A Brief Review of Computational Gene Prediction Methods

With the development of genome sequencing for many organisms, more and more raw sequences need to be annotated. Gene prediction by computational methods for finding the location of protein coding regions is one of the essential issues in bioinformatics. Two classes of methods are generally adopted: similarity based searches and ab initio prediction. Here, we review the development of gene prediction methods, summarize the measures for evaluating predictor quality, highlight open problems in this area, and discuss future research directions

Human transcriptional interactome of chromatin contribute to gene co-expression

BACKGROUND: Transcriptional interactome of chromatin is one of the important mechanisms in gene transcription regulation. By chromatin conformation capture and 3D FISH experiments, several chromatin interactions cases among sequence-distant genes or even inter-chromatin genes were reported. However, on genomics level, there is still little evidence to support these mechanisms. Recently based on Hi-C experiment, a genome-wide picture of chromatin interactions in human cells was presented. It provides a useful material for analysing whether the mechanism of transcriptional interactome is common. RESULTS: The main work here is to demonstrate whether the effects of transcriptional interactome on gene co-expression exist on genomic level. While controlling the effects of transcription factors control similarities (TCS), we tested the correlation between Hi-C interaction and the mutual ranks of gene co-expression rates (provided by COXPRESdb) of intra-chromatin gene pairs. We used 6,084 genes with both TF annotation and co-expression information, and matched them into 273,458 pairs with similar Hi-C interaction ranks in different cell types. The results illustrate that co-expression is strongly associated with chromatin interaction. Further analysis using GO annotation reveals potential correlation between gene function similarity, Hi-C interaction and their co-expression. CONCLUSIONS: According to the results in this research, the intra-chromatin interactome may have relation to gene function and associate with co-expression. This study provides evidence for illustrating the effect of transcriptional interactome on transcription regulation

Radiation Dose Calculations for a Hypothetical Accident in Xianning Nuclear Power Plant

Atmospheric dispersion modeling and radiation dose calculations have been performed for a hypothetical AP1000 SGTR accident by HotSpot code 3.03. TEDE, the respiratory time-integrated air concentration, and the ground deposition are calculated for various atmospheric stability classes, Pasquill stability categories A–F with site-specific averaged meteorological conditions. The results indicate that the maximum plume centerline ground deposition value of 1.2E+2 kBq/m2 occurred at about 1.4 km and the maximum TEDE value of 1.41E-05 Sv occurred at 1.4 km from the reactor. It is still far below the annual regulatory limits of 1 mSv for the public as set in IAEA Safety Report Series number 115. The released radionuclides might be transported to long distances but will not have any harmful effect on the public

CasFusionNet: A Cascaded Network for Point Cloud Semantic Scene Completion by Dense Feature Fusion

Semantic scene completion (SSC) aims to complete a partial 3D scene and predict its semantics simultaneously. Most existing works adopt the voxel representations, thus suffering from the growth of memory and computation cost as the voxel resolution increases. Though a few works attempt to solve SSC from the perspective of 3D point clouds, they have not fully exploited the correlation and complementarity between the two tasks of scene completion and semantic segmentation. In our work, we present CasFusionNet, a novel cascaded network for point cloud semantic scene completion by dense feature fusion. Specifically, we design (i) a global completion module (GCM) to produce an upsampled and completed but coarse point set, (ii) a semantic segmentation module (SSM) to predict the per-point semantic labels of the completed points generated by GCM, and (iii) a local refinement module (LRM) to further refine the coarse completed points and the associated labels from a local perspective. We organize the above three modules via dense feature fusion in each level, and cascade a total of four levels, where we also employ feature fusion between each level for sufficient information usage. Both quantitative and qualitative results on our compiled two point-based datasets validate the effectiveness and superiority of our CasFusionNet compared to state-of-the-art methods in terms of both scene completion and semantic segmentation. The codes and datasets are available at: https://github.com/JinfengX/CasFusionNet

Exploring photosynthesis evolution by comparative analysis of metabolic networks between chloroplasts and photosynthetic bacteria

BACKGROUND: Chloroplasts descended from cyanobacteria and have a drastically reduced genome following an endosymbiotic event. Many genes of the ancestral cyanobacterial genome have been transferred to the plant nuclear genome by horizontal gene transfer. However, a selective set of metabolism pathways is maintained in chloroplasts using both chloroplast genome encoded and nuclear genome encoded enzymes. As an organelle specialized for carrying out photosynthesis, does the chloroplast metabolic network have properties adapted for higher efficiency of photosynthesis? We compared metabolic network properties of chloroplasts and prokaryotic photosynthetic organisms, mostly cyanobacteria, based on metabolic maps derived from genome data to identify features of chloroplast network properties that are different from cyanobacteria and to analyze possible functional significance of those features. RESULTS: The properties of the entire metabolic network and the sub-network that consists of reactions directly connected to the Calvin Cycle have been analyzed using hypergraph representation. Results showed that the whole metabolic networks in chloroplast and cyanobacteria both possess small-world network properties. Although the number of compounds and reactions in chloroplasts is less than that in cyanobacteria, the chloroplast's metabolic network has longer average path length, a larger diameter, and is Calvin Cycle -centered, indicating an overall less-dense network structure with specific and local high density areas in chloroplasts. Moreover, chloroplast metabolic network exhibits a better modular organization than cyanobacterial ones. Enzymes involved in the same metabolic processes tend to cluster into the same module in chloroplasts. CONCLUSION: In summary, the differences in metabolic network properties may reflect the evolutionary changes during endosymbiosis that led to the improvement of the photosynthesis efficiency in higher plants. Our findings are consistent with the notion that since the light energy absorption, transfer and conversion is highly efficient even in photosynthetic bacteria, the further improvements in photosynthetic efficiency in higher plants may rely on changes in metabolic network properties