Computational prediction of Ds transposon insertion sites in plants using DNA structural features

Transposons, with the ability to integrate into new positions in the genome, can disrupt a gene\u27s function and thereby have been utilized as tools for genome mutagenesis. Critical to improving efficiency of such applications is to elucidate the patterns and preferences of insertion sites selection. We here focus on understanding target site selection of transposon Ac/Ds, one of the best-characterized transposon systems in plants, by exploring various DNA features and predicting insertion sites. A package named DnaFVP (DNA Feature Calculation, Visualization and Vector Preparation) was first developed for calculation, visualization and analysis of various DNA features, including nucleotide sequence features and a broad list of structural/physical properties. In addition, this package allows data preparation prior to calculating features and/or preparation of feature vectors for machine learning. It is developed for building a semi-automatic pipeline to explore various DNA features of any collection of genomic DNA sequences of interest and to prepare feature vectors for further machine learning. By use of combined nucleotide and structural features with application of the DnaFVP package, we prepared various feature vectors and predicted Ds insertion sites for machine learning. Training datasets included well-evidenced Ds insertion events (1605 events in maize and 2078 events in Arabidopsis) as positive datasets and 2000 random sampled genomic coordinates in genic regions from maize and Arabidopsis as negative datasets. An ROC (Receiver Operating Characteristic) of 0.77 in maize, 0.85 in Arabidopsis, and 0.82 in a combined dataset of maize and Arabidopsis have been achieved. One initially tested dataset in maize shows interesting results. Our prediction may provide further insight to the Ac/Ds transposition mechanism, and facilitate the ease of targeted mutagenesis and gene delivery mediated by transposons

Somatic Mutagenesis with a Sleeping Beauty Transposon System Leads to Solid Tumor Formation in Zebrafish

Large-scale sequencing of human cancer genomes and mouse transposon-induced tumors has identified a vast number of genes mutated in different cancers. One of the outstanding challenges in this field is to determine which genes, when mutated, contribute to cellular transformation and tumor progression. To identify new and conserved genes that drive tumorigenesis we have developed a novel cancer model in a distantly related vertebrate species, the zebrafish, Danio rerio. The Sleeping Beauty (SB) T2/Onc transposon system was adapted for somatic mutagenesis in zebrafish. The carp ß-actin promoter was cloned into T2/Onc to create T2/OncZ. Two transgenic zebrafish lines that contain large concatemers of T2/OncZ were isolated by injection of linear DNA into the zebrafish embryo. The T2/OncZ transposons were mobilized throughout the zebrafish genome from the transgene array by injecting SB11 transposase RNA at the 1-cell stage. Alternatively, the T2/OncZ zebrafish were crossed to a transgenic line that constitutively expresses SB11 transposase. T2/OncZ transposon integration sites were cloned by ligation-mediated PCR and sequenced on a Genome Analyzer II. Between 700–6800 unique integration events in individual fish were mapped to the zebrafish genome. The data show that introduction of transposase by transgene expression or RNA injection results in an even distribution of transposon re-integration events across the zebrafish genome. SB11 mRNA injection resulted in neoplasms in 10% of adult fish at ∼10 months of age. T2/OncZ-induced zebrafish tumors contain many mutated genes in common with human and mouse cancer genes. These analyses validate our mutagenesis approach and provide additional support for the involvement of these genes in human cancers. The zebrafish T2/OncZ cancer model will be useful for identifying novel and conserved genetic drivers of human cancers

Studies of site-specific DNA double strand break repair in plants

The maize transposon Ac/Ds transposes by a “cut and paste"” mechanism, leaving a site-specific DSB at the Ds locus for repair. Previous studies in maize and Arabidopsis showed that Ac/Ds excision can stimulate homologous recombination between tandem duplicated repeats and between ectopic homologies. In this study, we tested the efficacy of such Ac/Ds excision-induced homologous recombination to achieve gene targeting in Arabidopsis. A defective visual-selective dual marker harboring Ds element is transformed into Arabidopsis as target locus, and donor homology is provided by Agrobacterium-mediated T-DNA by floral dipping. The DSB inducer Ac is either first crossed into target lines or co-transformed with donor T-DNA, and several independent germinal recombinants have been recovered from both strategies with gene targeting frequency of 0.3-2.0X10(-3). We also attempted gene targeting by ectopic recombination, with donor homology provided by the concomitant integrated T-DNA copies in genome. With this approach the gene targeting frequency turns out less than 6.7X10(-6). Our results suggest that Ac/Ds transposon-induced homologous recombination may provide an alternative gene targeting strategy. The implications of these results for plant gene targeting are discussed. Multiple pathways are used to repair Double Strand Breaks (DSBs), including Nonhomologous End Joining (NHEJ) and Homologous Recombination (HR) which can take Single Strand Annealing (SSA) or Gene Conversion (GC) pathway. DSB repair in plants has been extensively studied using site-specific DSB agents, including two mechanistically different inducers transposon Ac/Ds and endonuclease I-SceI. A direct comparison between Ac/Ds and I-SceI in DSB repair, however, is lacking due to the diversity of systems used among previous studies. In addition, only a few DSB repair studies addressed germinal recombination frequencies. In this study, we developed three constructs (HRS1, 2 and 3) that allow comparison of multiple pathways for repair of DSBs induced by Ac/Ds excision and by I-SceI cutting at the same chromosomal loci. The results show that differential pathway utilization exists between the repair of Ac/Ds excision and I-SceI induced DSBs: (i) In somatic tissues, Ac/Ds induced HR preferentially utilizes SSA and/or represses GC 4 fold higher than I-SceI; (ii) In germinal tissues, repair of Ac/Ds induced DSBs favors NHEJ and strongly represses HR by 2 to 3 orders magnitude, whereas I-SceI-induced DSBs are repaired equally by NHEJ and HR; (iii) Furthermore, Ac/Ds induced germinal HR preferentially utilizes SSA and/or represses GC 5 fold higher than I-SceI; and (iv) germinal tissues preferentially utilize SSA 3 fold higher than GC compared to somatic tissues for both Ac/Ds and I-SceI induced HR. Despite these inequalities, a roughly positive correlation exists between somatic and germinal HR frequencies for both Ac/Ds and I-SceI-induced DSBs. Overall, DSB repair pathway and frequency is strongly affected by both cell type (somatic vs. germinal) and DSB agent. The striking difference of repair pathway utilization between Ac/Ds and I-SceI suggests specific role(s) of Ac/Ds in DSB repair. The hairpin intermediate generated prior to DSB formation, or Ac transposase per se, may promote NHEJ and SSA and/or repress GC. These results provide new insight into how transposons affect genome structure and also shed light on the biology of DSB-induced HR that may facilitate the development of genome modification tools for plants.</p

Computational prediction of Ds transposon insertion sites in plants using DNA structural features

Transposons, with the ability to integrate into new positions in the genome, can disrupt a gene's function and thereby have been utilized as tools for genome mutagenesis. Critical to improving efficiency of such applications is to elucidate the patterns and preferences of insertion sites selection. We here focus on understanding target site selection of transposon Ac/Ds, one of the best-characterized transposon systems in plants, by exploring various DNA features and predicting insertion sites. A package named DnaFVP (DNA Feature Calculation, Visualization and Vector Preparation) was first developed for calculation, visualization and analysis of various DNA features, including nucleotide sequence features and a broad list of structural/physical properties. In addition, this package allows data preparation prior to calculating features and/or preparation of feature vectors for machine learning. It is developed for building a semi-automatic pipeline to explore various DNA features of any collection of genomic DNA sequences of interest and to prepare feature vectors for further machine learning. By use of combined nucleotide and structural features with application of the DnaFVP package, we prepared various feature vectors and predicted Ds insertion sites for machine learning. Training datasets included well-evidenced Ds insertion events (1605 events in maize and 2078 events in Arabidopsis) as positive datasets and 2000 random sampled genomic coordinates in genic regions from maize and Arabidopsis as negative datasets. An ROC (Receiver Operating Characteristic) of 0.77 in maize, 0.85 in Arabidopsis, and 0.82 in a combined dataset of maize and Arabidopsis have been achieved. One initially tested dataset in maize shows interesting results. Our prediction may provide further insight to the Ac/Ds transposition mechanism, and facilitate the ease of targeted mutagenesis and gene delivery mediated by transposons.</p

Real-Time Detection and Recognition of Road Traffic Signs using MSER and Random Forests

Real-time detection and recognition of road traffic signs plays an important role in advanced driving assistance system. Typically, the region of interest (ROI) method is effective in feature extraction but inefficient because it is sensitive to illumination changes. In this paper, we propose a maximally stable extremal regions (MSER) method with image enhancement to greatly improve ROI. Firstly, we employ gray world algorithm to process original images. And then potential areas of traffic signs are obtained through increasing the image contrast ratio and extracting the image-enhanced MSER. According to the characteristic variable and the geometry moment invariants, the geometric characteristics of traffic signs are extracted to obtain the ROIs. Finally, HSV-HOG-LBP feature is constructed and the random forests algorithm is used to identify the traffic signs. The experimental results show that our proposed method show strong robustness on illumination condition and rotation scale, and achieves a good performance by experiments with actual images and German traffic sign detection benchmark (GTSDB) data set

Real-Time Detection and Recognition of Road Traffic Signs using MSER and Random Forests

Real-time detection and recognition of road traffic signs plays an important role in advanced driving assistance system. Typically, the region of interest (ROI) method is effective in feature extraction but inefficient because it is sensitive to illumination changes. In this paper, we propose a maximally stable extremal regions (MSER) method with image enhancement to greatly improve ROI. Firstly, we employ gray world algorithm to process original images. And then potential areas of traffic signs are obtained through increasing the image contrast ratio and extracting the image-enhanced MSER. According to the characteristic variable and the geometry moment invariants, the geometric characteristics of traffic signs are extracted to obtain the ROIs. Finally, HSV-HOG-LBP feature is constructed and the random forests algorithm is used to identify the traffic signs. The experimental results show that our proposed method show strong robustness on illumination condition and rotation scale, and achieves a good performance by experiments with actual images and German traffic sign detection benchmark (GTSDB) data set

Simulation and Analysis for Electric Bicycle Traffic Flow

Keywords: traffic engineering, electric bicycle flow, lane changing rule, cellular automaton model. Abstract. The electric bicycle has become the main part of non motor vehicles in small and medium-sized cities. Research on the traffic flow characteristic of the electric bicycle has important practical significance. Based on NaSch model, this paper models electric bicycle traffic flow with CA model and improves the lane changing model. Then the electric bicycle lanes change into general lane change and whistle change, and corresponding lane changing rules are set up. Simulation analysis of the model is carried out. The results show that when the traffic density is small, whistling behavior to raise the road utilization rate has some effect, but in the high density, whistle behavior can not improve road traffic capacity

Multi-Agent Based Microscopic Simulation Modeling for Urban Traffic Flow

Traffic simulation plays an important role in the evaluation of traffic decisions. The movement of vehicles essentially is the operating process of drivers, in order to reproduce the urban traffic flow from the micro-aspect on computer, this paper establishes an urban traffic flow microscopic simulation system (UTFSim) based on multi-agent. The system is seen as an intelligent virtual environment system (IVES), and the four-layer structure of it is built. The road agent, vehicle agent and signal agent are modeled. The concept of driving trajectory which is divided into LDT (Lane Driving Trajectory) and VDDT (Vehicle Dynamic Driving Trajectory) is introduced. The “Link-Node” road network model is improved. The driving behaviors including free driving, following driving, lane changing, slowing down, vehicle stop, etc. are analyzed. The results of the signal control experiments utilizing the UTFSim developed in the platform of Visual Studio. NET indicates that it plays a good performance and can be used in the evaluation of traffic management and control

Urban Expressway Travel Time Prediction Method Based on Fuzzy Adaptive Kalman Filter

Abstract: According to the poor adaptive ability of traditional filter algorithm in the estimation for traffic state and travel time with Kalman filter, an improved fuzzy adaptive Kalman filtering method was proposed. The new interest of observation noise was defined, and the fuzzy logic was used to adjust the importance weights of system noise and observation noise through on-line monitoring the interest changes, which changed the trust and utilization degree of the model for the observation information, and this made the filter eventually tend to be stable. To guarantee the real-time performance of system, a direct input- output fuzzy membership function matching method was put forward to take the place of fuzzy reasoning. The method was tested on the urban expressway in Guangzhou by using real-time detection data, and the result show that the traffic state estimation model had better tracking ability than conventional Kalman filter, and results of travel time prediction show that there was a slight difference between the prediction value and that of actual observation in free traffic flow state, and the relative error was under 15 % in traffic congested state. The precision and applicability of this method were acceptable, and it can be used to provide a basis for travel time of urban expressway in traffic control and guidance system