DNA modification plays a pivotal role in regulating gene expression in cell development. As prevalent markers on DNA, 5‐methylcytosine (5mC), N6‐methyladenine (6mA), and N4‐methylcytosine (4mC) can ...be recognized by specific methyltransferases, facilitating cellular defense and the versatile regulation of gene expression in eukaryotes and prokaryotes. Recent advances in DNA sequencing technology have permitted the positions of different modifications to be resolved at the genome‐wide scale, which has led to the discovery of several novel insights into the complexity and functions of multiple methylations. In this review, we summarize differences in the various mapping approaches and discuss their pros and cons with respect to their relative read depths, speeds, and costs. We also discuss the development of future sequencing technologies and strategies for improving the detection resolution of current sequencing technologies. Lastly, we speculate on the potentially instrumental role that these sequencing technologies might play in future research.
We provided a systematic review of various DNA sequencing technologies with respect to their pros and cons. We offered the potentially instrumental directions for improving the detection resolution of current DNA sequencing technologies.
2D ferroelectrics have received wide interest due to the remarkable quantum states of emerging physics at reduced dimensionality, associated with their exotic properties in high‐performance and ...nonvolatile functional devices. Here, by combing molecular beam epitaxy synthesis and scanning tunneling microscopy characterization, two metastable phases of layered In2Se3 films: β′‐ and β*‐In2Se3 are reported, which develop different types of in‐plane spontaneous polarizations, thus resulting in different striped morphologies. The anti‐ferroelectric order in β′‐In2Se3 and ferroelectric order of β*‐In2Se3 are identified, respectively, down to the 2D limit by comprehensive investigations of structural and spectroscopic signatures, including the lattice distortion, the spatial profile of images, the formation of domain structure, and the electronic band‐bending by polarization charges at edges. The ferroelectric switching between those two phases are further controlled via applying an electric field generated from the scanning tunneling microscopy tip in a reversible manner. The intriguing tunability between the (anti‐)ferroelectric orders in the 2D limit provides a promising platform for studying the interplay between electronic structure and ferroelectricity in van der Waals materials, and promotes potential development of miniaturized transistors and memory devices based on electric polarizations.
Single layer β′‐ and β*‐In2Se3 films are experimentally validated to host 2D in‐plane anti‐ferroelectric and ferroelectric order, respectively, with structural and spectroscopic evidences characterized by low‐temperature scanning tunneling microscopy (STM)/spectroscopy. An electric field‐induced phase transition is also realized via applying an STM tip pulse, demonstrating the manipulation of the ferroelectric polarization with reversible switching.
Abstract
The locations of the initiation of genomic DNA replication are defined as origins of replication sites (ORIs), which regulate the onset of DNA replication and play significant roles in the ...DNA replication process. The study of ORIs is essential for understanding the cell-division cycle and gene expression regulation. Accurate identification of ORIs will provide important clues for DNA replication research and drug development by developing computational methods. In this paper, the first integrated predictor named iORI-Euk was built to identify ORIs in multiple eukaryotes and multiple cell types. In the predictor, seven eukaryotic (Homo sapiens, Mus musculus, Drosophila melanogaster, Arabidopsis thaliana, Pichia pastoris, Schizosaccharomyces pombe and Kluyveromyces lactis) ORI data was collected from public database to construct benchmark datasets. Subsequently, three feature extraction strategies which are k-mer, binary encoding and combination of k-mer and binary were used to formulate DNA sequence samples. We also compared the different classification algorithms’ performance. As a result, the best results were obtained by using support vector machine in 5-fold cross-validation test and independent dataset test. Based on the optimal model, an online web server called iORI-Euk (http://lin-group.cn/server/iORI-Euk/) was established for the novel ORI identification.
Abstract
As a newly discovered protein posttranslational modification, histone lysine crotonylation (Kcr) involved in cellular regulation and human diseases. Various proteomics technologies have been ...developed to detect Kcr sites. However, experimental approaches for identifying Kcr sites are often time-consuming and labor-intensive, which is difficult to widely popularize in large-scale species. Computational approaches are cost-effective and can be used in a high-throughput manner to generate relatively precise identification. In this study, we develop a deep learning-based method termed as Deep-Kcr for Kcr sites prediction by combining sequence-based features, physicochemical property-based features and numerical space-derived information with information gain feature selection. We investigate the performances of convolutional neural network (CNN) and five commonly used classifiers (long short-term memory network, random forest, LogitBoost, naive Bayes and logistic regression) using 10-fold cross-validation and independent set test. Results show that CNN could always display the best performance with high computational efficiency on large dataset. We also compare the Deep-Kcr with other existing tools to demonstrate the excellent predictive power and robustness of our method. Based on the proposed model, a webserver called Deep-Kcr was established and is freely accessible at http://lin-group.cn/server/Deep-Kcr.
Coded aperture snapshot spectral imaging (CASSI) system encodes the 3D hyperspectral image (HSI) within a single 2D compressive image and then reconstructs the underlying HSI by employing an inverse ...optimization algorithm, which equips with the distinct advantage of snapshot but usually results in low reconstruction accuracy. To improve the accuracy, existing methods attempt to design either alternative coded apertures or advanced reconstruction methods, but cannot connect these two aspects via a unified framework, which limits the accuracy improvement. In this paper, we propose a convolution neural network-based end-to-end method to boost the accuracy by jointly optimizing the coded aperture and the reconstruction method. On the one hand, based on the nature of CASSI forward model, we design a repeated pattern for the coded aperture, whose entities are learned by acting as the network weights. On the other hand, we conduct the reconstruction through simultaneously exploiting intrinsic properties within HSI-the extensive correlations across the spatial and spectral dimensions. By leveraging the power of deep learning, the coded aperture design and the image reconstruction are connected and optimized via a unified framework. Experimental results show that our method outperforms the state-of-the-art methods under both comprehensive quantitative metrics and perceptive quality.
The Heisenberg ferromagnetic spin chain equation is investigated. By applying the improved F‐expansion method (Exp‐function method) and the Jacobi elliptic method, respectively, a series of exact ...solutions is constructed. The parametric conditions of the existence for the solutions are presented. These solutions comprise periodic wave solutions, doubly periodic wave solutions, and dark and bright soliton solutions, which are expressed in several different function forms, namely, Jacobi elliptic function, trigonometric function, hyperbolic function, and exponential function. The results illustrate that the Exp‐function method is a powerful symbolic algorithm to look for new solutions for the nonlinear evolution systems.
Conspectus The rise of organic bioelectronics efficiently bridges the gap between semiconductor devices and biological systems, leading to flexible, lightweight, and low-cost organic bioelectronic ...devices suitable for health or body signal monitoring. The introduction of organic semiconductors in the devices can soften the boundaries between microelectronic systems and dynamically active cells and tissues. Therefore, organic bioelectronics has attracted much attention recently due to the unique properties and promising applications. Organic thin film transistors (OTFTs), owing to their inherent capability of amplifying received signals, have emerged as one of the state-of-the-art biosensing platforms. The advantages of organic semiconductors in terms of synthetic freedom, low temperature solution processing, biocompatibility, and mechanical flexibility render OTFTs ideal transducers for wearable electronics, e-skin, and implantable devices. How to realize highly sensitive, selective, rapid, and efficient signal capture and extraction of biological recognition events is the major challenge in the design of biosensors. OTFTs are prone to converting the presence or change of target analytes into specific electrical signals even in complex biological systems. More importantly, OTFT sensors can be conveniently functionalized with chemical or biological modifications and exhibit substantially improved device sensitivity and selectivity as well as other analytical figure of merits, including calibration range, linearity, and accuracy. However, the stability and reproducibility of the organic devices need to be further improved. In this Account, we first introduce the unique features of OTFTs for bioelectronic applications. Two typical OTFT configurations, including organic electrochemical transistor (OECT) and electrolyte gated organic field effect transistor (EGOFET), are highlighted in their sensing applications mainly due to the operation of the devices in electrolytes and the combination of ionic and electronic charge transports in the devices. These devices are potentiometric transducers with low working voltages (<1 V) and high sensitivity, and are thus suitable for wearable applications with low power consumption. Second, the functionalization strategies on channel materials, electrolytes, and gate electrodes based on various modification methods and sensing mechanisms are discussed in sequence. In an OECT- or EGOFET-based biosensor, the device performance is particularly sensitive to the physical properties of the two interfaces, including channel/electrolyte and gate/electrolyte interfaces. Any change in the potential drop or capacitance of either interface can influence the channel current substantially. Therefore, the functionalization of the interfaces is critical to the sensing performance. In particular, when an electrochemically active material is modified on the interfaces, the reaction of the analyte catalyzed by the modified material can influence the interface potential and lead to a channel current response much stronger than that of a conventional electrochemical measurement. So the biosensors are much more sensitive than typical analytical methods due to the signal amplification of the transistors. Third, the processing techniques including screen printing and inkjet printing and the possibility for mass production are discussed. The applications of organic transistors in wearable electronics and healthcare monitoring systems, especially the fabric OECT-based biosensors for noninvasive detection, are presented. It is expected that the versatile organic transistors will enable various compact, flexible and disposable biosensors compatible with wearable electronics.
Abstract
The protein Yin Yang 1 (YY1) could form dimers that facilitate the interaction between active enhancers and promoter-proximal elements. YY1-mediated enhancer–promoter interaction is the ...general feature of mammalian gene control. Recently, some computational methods have been developed to characterize the interactions between DNA elements by elucidating important features of chromatin folding; however, no computational methods have been developed for identifying the YY1-mediated chromatin loops. In this study, we developed a deep learning algorithm named DeepYY1 based on word2vec to determine whether a pair of YY1 motifs would form a loop. The proposed models showed a high prediction performance (AUCs$\ge$0.93) on both training datasets and testing datasets in different cell types, demonstrating that DeepYY1 has an excellent performance in the identification of the YY1-mediated chromatin loops. Our study also suggested that sequences play an important role in the formation of YY1-mediated chromatin loops. Furthermore, we briefly discussed the distribution of the replication origin site in the loops. Finally, a user-friendly web server was established, and it can be freely accessed at http://lin-group.cn/server/DeepYY1.
Abstract
Meiotic recombination is one of the most important driving forces of biological evolution, which is initiated by double-strand DNA breaks. Recombination has important roles in genome ...diversity and evolution. This review firstly provides a comprehensive survey of the 15 computational methods developed for identifying recombination hotspots in Saccharomyces cerevisiae. These computational methods were discussed and compared in terms of underlying algorithms, extracted features, predictive capability and practical utility. Subsequently, a more objective benchmark data set was constructed to develop a new predictor iRSpot-Pse6NC2.0 (http://lin-group.cn/server/iRSpot-Pse6NC2.0). To further demonstrate the generalization ability of these methods, we compared iRSpot-Pse6NC2.0 with existing methods on the chromosome XVI of S. cerevisiae. The results of the independent data set test demonstrated that the new predictor is superior to existing tools in the identification of recombination hotspots. The iRSpot-Pse6NC2.0 will become an important tool for identifying recombination hotspot.
To solve the low spatial and/or temporal resolution problem which the conventional hyperspectral cameras often suffer from, coded hyperspectral imaging systems have attracted more attention recently. ...Recovering a hyperspectral image (HSI) from its corresponding coded image is an ill-posed inverse problem, and learning accurate prior of HSI is essential to solve this inverse problem. In this paper, we present an effective convolutional neural network (CNN) based method for coded HSI reconstruction, which learns the deep prior from the external dataset as well as the internal information of input coded image with spatial-spectral constraint. Specifically, we first develop a CNN-based channel attention reconstruction network to effectively exploit the spatial-spectral correlation of the HSI. Then, the reconstruction network is learned by leveraging an arbitrary external hyperspectral dataset to exploit the general spatial-spectral correlation under adversarial loss. Finally, we customize the network by internal learning with spatial-spectral constraint and total variation regularization for each coded image, which can make use of the internal imaging model to learn specific prior for current desirable image and effectively avoids overfitting. Experimental results using both synthetic data and real images show that our method outperforms the state-of-the-art methods on several popular coded hyperspectral imaging systems under both comprehensive quantitative metrics and perceptive quality.
