Computational evaluation of TIS annotation for prokaryotic genomes

<p>Abstract</p> <p>Background</p> <p>Accurate annotation of translation initiation sites (TISs) is essential for understanding the translation initiation mechanism. However, the reliability of TIS annotation in widely used databases such as RefSeq is uncertain due to the lack of experimental benchmarks.</p> <p>Results</p> <p>Based on a homogeneity assumption that gene translation-related signals are uniformly distributed across a genome, we have established a computational method for a large-scale quantitative assessment of the reliability of TIS annotations for any prokaryotic genome. The method consists of modeling a positional weight matrix (PWM) of aligned sequences around predicted TISs in terms of a linear combination of three elementary PWMs, one for true TIS and the two others for false TISs. The three elementary PWMs are obtained using a reference set with highly reliable TIS predictions. A generalized least square estimator determines the weighting of the true TIS in the observed PWM, from which the accuracy of the prediction is derived. The validity of the method and the extent of the limitation of the assumptions are explicitly addressed by testing on experimentally verified TISs with variable accuracy of the reference sets. The method is applied to estimate the accuracy of TIS annotations that are provided on public databases such as RefSeq and ProTISA and by programs such as EasyGene, GeneMarkS, Glimmer 3 and TiCo. It is shown that RefSeq's TIS prediction is significantly less accurate than two recent predictors, Tico and ProTISA. With convincing proofs, we show two general preferential biases in the RefSeq annotation, <it>i.e</it>. over-annotating the longest open reading frame (LORF) and under-annotating ATG start codon. Finally, we have established a new TIS database, SupTISA, based on the best prediction of all the predictors; SupTISA has achieved an average accuracy of 92% over all 532 complete genomes.</p> <p>Conclusion</p> <p>Large-scale computational evaluation of TIS annotation has been achieved. A new TIS database much better than RefSeq has been constructed, and it provides a valuable resource for further TIS studies.</p

BinTree Seeking: A Novel Approach to Mine Both Bi-Sparse and Cohesive Modules in Protein Interaction Networks

Modern science of networks has brought significant advances to our understanding of complex systems biology. As a representative model of systems biology, Protein Interaction Networks (PINs) are characterized by a remarkable modular structures, reflecting functional associations between their components. Many methods were proposed to capture cohesive modules so that there is a higher density of edges within modules than those across them. Recent studies reveal that cohesively interacting modules of proteins is not a universal organizing principle in PINs, which has opened up new avenues for revisiting functional modules in PINs. In this paper, functional clusters in PINs are found to be able to form unorthodox structures defined as bi-sparse module. In contrast to the traditional cohesive module, the nodes in the bi-sparse module are sparsely connected internally and densely connected with other bi-sparse or cohesive modules. We present a novel protocol called the BinTree Seeking (BTS) for mining both bi-sparse and cohesive modules in PINs based on Edge Density of Module (EDM) and matrix theory. BTS detects modules by depicting links and nodes rather than nodes alone and its derivation procedure is totally performed on adjacency matrix of networks. The number of modules in a PIN can be automatically determined in the proposed BTS approach. BTS is tested on three real PINs and the results demonstrate that functional modules in PINs are not dominantly cohesive but can be sparse. BTS software and the supporting information are available at: www.csbio.sjtu.edu.cn/bioinf/BTS/

The cross-sectional relationship between vitamin C and high-sensitivity C-reactive protein levels: insights from NHANES database

BackgroundAscorbic acid or vitamin C has antioxidant and anti-inflammatory properties that may impact markers of inflammation like C-reactive protein (CRP). However, studies specifically on vitamin C and high-sensitivity CRP (hs-CRP) have been scarce.MethodsWe conducted a cross-sectional analysis of the National Health and Nutrition Examination Survey 2017–2018 dataset including 5,380 U.S. adults aged ≥20 years. Multiple regression models examined the relationship between plasma vitamin C and serum hs-CRP while adjusting for potential confounders. Stratified analyses and curve fitting assessed effect modification and nonlinearity.ResultsAn inverse association was found between plasma vitamin C and serum hs-CRP overall (β = −0.025, 95% CI: −0.033 to −0.017, p &lt; 0.00001) and in subgroups except for the “other Hispanic” subgroup in model II (β = −0.009, 95% CI: (−0.040, 0.023), p = 0.5885). The relationship was nonlinear, with the greatest hs-CRP reduction observed up to a plasma vitamin C level of 53.1 μmol/L.ConclusionThe results showed a non-linear negative correlation between vitamin C levels and hs-CRP in adults. These results suggest vitamin C intake may reduce inflammation and cardiovascular risk, but only up to 53.1 μmol/L plasma vitamin C