In silico design of single guide RNA (sgRNA) plays a critical role in clustered regularly interspaced, short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) system. Continuous efforts are aimed at improving sgRNA design with efficient on-target activity and reduced off-target mutations. In the last 5 years, an increasing number of deep learning-based methods have achieved breakthrough performance in predicting sgRNA on- and off-target activities. Nevertheless, it is worthwhile to systematically evaluate these methods for their predictive abilities. In this review, we conducted a systematic survey on the progress in prediction of on- and off-target editing. We investigated the performances of 10 mainstream deep learning-based on-target predictors using nine public datasets with different sample sizes. We found that in most scenarios, these methods showed superior predictive power on large- and medium-scale datasets than on small-scale datasets. In addition, we performed unbiased experiments to provide in-depth comparison of eight representative approaches for off-target prediction on 12 publicly available datasets with various imbalanced ratios of positive/negative samples. Most methods showed excellent performance on balanced datasets but have much room for improvement on moderate- and severe-imbalanced datasets. This study provides comprehensive perspectives on CRISPR/Cas9 sgRNA on- and off-target activity prediction and improvement for method development.

Spectrally efficient frequency division multiplexing (SEFDM) has been recognised as a promising multi‐carrier technology since it can offer a higher spectral efficiency than orthogonal frequency division multiplexing (OFDM). In this study, the authors provide a framework for SEFDM transceiver design and investigate the techniques of channel equalisation and data detection of SEFDM over frequency selective fading scenarios. Specifically, they first demonstrate through mathematical analysis that SEFDM with the proposed frequency‐domain equalisation scheme can benefit from the frequency diversity, and thus, performs more robust to the frequency selective fading environment than OFDM. To effectively mitigate inter‐carrier interference raised by the non‐orthogonality between sub‐carriers of SEFDM, they then propose a low‐computational data detection scheme based on the Viterbi principle and maximum a posteriori criterion. The proposed detector can provide an indistinguishable performance from the maximum likelihood detection using sphere decoding while entailing a much lower computation. Numerical results validate the superiority of SEFDM to OFDM over frequency selective fading channels.

Spectrally efficient frequency division multiplexing enables higher spectral efficiency at the cost of suffering from inter‐carrier interference (ICI), which leads to either a severe error floor or a heavy computational burden at the receiver. In this study, the authors propose a low‐complexity demodulation technique, in which the detected symbols are used as feedback to effectively mitigate ICI. To restrain the error propagation caused by non‐ideal feedback, the authors enumerate subsequent unknown symbols as preconditions, and then perform the demodulation based on minimum distance criterion, in order to enable the optimal solution in each step of feedback. As a consequence, the computations necessitated to data demodulation can be significantly reduced as a quadratic order of symbol‐size. Furthermore, the authors mathematically characterise the demodulation performance by deriving the signal‐to‐interference‐plus‐noise ratio as a function of bandwidth compression factor. It is shown that the demodulation scheme can effectively reduce the error floor raised by ICI while preserving a low complexity. Simulation results demonstrate that the proposed demodulator outperforms the conventional ones, and achieves indistinguishable performance as that of orthogonal frequency‐division multiplexing while increasing up to 25% spectrum efficiency.

MOTIVATION: Genome-wide pervasive transcription is widespread in eukaryotes, revealing an extensive array of antisense transcription that involves hundreds of previously unknown non-coding RNAs. Individual cases have shown that antisense transcription influences sense transcription, however, genome-wide mechanisms of how antisense transcription regulates sense transcription remain to be elucidated. RESULTS: Here, we performed a systematic analysis of sense-antisense transcription and nucleosome occupancy in yeast. We found that antisense transcription is associated with nucleosome occupancy in sense promoters. Using RNA polymerase II inactivation data as a reasonable approximation to antisense transcription inactivation data, we further showed that antisense transcripts increase nucleosome occupancy in sense promoter regions they overlap, and reduce nucleosome occupancy in sense promoter regions around their transcription termination sites. These results reveal the previously unappreciated roles of antisense transcription in directing nucleosome occupancy in sense promoters. Our findings will have implications in understanding regulatory functions of antisense transcription.

How to improve the short-term fairness and increase the aggregated throughput simultaneously is a major problem in WLANs, yet has not been solved satisfactorily in previous work, especially in the situation that network loads vary significantly. To improve the performance in saturation state, we propose a novel access mechanism that sets an optimal Contention Window (CW) for all nodes in the network. By introducing a new parameter of CW Index (CWI), we present a linear CW adjustment rule based on the active node number and then develop an estimation algorithm of node number with three thresholds. Compared to previous algorithms, the new algorithm predicts the future node number based on the current network status, thus minimizes the fluctuating effect of idle slot intervals observed at each process. Moreover, it can identify the dense degree of idle slot intervals and track the changes of the accurate node number quickly. By selecting the optimal CWI adaptively, the maximum aggregated throughput keeps basically a constant despite the variation of the number of nodes. Meanwhile, by eliminating multiple transmission attempts of the same node, the short-term fairness is improved significantly. To evaluate the mechanism, we derive closed-form expressions for the network performance. The simulation results demonstrate the validity and good scalability of the proposed access mechanism.

MOTIVATION: The intrinsic DNA sequence is an important determinant of nucleosome positioning. Some DNA sequence patterns can facilitate nucleosome formation, while others can inhibit nucleosome formation. Nucleosome positioning influences the overall rate of sequence evolution. However, its impacts on specific patterns of sequence evolution are still poorly understood. RESULTS: Here, we examined whether nucleosomal DNA and nucleosome-depleted DNA show distinct polymorphism patterns to maintain adequate nucleosome architecture on a genome scale in yeast. We found that sequence polymorphisms in nucleosomal DNA tend to facilitate nucleosome formation, whereas polymorphisms in nucleosome-depleted DNA tend to inhibit nucleosome formation, which is especially evident at nucleosome-disfavored sequences in nucleosome-free regions at both ends of genes. Sequence polymorphisms facilitating nucleosome positioning correspond to stable nucleosome positioning. These results reveal that sequence polymorphisms are under selective constraints to maintain nucleosome positioning. CONTACT: [email protected]; [email protected]

Sequence changes in coding region and regulatory region of the gene itself (cis) determine most of gene expression divergence between closely related species. But gene expression divergence between yeast species is not correlated with evolution of primary nucleotide sequence. This indicates that other factors in cis direct gene expression divergence. Here, we studied the contribution of DNA three-dimensional structural evolution as cis to gene expression divergence. We found that the evolution of DNA structure in coding regions and gene expression divergence are correlated in yeast. Similar result was also observed between Drosophila species. DNA structure is associated with the binding of chromatin remodelers and histone modifiers to DNA sequences in coding regions, which influence RNA polymerase II occupancy that controls gene expression level. We also found that genes with similar DNA structures are involved in the same biological process and function. These results reveal the previously unappreciated roles of DNA structure as cis-effects in gene expression.

Channel estimation for multiple-input multiple-output/orthogonal frequency-division multiplexing (MIMO/ OFDM) systems in linearly time-varying (LTV) wireless channels using superimposed training (ST) is considered. The LTV channel is modeled by truncated discrete Fourier bases. Based on this model, a two-step approach is adopted to estimate the LTV channel over multiple OFDM symbols. We also present a performance analysis of the channel estimation and derive a closed-form expression for the channel estimation variances. It is shown that the estimation variances, unlike that of the conventional ST-based schemes, approach to a fixed lowerbound as the training length increases, which is directly proportional to information-pilot power ratios. To further enhance the channel estimation performance with a limited pilot power, an interference cancellation procedure is introduced to iteratively mitigate the information sequence interference to channel estimation. Simulation results show that the proposed algorithm outperforms frequency-division multiplexed trainings schemes.

BACKGROUND: Nucleosomes regulate DNA accessibility and therefore play a central role in transcription control. Computational methods have been developed to predict static nucleosome positions from DNA sequences, but nucleosomes are dynamic in vivo. RESULTS: Motivated by our observation that transcriptional interaction is discriminative information for nucleosome occupancy, we developed a novel computational approach to identify dynamic nucleosome positions at promoters by combining transcriptional interaction and genomic sequence information. Our approach successfully identified experimentally determined nucleosome positioning dynamics available in three cellular conditions, and significantly improved the prediction accuracy which is based on sequence information alone. We then applied our approach to various cellular conditions and established a comprehensive landscape of dynamic nucleosome positioning in yeast. CONCLUSION: Analysis of this landscape revealed that the majority of nucleosome positions are maintained during most conditions. However, nucleosome occupancy at most promoters fluctuates with the corresponding gene expression level and is reduced specifically at the phase of peak expression. Further investigation into properties of nucleosome occupancy identified two gene groups associated with distinct modes of nucleosome modulation. Our results suggest that both the intrinsic sequence and regulatory proteins modulate nucleosomes in an altered manner.

BACKGROUND: It is an important pre-processing step to accurately estimate missing values in microarray data, because complete datasets are required in numerous expression profile analysis in bioinformatics. Although several methods have been suggested, their performances are not satisfactory for datasets with high missing percentages. RESULTS: The paper explores the feasibility of doing missing value imputation with the help of gene regulatory mechanism. An imputation framework called histone acetylation information aided imputation method (HAIimpute method) is presented. It incorporates the histone acetylation information into the conventional KNN(k-nearest neighbor) and LLS(local least square) imputation algorithms for final prediction of the missing values. The experimental results indicated that the use of acetylation information can provide significant improvements in microarray imputation accuracy. The HAIimpute methods consistently improve the widely used methods such as KNN and LLS in terms of normalized root mean squared error (NRMSE). Meanwhile, the genes imputed by HAIimpute methods are more correlated with the original complete genes in terms of Pearson correlation coefficients. Furthermore, the proposed methods also outperform GOimpute, which is one of the existing related methods that use the functional similarity as the external information. CONCLUSION: We demonstrated that the using of histone acetylation information could greatly improve the performance of the imputation especially at high missing percentages. This idea can be generalized to various imputation methods to facilitate the performance. Moreover, with more knowledge accumulated on gene regulatory mechanism in addition to histone acetylation, the performance of our approach can be further improved and verified.

The paper develops a new method for estimating and correcting the carrier frequency offsets (CFO) of orthogonal frequency division multiple access (OFDMA) systems at the uplink receiver. Multiple CFO of the different users in the OFDMA system may violate the orthogonality among subcarriers and induce inter-carrier interference (ICI) and inter-user interference (IUI). The IUI provides some extra challenges to CFO estimation not addressed in OFDM systems with only a single CFO. By significantly reducing the IUI, the paper presents an improved CFO estimator which can provide a higher accuracy than the existing approaches. After having estimated the CFO, the author also proposes a new method for CFO correction or compensation at the uplink receiver. Through simulations, it is shown that the new CFO estimator and the correction method outperform the existing approaches.