>This construction involved as a crucial step the passage to a mammalian protein identifier format as well as to a reaction-based semantics of metabolism. This yielded good connectivity but also led to the need to perform benchmark tests to exclude loss of essential information. Such validation, albeit tedious due to limitations of existing methods, turned out to be informative, and in particular provided biological insights as well as information on the degrees of coherence of the networks despite fragmentation of experimental data.</p> <p>Key node analysis exploited the networks for potentially interesting proteins in view of drug target prediction.</p

Search for strange-pentaquark production in e(+)e(-) annihilation at root s=10.58 GeV

We search for strange-pentaquark states that have been previously reported by other experiments-the Theta(1540)(+), Xi(5)(1860)(--), and Xi(5)(1860)(0)-in 123 fb(-1) of data recorded with the BABAR detector at the PEP-II e(+)e(-) storage ring. We find no evidence for these states and set 95% confidence level upper limits on the number of Theta(1540)(+) and Xi(5)(1860)(--) pentaquarks produced per e(+)e(-) annihilation into q (q) over bar and per Upsilon(4S) decay. For q (q) over bar events the Theta(1540)(+) [Xi(5)(1860)(--)] limit is about 8 [4] times lower than the rates measured for ordinary baryons of similar mass

Measurements of branching fractions and dalitz distributions for B-0 ->(DK0)-K-(*)+/-pi(-/+) decays

We present measurments of the branching fractions for the three-body decays B-0 -> D((*) -/+)K(0)pi(+/-) and their resonant submodes B0 -> D(*)K-/+*(+/-) usinga sample of approximately 88 x 10(6) B (B) over bar pairs collected by the BABER detector at the SLAC PEP-II assymetric energy storage ring. We measure: B(B-0-> D(-/+)K(0)pi(+/-)) = (4.9 +/- 0.7(stat) +/- 0.5(syst)) x 10(-4), B(B-0 -> D*(-/+)K(0)pi(+/-)) = (3.0 +/- 0.7(stat) +/- 0.3(syst)) x 10(-4), B(B-0 -> D-/+K*(+/-)) = (4.6 +/- 0.6(stat) +/- 0.5(syst)) x 10(-4), B(B-0 -> D*K-/+*(+/-) = (3.2 +/- 0.6(stat) +/- 0.3(syst)) x 10(-4). From these measurements we determine the fractions of resonant events to be f(B0 -> D+/-K*(-/+)) = 0.63 +/- 0.08(stat) +/- 0.04(syst) and f(B-0 -> D*K-/+*(+/-)) = 0.72 +/- 0.14(stat) +/- 0.05(syst)

Sperimentazione - I risultati della rete varietale 2013. Sorgo da granella, i migliori ibridi nelle prove nazionali

Nel 30° anno di prove della rete di confronto l’andamento pluviometrico favorevole ha permesso a molti genotipi il raggiungimento di buone produzioni areiche e qualitative della granell

Fecal microbiome analysis as a diagnostic test for diverticulitis

Disease-specific variations in intestinal microbiome composition have been found for a number of intestinal disorders, but little is known about diverticulitis. The purpose of this study was to compare the fecal microbiota of diverticulitis patients with control subjects from a general gastroenterological practice and to investigate the feasibility of predictive diagnostics based on complex microbiota data. Thirty-one patients with computed tomography (CT)-proven left-sided uncomplicated acute diverticulitis were included and compared with 25 control subjects evaluated for a range of gastrointestinal indications. A high-throughput polymerase chain reaction (PCR)-based profiling technique (IS-pro) was performed on DNA isolates from baseline fecal samples. Differences in bacterial phylum abundance and diversity (Shannon index) of the resulting profiles were assessed by conventional statistics. Dissimilarity in microbiome composition was analyzed with principal coordinate analysis (PCoA) based on cosine distance measures. To develop a prediction model for the diagnosis of diverticulitis, we used cross-validated partial least squares discriminant analysis (PLS-DA). Firmicutes/Bacteroidetes ratios and Proteobacteria load were comparable among patients and controls (p = 0.20). The Shannon index indicated a higher diversity in diverticulitis for Proteobacteria (p  < 0.00002) and all phyla combined (p = 0.002). PCoA based on Proteobacteria profiles resulted in visually separate clusters of patients and controls. The diagnostic accuracy of the cross-validated PLS-DA regression model was 84 %. The most discriminative species derived largely from the family Enterobacteriaceae. Diverticulitis patients have a higher diversity of fecal microbiota than controls from a mixed population, with the phylum Proteobacteria defining the difference. The analysis of intestinal microbiota offers a novel way to diagnose diverticuliti