Benchmarking deep learning methods for predicting CRISPR/Cas9 sgRNA on- and off-target activities.

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.

Generalized Poisson Hurdle Model for Count Data and Its Application in Ear Disease.

For count data, though a zero-inflated model can work perfectly well with an excess of zeroes and the generalized Poisson model can tackle over- or under-dispersion, most models cannot simultaneously deal with both zero-inflated or zero-deflated data and over- or under-dispersion. Ear diseases are important in healthcare, and falls into this kind of count data. This paper introduces a generalized Poisson Hurdle model that work with count data of both too many/few zeroes and a sample variance not equal to the mean. To estimate parameters, we use the generalized method of moments. In addition, the asymptotic normality and efficiency of these estimators are established. Moreover, this model is applied to ear disease using data gained from the New South Wales Health Research Council in 1990. This model performs better than both the generalized Poisson model and the Hurdle model.

Insights into active intragenic enhancers.

Enhancers can regulate gene transcription from afar. Many enhancers are located in genes. Although the regulatory roles of several individual intragenic enhancers have been elaborated, a genome-wide insight into intragenic enhancers remains to be elucidated. We found that active intragenic enhancers have a preference for being located in expressed genes. Unlike intergenic enhancers, active intragenic enhancers are enriched of H3K79me2 epigenetic signal, and depleted of variant histone H2A.Z. Moreover, eRNAs of active intragenic enhancers show lower degradation rates than those of the other enhancers. Our findings will have implications in understanding functions of intragenic enhancers.

150-Gb/s SEFDM IM/DD transmission using log-MAP Viterbi decoding for short reach optical links.

Spectral efficient frequency division multiplexing (SEFDM) can improve the spectral efficiency for next-generation optical and wireless communications. In this work, we apply SEFDM in beyond 100-Gb/s optical intensity modulation and direct detection transmissions and propose a low-complexity logarithmic-maximum-a-posteriori (log-MAP) Viterbi decoding algorithm to achieve the maximum likelihood (ML) detection. We evaluate the likelihood of detections using a posteriori probability instead of Euclidean distance by taking both noise and inter-carrier interference into consideration. In order to balance the performance and complexity, we then employ Viterbi decoding principle to retain only certain paths with ML detections (a.k.a., the surviving paths) while discarding the others during the decoding procedure. Results show that the proposed log-MAP Viterbi decoding scheme achieves optimal performance due to the precise likelihood evaluation, which guarantees the retention of the global ML detection. By using the proposed decoding scheme, the data rate of SEFDM signals can reach 150-Gb/s in a 2-km standard single mode fiber transmission, using only 28-GHz bandwidth and 16-QAM modulation. Experimental results show that the 16-QAM modulated SEFDM signal with a bandwidth compression factor of 0.8 outperforms 32-QAM modulated OFDM, while both signals have the same bandwidth (28-GHz) and data rate (140-Gb/s), which demonstrate the superiority of SEFDM in optical short reach applications.

Analysis of global gene expression profiles suggests a role of acute inflammation in type 3C diabetes mellitus caused by pancreatic ductal adenocarcinoma.

AIMS/HYPOTHESIS: Pancreatic ductal adenocarcinoma (PDAC) can cause type 3C diabetes, known as PDAC-associated diabetes mellitus (PDAC-DM), but the mechanism is unknown. This study aimed to reveal the mechanism. METHODS: PDAC lesions from patients with or without PDAC-DM (n = 4 in each group) were individually profiled for 23,512 mRNAs with microarrays. Bioinformatic analysis and in vivo and in vitro assays were then conducted. RESULTS: We determined that 2,778 genes were differentially expressed; over-representation of ten genes was validated with quantitative RT-PCR. The analysis of gene ontology showed that the differentially expressed secretory genes were related mainly to inflammation. High levels of a marker of inflammation (C-reactive protein [CRP]) and an inflammatory mediator (TNF super-family member 13 [TNFSF13]) were found in the serum of patients with PDAC-DM. After surgical resection of PDAC lesions, CRP and TNFSF13 levels significantly decreased (p < 0.01). Furthermore, we found that the levels of TNFSF13 in PDAC lesions and TNFSF13 and CRP in serum were significantly correlated with the diabetic status of patients with PDAC-DM (p < 0.01). Assays in vivo showed that after exposure to an inhibitor of inflammation (celecoxib), the fasting blood glucose level in the mouse model of PDAC-DM dramatically decreased from 6.9 ± 0.1 to 5.6 ± 0.1 mmol/l in 2-4 days (p < 0.01). CONCLUSIONS/INTERPRETATION: We found that acute inflammation was involved in the pathogenesis of PDAC-DM. We contend that acute inflammation is a potential target for the diagnosis and treatment of PDAC-DM.

Genome-wide analysis of transcription factor binding sites and their characteristic DNA structures.

BACKGROUND: Transcription factors (TF) regulate gene expression by binding DNA regulatory regions. Transcription factor binding sites (TFBSs) are conserved not only in primary DNA sequences but also in DNA structures. However, the global relationship between TFs and their preferred DNA structures remains to be elucidated. RESULTS: In this paper, we have developed a computational method to generate a genome-wide landscape of TFs and their characteristic binding DNA structures in Saccharomyces cerevisiae. We revealed DNA structural features for different TFs. The structural conservation shows positional preference in TFBSs. Structural levels of DNA sequences are correlated with TF-DNA binding affinities. CONCLUSIONS: We provided the genome-wide correspondences of TFs to DNA structures. Our findings will have implications in understanding TF regulatory mechanisms.

Neighboring genes show interchromosomal colocalization after their separation.

The order of genes on eukaryotic chromosomes is nonrandom. Some neighboring genes show order conservation among species, while some neighboring genes separate during evolution. Here, we investigated whether neighboring genes show interactions after their separation. We found that neighboring gene pairs tend to show interchromosomal colocalization (i.e., nuclear colocalization) in the species in which they separate. These nuclear colocalized separated neighboring gene pairs 1) show neighborhood conservation in more species, 2) tend to be regulated by the same transcription factor, and 3) tend to be regulated by the same histone modification. These results suggest a mechanism by which neighboring genes could retain nuclear proximity after their separation.

The pattern and evolution of looped gene bendability.

Gene looping, defined as the physical interaction between the promoter and terminator regions of a RNA polymerase II-transcribed gene, is widespread in yeast and mammalian cells. Gene looping has been shown to play important roles in transcription. Gene-loop formation is dependent on regulatory proteins localized at the 5' and 3' ends of genes, such as TFIIB. However, whether other factors contribute to gene looping remains to be elucidated. Here, we investigated the contribution of intrinsic DNA and chromatin structures to gene looping. We found that Saccharomyces cerevisiae looped genes show high DNA bendability around middle and 3/4 regions in open reading frames (ORFs). This bendability pattern is conserved between yeast species, whereas the position of bendability peak varies substantially among species. Looped genes in human cells also show high DNA bendability. Nucleosome positioning around looped ORF middle regions is unstable. We also present evidence indicating that this unstable nucleosome positioning is involved in gene looping. These results suggest a mechanism by which DNA bendability and unstable nucleosome positioning could assist in the formation of gene loops.

Nuclear colocalization of transcription factor target genes strengthens coregulation in yeast.

Eukaryotic chromosomes are not randomly distributed in the interphase nucleus, but instead occupy distinct territories. Nonetheless, the genome-wide relationships of gene regulation to gene nuclear location remain poorly understood in yeast. In the three-dimensional view of gene regulation, we found that a considerable number of transcription factors (TFs) regulate genes that are colocalized in the nucleus. Colocalized TF target genes are more strongly coregulated compared with the other TF target genes. Target genes of chromatin regulators are also colocalized. These results demonstrate that colocalization of coregulated genes is a common process, and three-dimensional gene positioning is an important part of gene regulation. Our findings will have implications in understanding nuclear architecture and function.

Antisense transcription is coupled to nucleosome occupancy in sense promoters.

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.

Older Siblings' Contributions to Young Child's Cognitive Skills.

This work finds that older siblings as well as early parenting influence young children's cognitive skills directly or indirectly, for example, Mathematics, and English. Our findings challenge a pervasive view in the economical literatures that early parenting play a dominant role in explaining child development. In economics, early environmental conditions are important to demonstrate the evolution of adolescent and adult cognitive skills (Knudsen, Heckman, Cameron, and Shonkoff, 2006; Cunha and Heckman, 2007), and it establishes causal impacts of early parental inputs and other environmental factors on cognitive and non-cognitive skills (Heckman, Stixrud, and Urzua, 2006; Borghans, Duckworth, Heckman, and Weel, 2006; Cunha, Heckman, and Schennach, 2010). Early parenting as well as older siblings should explain a diverse array of academic and social outcomes, for example, Mathematics, English, maritage and pregnancy. In fact, older siblings' characteristics are as important, if not more important, than parenting for child development. Our analysis addresses the problems of measurement error, imperfect proxies, and reverse causality that plague conventional approach in psychology. We find that older brother contributes much more than older sister to child's mathematical achievement, while older sister contributes much more to child's english achievement. Our evidence is consistent with psychology literature, for example, Hetherington (1988), Jenkins (1992), Zukow-Goldring (1995), Marshall, Garcia-Coll, Marx, McCartney, Keffe, and Rub (1997), Maynard (2002), and Brody Ge, Kim, Murry, Simons, Gibbons, Gerrard, and Conger (2003) for siblings' direct contributions to child development, Bronfenbrenner (1997), East (1998), Whiteman and Buchanan (2002), and Brody, Ge, Kim, Murry, Simons, Gibbons, Gerrard, and Conger (2003) for siblings's indirect contributions, and Reiss, Neiderhiser, Hetherington, and Plomin (2000), Feinberg and Hetherington (2001), Kowal, Kramer, Krull, and Crick (2002) for parental differential treatment.

The mechanism accounting for DNA damage strength modulation of p53 dynamical properties.

The P53 protein levels exhibit a series of pulses in response to DNA double-stranded breaks (DSBs). However, the mechanism regarding how damage strength regulates physical parameters of p53 pulses remains to be elucidated. This paper established two mathematical models translating the mechanism of p53 dynamics in response to DSBs; the two models can reproduce many results observed in the experiments. Based on the models, numerical analysis suggested that the interval between pulses increases as the damage strength decreases, and we proposed that the p53 dynamical system in response to DSBs is modulated by frequency. Next, we found that the ATM positive self-feedback can realize the system characteristic that the pulse amplitude is independent of the damage strength. In addition, the pulse interval is negatively correlated with apoptosis; the greater the damage strength, the smaller the pulse interval, the faster the p53 accumulation rate, and the cells are more susceptible to apoptosis. These findings advance our understanding of the mechanism of p53 dynamical response and give new insights for experiments to probe the dynamics of p53 signaling.

Deep learning-based recurrence detector on magnetic resonance scans in nasopharyngeal carcinoma: A multicenter study.

OBJECTIVES: Accuracy in the detection of recurrent nasopharyngeal carcinoma (NPC) on follow-up magnetic resonance (MR) scans needs to be improved. MATERIAL AND METHODS: A total of 5 035 follow-up MR scans from 5 035 survivors with treated NPC between April 2007 and July 2020 were retrospectively collected from three cancer centers for developing and evaluating the deep learning (DL) model MODERN (MR-based Deep learning model for dEtecting Recurrent Nasopharyngeal carcinoma). In a reader study with 220 scans, the accuracy of two radiologists in detecting recurrence on scans with vs without MODERN was evaluated. The performance was measured using the area under the receiver operating characteristic curve (ROC-AUC) and accuracy with a 95% confidence interval (CI). RESULTS: MODERN exhibited sound performance in the validation cohort (internal: ROC-AUC, 0.88, 95% CI, 0.86-0.90; external 1: ROC-AUC, 0.88, 95% CI, 0.86-0.90; external 2: ROC-AUC, 0.85, 95% CI, 0.82-0.88). In a reader study, MODERN alone achieved reliable accuracy compared to that of radiologists (MODERN: 84.1%, 95% CI, 79.3%-88.9%; competent: 80.9%, 95% CI, 75.7%-86.1%, P < 0.001; expert: 85.9%, 95% CI, 81.3%-90.5%, P < 0.001). The accuracy of radiologists was boosted by the MODERN score (competent with MODERN score: 84.6%, 95% CI, 79.8%-89.3%, P < 0.001; expert with MODERN score: 87.7%, 95% CI, 83.4%-92.1%, P < 0.001). CONCLUSION: We developed a DL model for recurrence detection with reliable performance. Computer-human collaboration has the potential to refine the workflow in interpreting surveillant MR scans among patients with treated NPC.

Genome-wide DNA sequence polymorphisms facilitate nucleosome positioning in yeast.

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: zhimdai@gmail.com; issdxh@mail.sysu.edu.cn

Gene expression divergence is coupled to evolution of DNA structure in coding regions.

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.

The Influence of Culture Capital, Social Security, and Living Conditions on Children's Cognitive Ability: Evidence from 2018 China Family Panel Studies.

The aim of this study was to analyze the influence of economic capital, culture capital, social capital, social security, and living conditions on children's cognitive ability. However, most studies only focus on the impact of family socio-economic status/culture capital on children's cognitive ability by ordinary least squares regression analysis. To this end, we used the data from the China Family Panel Studies in 2018 and applied proxy variable, instrumental variables, and two-stage least squares regression analysis with a total of 2647 samples with ages from 6 to 16. The results showed that family education, education expectation, books, education participation, social communication, and tap water had a positive impact on both the Chinese and math cognitive ability of children, while children's age, gender, and family size had a negative impact on cognitive ability, and the impact of genes was attenuated by family capital. In addition, these results are robust, and the heterogeneity was found for gender and urban location. Specifically, in terms of gender, the culture, social capital, and social security are more sensitive to the cognitive ability of girls, while living conditions are more sensitive to the cognitive ability of boys. In urban locations, the culture and social capital are more sensitive to rural children's cognitive ability, while the social security and living conditions are more sensitive to urban children's cognitive ability. These findings provide theoretical support to further narrow the cognitive differences between children from many aspects, which allows social security and living conditions to be valued.

SAAED: Embedding and Deep Learning Enhance Accurate Prediction of Association Between circRNA and Disease.

Emerging evidence indicates that circRNA can regulate various diseases. However, the mechanisms of circRNA in these diseases have not been fully understood. Therefore, detecting potential circRNA-disease associations has far-reaching significance for pathological development and treatment of these diseases. In recent years, deep learning models are used in association analysis of circRNA-disease, but a lack of circRNA-disease association data limits further improvement. Therefore, there is an urgent need to mine more semantic information from data. In this paper, we propose a novel method called Semantic Association Analysis by Embedding and Deep learning (SAAED), which consists of two parts, a neural network embedding model called Entity Relation Network (ERN) and a Pseudo-Siamese network (PSN) for analysis. ERN can fuse multiple sources of data and express the information with low-dimensional embedding vectors. PSN can extract the feature between circRNA and disease for the association analysis. CircRNA-disease, circRNA-miRNA, disease-gene, disease-miRNA, disease-lncRNA, and disease-drug association information are used in this paper. More association data can be introduced for analysis without restriction. Based on the CircR2Disease benchmark dataset for evaluation, a fivefold cross-validation experiment showed an AUC of 98.92%, an accuracy of 95.39%, and a sensitivity of 93.06%. Compared with other state-of-the-art models, SAAED achieves the best overall performance. SAAED can expand the expression of the biological related information and is an efficient method for predicting potential circRNA-disease association.

Impact of Education, Medical Services, and Living Conditions on Health: Evidence from China Health and Nutrition Survey.

Education, medical services, and living conditions can influence individual health and health literacy. We used the 2015 China Health and Nutrition Survey data to analyze the impact of education, medical services, and living conditions on individual health by extending the Grossman model. As a result, using the instrumental variable (read, write, and draw) two-stage least square method, we found that education, medical services, and living conditions have a positive impact on individual health, both physical health and psychological health. Among them, medical services have the largest influence, followed by living conditions and education. In addition, the results are robust. However, individual characteristics, family income, and working status also affect individual health. Moreover, we observed heterogeneity in age, sex, and residence in the impact of education, medical services, and living conditions on individual health. In particular, the health of the rural elderly and elderly women is more sensitive to education, the medical services of middle-aged women and young men have a greater impact on their health, and the living conditions of the rural elderly and youth have a greater impact on their health. All the findings are helpful for optimizing the path of the Healthy China program.

The Impact of Social Capital on Mental Health: Evidence from the China Family Panel Survey.

The influence of social capital on mental health is a controversial topic. As some studies have pointed out, cognitive social capital significantly affects mental health but structural social capital does not. Using data from the China Family Panel Survey, this study measured social capital from social help, social trust, social networks, and social participation, and took regional average level of social capital as the instrumental variables, and applied a two-stage least squares regression. We found that the mental health of residents who trust and help each other is significantly higher than that of residents without trust and mutual help. When residents' efforts to maintain social networks increase, their mental health significantly improves. These results are robust. Furthermore, the impact of social capital on mental health was heterogeneous in terms of urbanicity, gender, age, and area. These results are helpful for making policies for promoting residents' mental health.

Prediction of CRISPR/Cas9 single guide RNA cleavage efficiency and specificity by attention-based convolutional neural networks.

CRISPR/Cas9 is a preferred genome editing tool and has been widely adapted to ranges of disciplines, from molecular biology to gene therapy. A key prerequisite for the success of CRISPR/Cas9 is its capacity to distinguish between single guide RNAs (sgRNAs) on target and homologous off-target sites. Thus, optimized design of sgRNAs by maximizing their on-target activity and minimizing their potential off-target mutations are crucial concerns for this system. Several deep learning models have been developed for comprehensive understanding of sgRNA cleavage efficacy and specificity. Although the proposed methods yield the performance results by automatically learning a suitable representation from the input data, there is still room for the improvement of accuracy and interpretability. Here, we propose novel interpretable attention-based convolutional neural networks, namely CRISPR-ONT and CRISPR-OFFT, for the prediction of CRISPR/Cas9 sgRNA on- and off-target activities, respectively. Experimental tests on public datasets demonstrate that our models significantly yield satisfactory results in terms of accuracy and interpretability. Our findings contribute to the understanding of how RNA-guide Cas9 nucleases scan the mammalian genome. Data and source codes are available at https://github.com/Peppags/CRISPRont-CRISPRofft.