bluuebunny/crossref_metadata_2025 huggingface dataset:
Mild cognitive impairment understanding: an empirical study by data-driven approach
Authors: Liu Liyuan , Yu Bingchen , Han Meng , Yuan Shanshan , Wang Na
Published: December 2019

Background

Cognitive decline has emerged as a significant threat to both public health and personal welfare, and mild cognitive decline/impairment (MCI) can further develop into Dementia/Alzheimer’s disease. While treatment of Dementia/Alzheimer’s disease can be expensive and ineffective sometimes, the prevention of MCI by identifying modifiable risk factors is a complementary and effective strategy.

Results

In this study, based on the data collected by Centers for Disease Control and Prevention (CDC) through the nationwide telephone survey, we apply a data-driven approach to re-exam the previously founded risk factors and discover new risk factors. We found that depression, physical health, cigarette usage, education level, and sleep time play an important role in cognitive decline, which is consistent with the previous discovery. Besides that, the first time, we point out that other factors such as arthritis, pulmonary disease, stroke, asthma, marital status also contribute to MCI risk, which is less exploited previously. We also incorporate some machine learning and deep learning algorithms to weigh the importance of various factors contributed to MCI and predicted cognitive declined.

Conclusion

By incorporating the data-driven approach, we can determine that risk factors significantly correlated with diseases. These correlations could also be expanded to another medical diagnosis besides MCI.
DDIGIP: predicting drug-drug interactions based on Gaussian interaction profile kernels
Authors: Yan Cheng , Duan Guihua , Pan Yi , Wu Fang-Xiang , Wang Jianxin
Published: December 2019

Background

A drug-drug interaction (DDI) is defined as a drug effect modified by another drug, which is very common in treating complex diseases such as cancer. Many studies have evidenced that some DDIs could be an increase or a decrease of the drug effect. However, the adverse DDIs maybe result in severe morbidity and even morality of patients, which also cause some drugs to withdraw from the market. As the multi-drug treatment becomes more and more common, identifying the potential DDIs has become the key issue in drug development and disease treatment. However, traditional biological experimental methods, including in vitro and vivo, are very time-consuming and expensive to validate new DDIs. With the development of high-throughput sequencing technology, many pharmaceutical studies and various bioinformatics data provide unprecedented opportunities to study DDIs.

Result

In this study, we propose a method to predict new DDIs, namely DDIGIP, which is based on Gaussian Interaction Profile (GIP) kernel on the drug-drug interaction profiles and the Regularized Least Squares (RLS) classifier. In addition, we also use the k-nearest neighbors (KNN) to calculate the initial relational score in the presence of new drugs via the chemical, biological, phenotypic data of drugs. We compare the prediction performance of DDIGIP with other competing methods via the 5-fold cross validation, 10-cross validation and de novo drug validation.

Conlusion

In 5-fold cross validation and 10-cross validation, DDRGIP method achieves the area under the ROC curve (AUC) of 0.9600 and 0.9636 which are better than state-of-the-art method (L1 Classifier ensemble method) of 0.9570 and 0.9599. Furthermore, for new drugs, the AUC value of DDIGIP in de novo drug validation reaches 0.9262 which also outperforms the other state-of-the-art method (Weighted average ensemble method) of 0.9073. Case studies and these results demonstrate that DDRGIP is an effective method to predict DDIs while being beneficial to drug development and disease treatment.
Drug repositioning based on individual bi-random walks on a heterogeneous network
Authors: Wang Yuehui , Guo Maozu , Ren Yazhou , Jia Lianyin , Yu Guoxian
Published: December 2019

Background

Traditional drug research and development is high cost, time-consuming and risky. Computationally identifying new indications for existing drugs, referred as drug repositioning, greatly reduces the cost and attracts ever-increasing research interests. Many network-based methods have been proposed for drug repositioning and most of them apply random walk on a heterogeneous network consisted with disease and drug nodes. However, these methods generally adopt the same walk-length for all nodes, and ignore the different contributions of different nodes.

Results

In this study, we propose a drug repositioning approach based on individual bi-random walks (DR-IBRW) on the heterogeneous network. DR-IBRW firstly quantifies the individual work-length of random walks for each node based on the network topology and knowledge that similar drugs tend to be associated with similar diseases. To account for the inner structural difference of the heterogeneous network, it performs bi-random walks with the quantified walk-lengths, and thus to identify new indications for approved drugs. Empirical study on public datasets shows that DR-IBRW achieves a much better drug repositioning performance than other related competitive methods.

Conclusions

Using individual random walk-lengths for different nodes of heterogeneous network indeed boosts the repositioning performance. DR-IBRW can be easily generalized to prioritize links between nodes of a network.
Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis
Authors: Geddes Thomas A. , Kim Taiyun , Nan Lihao , Burchfield James G. , Yang Jean Y. H. , Tao Dacheng , Yang Pengyi
Published: December 2019

Background

Single-cell RNA-sequencing (scRNA-seq) is a transformative technology, allowing global transcriptomes of individual cells to be profiled with high accuracy. An essential task in scRNA-seq data analysis is the identification of cell types from complex samples or tissues profiled in an experiment. To this end, clustering has become a key computational technique for grouping cells based on their transcriptome profiles, enabling subsequent cell type identification from each cluster of cells. Due to the high feature-dimensionality of the transcriptome (i.e. the large number of measured genes in each cell) and because only a small fraction of genes are cell type-specific and therefore informative for generating cell type-specific clusters, clustering directly on the original feature/gene dimension may lead to uninformative clusters and hinder correct cell type identification.

Results

Here, we propose an autoencoder-based cluster ensemble framework in which we first take random subspace projections from the data, then compress each random projection to a low-dimensional space using an autoencoder artificial neural network, and finally apply ensemble clustering across all encoded datasets to generate clusters of cells. We employ four evaluation metrics to benchmark clustering performance and our experiments demonstrate that the proposed autoencoder-based cluster ensemble can lead to substantially improved cell type-specific clusters when applied with both the standard k-means clustering algorithm and a state-of-the-art kernel-based clustering algorithm (SIMLR) designed specifically for scRNA-seq data. Compared to directly using these clustering algorithms on the original datasets, the performance improvement in some cases is up to 100%, depending on the evaluation metric used.

Conclusions

Our results suggest that the proposed framework can facilitate more accurate cell type identification as well as other downstream analyses. The code for creating the proposed autoencoder-based cluster ensemble framework is freely available from https://github.com/gedcom/scCCESS
DEEPSEN: a convolutional neural network based method for super-enhancer prediction
Authors: Bu Hongda , Hao Jiaqi , Gan Yanglan , Zhou Shuigeng , Guan Jihong
Published: December 2019

Background

Super-enhancers (SEs) are clusters of transcriptional active enhancers, which dictate the expression of genes defining cell identity and play an important role in the development and progression of tumors and other diseases. Many key cancer oncogenes are driven by super-enhancers, and the mutations associated with common diseases such as Alzheimer’s disease are significantly enriched with super-enhancers. Super-enhancers have shown great potential for the identification of key oncogenes and the discovery of disease-associated mutational sites.

Results

In this paper, we propose a new computational method called DEEPSEN for predicting super-enhancers based on convolutional neural network. The proposed method integrates 36 kinds of features. Compared with existing approaches, our method performs better and can be used for genome-wide prediction of super-enhancers. Besides, we screen important features for predicting super-enhancers.

Conclusion

Convolutional neural network is effective in boosting the performance of super-enhancer prediction.
Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization
Authors: Huang Jiang , Wu Min , Lu Fan , Ou-Yang Le , Zhu Zexuan
Published: December 2019

Background

Synthetic lethality has attracted a lot of attentions in cancer therapeutics due to its utility in identifying new anticancer drug targets. Identifying synthetic lethal (SL) interactions is the key step towards the exploration of synthetic lethality in cancer treatment. However, biological experiments are faced with many challenges when identifying synthetic lethal interactions. Thus, it is necessary to develop computational methods which could serve as useful complements to biological experiments.

Results

In this paper, we propose a novel graph regularized self-representative matrix factorization (GRSMF) algorithm for synthetic lethal interaction prediction. GRSMF first learns the self-representations from the known SL interactions and further integrates the functional similarities among genes derived from Gene Ontology (GO). It can then effectively predict potential SL interactions by leveraging the information provided by known SL interactions and functional annotations of genes. Extensive experiments on the synthetic lethal interaction data downloaded from SynLethDB database demonstrate the superiority of our GRSMF in predicting potential synthetic lethal interactions, compared with other competing methods. Moreover, case studies of novel interactions are conducted in this paper for further evaluating the effectiveness of GRSMF in synthetic lethal interaction prediction.

Conclusions

In this paper, we demonstrate that by adaptively exploiting the self-representation of original SL interaction data, and utilizing functional similarities among genes to enhance the learning of self-representation matrix, our GRSMF could predict potential SL interactions more accurately than other state-of-the-art SL interaction prediction methods.
scDC: single cell differential composition analysis
Authors: Cao Yue , Lin Yingxin , Ormerod John T. , Yang Pengyi , Yang Jean Y.H. , Lo Kitty K.
Published: December 2019

Background

Differences in cell-type composition across subjects and conditions often carry biological significance. Recent advancements in single cell sequencing technologies enable cell-types to be identified at the single cell level, and as a result, cell-type composition of tissues can now be studied in exquisite detail. However, a number of challenges remain with cell-type composition analysis – none of the existing methods can identify cell-type perfectly and variability related to cell sampling exists in any single cell experiment. This necessitates the development of method for estimating uncertainty in cell-type composition.

Results

We developed a novel single cell differential composition (scDC) analysis method that performs differential cell-type composition analysis via bootstrap resampling. scDC captures the uncertainty associated with cell-type proportions of each subject via bias-corrected and accelerated bootstrap confidence intervals. We assessed the performance of our method using a number of simulated datasets and synthetic datasets curated from publicly available single cell datasets. In simulated datasets, scDC correctly recovered the true cell-type proportions. In synthetic datasets, the cell-type compositions returned by scDC were highly concordant with reference cell-type compositions from the original data. Since the majority of datasets tested in this study have only 2 to 5 subjects per condition, the addition of confidence intervals enabled better comparisons of compositional differences between subjects and across conditions.

Conclusions

scDC is a novel statistical method for performing differential cell-type composition analysis for scRNA-seq data. It uses bootstrap resampling to estimate the standard errors associated with cell-type proportion estimates and performs significance testing through GLM and GLMM models. We have made this method available to the scientific community as part of thescdneypackage (SingleCellData Integrative Analysis) R package, available fromhttps://github.com/SydneyBioX/scdney.
DDI-PULearn: a positive-unlabeled learning method for large-scale prediction of drug-drug interactions
Authors: Zheng Yi , Peng Hui , Zhang Xiaocai , Zhao Zhixun , Gao Xiaoying , Li Jinyan
Published: December 2019

Background

Drug-drug interactions (DDIs) are a major concern in patients’ medication. It’s unfeasible to identify all potential DDIs using experimental methods which are time-consuming and expensive. Computational methods provide an effective strategy, however, facing challenges due to the lack of experimentally verified negative samples.

Results

To address this problem, we propose a novel positive-unlabeled learning method named DDI-PULearn for large-scale drug-drug-interaction predictions. DDI-PULearn first generates seeds of reliable negatives via OCSVM (one-class support vector machine) under a high-recall constraint and via the cosine-similarity based KNN (k-nearest neighbors) as well. Then trained with all the labeled positives (i.e., the validated DDIs) and the generated seed negatives, DDI-PULearn employs an iterative SVM to identify a set of entire reliable negatives from the unlabeled samples (i.e., the unobserved DDIs). Following that, DDI-PULearn represents all the labeled positives and the identified negatives as vectors of abundant drug properties by a similarity-based method. Finally, DDI-PULearn transforms these vectors into a lower-dimensional space via PCA (principal component analysis) and utilizes the compressed vectors as input for binary classifications. The performance of DDI-PULearn is evaluated on simulative prediction for 149,878 possible interactions between 548 drugs, comparing with two baseline methods and five state-of-the-art methods. Related experiment results show that the proposed method for the representation of DDIs characterizes them accurately. DDI-PULearn achieves superior performance owing to the identified reliable negatives, outperforming all other methods significantly. In addition, the predicted novel DDIs suggest that DDI-PULearn is capable to identify novel DDIs.

Conclusions

The results demonstrate that positive-unlabeled learning paves a new way to tackle the problem caused by the lack of experimentally verified negatives in the computational prediction of DDIs.
Research on predicting 2D-HP protein folding using reinforcement learning with full state space
Authors: Wu Hongjie , Yang Ru , Fu Qiming , Chen Jianping , Lu Weizhong , Li Haiou
Published: December 2019

Background

Protein structure prediction has always been an important issue in bioinformatics. Prediction of the two-dimensional structure of proteins based on the hydrophobic polarity model is a typical non-deterministic polynomial hard problem. Currently reported hydrophobic polarity model optimization methods, greedy method, brute-force method, and genetic algorithm usually cannot converge robustly to the lowest energy conformations. Reinforcement learning with the advantages of continuous Markov optimal decision-making and maximizing global cumulative return is especially suitable for solving global optimization problems of biological sequences.

Results

In this study, we proposed a novel hydrophobic polarity model optimization method derived from reinforcement learning which structured the full state space, and designed an energy-based reward function and a rigid overlap detection rule. To validate the performance, sixteen sequences were selected from the classical data set. The results indicated that reinforcement learning with full states successfully converged to the lowest energy conformations against all sequences, while the reinforcement learning with partial states folded 50% sequences to the lowest energy conformations. Reinforcement learning with full states hits the lowest energy on an average 5 times, which is 40 and 100% higher than the three and zero hit by the greedy algorithm and reinforcement learning with partial states respectively in the last 100 episodes.

Conclusions

Our results indicate that reinforcement learning with full states is a powerful method for predicting two-dimensional hydrophobic-polarity protein structure. It has obvious competitive advantages compared with greedy algorithm and reinforcement learning with partial states.
RCMF: a robust collaborative matrix factorization method to predict miRNA-disease associations
Authors: Cui Zhen , Liu Jin-Xing , Gao Ying-Lian , Zheng Chun-Hou , Wang Juan
Published: December 2019

Background

Predicting miRNA-disease associations (MDAs) is time-consuming and expensive. It is imminent to improve the accuracy of prediction results. So it is crucial to develop a novel computing technology to predict new MDAs. Although some existing methods can effectively predict novel MDAs, there are still some shortcomings. Especially when the disease matrix is processed, its sparsity is an important factor affecting the final results.

Results

A robust collaborative matrix factorization (RCMF) is proposed to predict novel MDAs. The L2,1-norm are introduced to our method to achieve the highest AUC value than other advanced methods.

Conclusions

5-fold cross validation is used to evaluate our method, and simulation experiments are used to predict novel associations on Gold Standard Dataset. Finally, our prediction accuracy is better than other existing advanced methods. Therefore, our approach is effective and feasible in predicting novel MDAs.
BIN1 rs744373 variant shows different association with Alzheimer’s disease in Caucasian and Asian populations
Authors: Han Zhifa , Wang Tao , Tian Rui , Zhou Wenyang , Wang Pingping , Ren Peng , Zong Jian , Hu Yang , Jin Shuilin , Jiang Qinghua
Published: December 2019

Background

The association between BIN1 rs744373 variant and Alzheimer’s disease (AD) had been identified by genome-wide association studies (GWASs) as well as candidate gene studies in Caucasian populations. But in East Asian populations, both positive and negative results had been identified by association studies. Considering the smaller sample sizes of the studies in East Asian, we believe that the results did not have enough statistical power.

Results

We conducted a meta-analysis with 71,168 samples (22,395 AD cases and 48,773 controls, from 37 studies of 19 articles). Based on the additive model, we observed significant genetic heterogeneities in pooled populations as well as Caucasians and East Asians. We identified a significant association between rs744373 polymorphism with AD in pooled populations (P = 5 × 10− 07, odds ratio (OR) = 1.12, and 95% confidence interval (CI) 1.07–1.17) and in Caucasian populations (P = 3.38 × 10− 08, OR = 1.16, 95% CI 1.10–1.22). But in the East Asian populations, the association was not identified (P = 0.393, OR = 1.057, and 95% CI 0.95–1.15). Besides, the regression analysis suggested no significant publication bias. The results for sensitivity analysis as well as meta-analysis under the dominant model and recessive model remained consistent, which demonstrated the reliability of our finding.

Conclusions

The large-scale meta-analysis highlighted the significant association between rs744373 polymorphism and AD risk in Caucasian populations but not in the East Asian populations.
How to balance the bioinformatics data: pseudo-negative sampling
Authors: Zhang Yongqing , Qiao Shaojie , Lu Rongzhao , Han Nan , Liu Dingxiang , Zhou Jiliu
Published: December 2019

Background

Imbalanced datasets are commonly encountered in bioinformatics classification problems, that is, the number of negative samples is much larger than that of positive samples. Particularly, the data imbalance phenomena will make us underestimate the performance of the minority class of positive samples. Therefore, how to balance the bioinformatic data becomes a very challenging and difficult problem.

Results

In this study, we propose a new data sampling approach, called pseudo-negative sampling, which can be effectively applied to handle the case that: negative samples greatly dominate positive samples. Specifically, we design a supervised learning method based on a max-relevance min-redundancy criterion beyond Pearson correlation coefficient (MMPCC), which is used to choose pseudo-negative samples from the negative samples and view them as positive samples. In addition, MMPCC uses an incremental searching technique to select optimal pseudo-negative samples to reduce the computation cost. Consequently, the discovered pseudo-negative samples have strong relevance to positive samples and less redundancy to negative ones.

Conclusions

To validate the performance of our method, we conduct experiments base on four UCI datasets and three real bioinformatics datasets. According to the experimental results, we clearly observe the performance of MMPCC is better than other sampling methods in terms of Sensitivity, Specificity, Accuracy and the Mathew’s Correlation Coefficient. This reveals that the pseudo-negative samples are particularly helpful to solve the imbalance dataset problem. Moreover, the gain of Sensitivity from the minority samples with pseudo-negative samples grows with the improvement of prediction accuracy on all dataset.
Pushing the accuracy limit of shape complementarity for protein-protein docking
Authors: Yan Yumeng , Huang Sheng-You
Published: December 2019

Background

Protein-protein docking is a valuable computational approach for investigating protein-protein interactions. Shape complementarity is the most basic component of a scoring function and plays an important role in protein-protein docking. Despite significant progresses, shape representation remains an open question in the development of protein-protein docking algorithms, especially for grid-based docking approaches.

Results

We have proposed a new pairwise shape-based scoring function (LSC) for protein-protein docking which adopts an exponential form to take into account long-range interactions between protein atoms. The LSC scoring function was incorporated into our FFT-based docking program and evaluated for both bound and unbound docking on the protein docking benchmark 4.0. It was shown that our LSC achieved a significantly better performance than four other similar docking methods, ZDOCK 2.1, MolFit/G, GRAMM, and FTDock/G, in both success rate and number of hits. When considering the top 10 predictions, LSC obtained a success rate of 51.71% and 6.82% for bound and unbound docking, respectively, compared to 42.61% and 4.55% for the second-best program ZDOCK 2.1. LSC also yielded an average of 8.38 and 3.94 hits per complex in the top 1000 predictions for bound and unbound docking, respectively, followed by 6.38 and 2.96 hits for the second-best ZDOCK 2.1.

Conclusions

The present LSC method will not only provide an initial-stage docking approach for post-docking processes but also have a general implementation for accurate representation of other energy terms on grids in protein-protein docking. The software has been implemented in our HDOCK web server athttp://hdock.phys.hust.edu.cn/.
Semi-supervised prediction of protein interaction sites from unlabeled sample information
Authors: Wang Ye , Mei Changqing , Zhou Yuming , Wang Yan , Zheng Chunhou , Zhen Xiao , Xiong Yan , Chen Peng , Zhang Jun , Wang Bing
Published: December 2019

Background

The recognition of protein interaction sites is of great significance in many biological processes, signaling pathways and drug designs. However, most sites on protein sequences cannot be defined as interface or non-interface sites because only a small part of protein interactions had been identified, which will cause the lack of prediction accuracy and generalization ability of predictors in protein interaction sites prediction. Therefore, it is necessary to effectively improve prediction performance of protein interaction sites using large amounts of unlabeled data together with small amounts of labeled data and background knowledge today.

Results

In this work, three semi-supervised support vector machine–based methods are proposed to improve the performance in the protein interaction sites prediction, in which the information of unlabeled protein sites can be involved. Herein, five features related with the evolutionary conservation of amino acids are extracted from HSSP database and Consurf Sever, i.e., residue spatial sequence spectrum, residue sequence information entropy and relative entropy, residue sequence conserved weight and residual Base evolution rate, to represent the residues within the protein sequence. Then three predictors are built for identifying the interface residues from protein surface using three types of semi-supervised support vector machine algorithms.

Conclusion

The experimental results demonstrated that the semi-supervised approaches can effectively improve prediction performance of protein interaction sites when unlabeled information is involved into the predictors and one of them can achieve the best prediction performance, i.e., the accuracy of 70.7%, the sensitivity of 62.67% and the specificity of 78.72%, respectively. With comparison to the existing studies, the semi-supervised models show the improvement of the predication performance.
Accurate classification of membrane protein types based on sequence and evolutionary information using deep learning
Authors: Guo Lei , Wang Shunfang , Li Mingyuan , Cao Zicheng
Published: December 2019

Background

Membrane proteins play an important role in the life activities of organisms. Knowing membrane protein types provides clues for understanding the structure and function of proteins. Though various computational methods for predicting membrane protein types have been developed, the results still do not meet the expectations of researchers.

Results

We propose two deep learning models to process sequence information and evolutionary information, respectively. Both models obtained better results than traditional machine learning models. Furthermore, to improve the performance of the sequence information model, we also provide a new vector representation method to replace the one-hot encoding, whose overall success rate improved by 3.81% and 6.55% on two datasets. Finally, a more effective model is obtained by fusing the above two models, whose overall success rate reached 95.68% and 92.98% on two datasets.

Conclusion

The final experimental results show that our method is more effective than existing methods for predicting membrane protein types, which can help laboratory researchers to identify the type of novel membrane proteins.
Rapid classification of group B Streptococcus serotypes based on matrix-assisted laser desorption ionization-time of flight mass spectrometry and machine learning techniques
Authors: Wang Hsin-Yao , Li Wen-Chi , Huang Kai-Yao , Chung Chia-Ru , Horng Jorng-Tzong , Hsu Jen-Fu , Lu Jang-Jih , Lee Tzong-Yi
Published: December 2019

Background

Group B streptococcus (GBS) is an important pathogen that is responsible for invasive infections, including sepsis and meningitis. GBS serotyping is an essential means for the investigation of possible infection outbreaks and can identify possible sources of infection. Although it is possible to determine GBS serotypes by either immuno-serotyping or geno-serotyping, both traditional methods are time-consuming and labor-intensive. In recent years, the matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been reported as an effective tool for the determination of GBS serotypes in a more rapid and accurate manner. Thus, this work aims to investigate GBS serotypes by incorporating machine learning techniques with MALDI-TOF MS to carry out the identification.

Results

In this study, a total of 787 GBS isolates, obtained from three research and teaching hospitals, were analyzed by MALDI-TOF MS, and the serotype of the GBS was determined by a geno-serotyping experiment. The peaks of mass-to-charge ratios were regarded as the attributes to characterize the various serotypes of GBS. Machine learning algorithms, such as support vector machine (SVM) and random forest (RF), were then used to construct predictive models for the five different serotypes (Types Ia, Ib, III, V, and VI). After optimization of feature selection and model generation based on training datasets, the accuracies of the selected models attained 54.9–87.1% for various serotypes based on independent testing data. Specifically, for the major serotypes, namely type III and type VI, the accuracies were 73.9 and 70.4%, respectively.

Conclusion

The proposed models have been adopted to implement a web-based tool (GBSTyper), which is now freely accessible at http://csb.cse.yzu.edu.tw/GBSTyper/, for providing efficient and effective detection of GBS serotypes based on a MALDI-TOF MS spectrum. Overall, this work has demonstrated that the combination of MALDI-TOF MS and machine intelligence could provide a practical means of clinical pathogen testing.
LePrimAlign: local entropy-based alignment of PPI networks to predict conserved modules
Authors: Maskey Sawal , Cho Young-Rae
Published: December 2019

Background

Cross-species analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved interaction patterns. Identifying such conserved substructures between PPI networks of different species increases our understanding of the principles deriving evolution of cellular organizations and their functions in a system level. In recent years, network alignment techniques have been applied to genome-scale PPI networks to predict evolutionary conserved modules. Although a wide variety of network alignment algorithms have been introduced, developing a scalable local network alignment algorithm with high accuracy is still challenging.

Results

We present a novel pairwise local network alignment algorithm, called LePrimAlign, to predict conserved modules between PPI networks of three different species. The proposed algorithm exploits the results of a pairwise global alignment algorithm with many-to-many node mapping. It also applies the concept of graph entropy to detect initial cluster pairs from two networks. Finally, the initial clusters are expanded to increase the local alignment score that is formulated by a combination of intra-network and inter-network scores. The performance comparison with state-of-the-art approaches demonstrates that the proposed algorithm outperforms in terms of accuracy of identified protein complexes and quality of alignments.

Conclusion

The proposed method produces local network alignment of higher accuracy in predicting conserved modules even with large biological networks at a reduced computational cost.
Improving CRISPR guide design with consensus approaches
Authors: Bradford Jacob , Perrin Dimitri
Published: December 2019

Background

CRISPR-based systems are playing an important role in modern genome engineering. A large number of computational methods have been developed to assist in the identification of suitable guides. However, there is only limited overlap between the guides that each tool identifies. This can motivate further development, but also raises the question of whether it is possible to combine existing tools to improve guide design.

Results

We considered nine leading guide design tools, and their output when tested using two sets of guides for which experimental validation data is available. We found that consensus approaches were able to outperform individual tools. The best performance (with a precision of up to 0.912) was obtained when combining four of the tools and accepting all guides selected by at least three of them.

Conclusions

These results can be used to improve CRISPR-based studies, but also to guide further tool development. However, they only provide a short-term solution as the time and computational resources required to run four tools may be impractical in certain applications.
scReClassify: post hoc cell type classification of single-cell rNA-seq data
Authors: Kim Taiyun , Lo Kitty , Geddes Thomas A. , Kim Hani Jieun , Yang Jean Yee Hwa , Yang Pengyi
Published: December 2019

Background

Single-cell RNA-sequencing (scRNA-seq) is a fast emerging technology allowing global transcriptome profiling on the single cell level. Cell type identification from scRNA-seq data is a critical task in a variety of research such as developmental biology, cell reprogramming, and cancers. Typically, cell type identification relies on human inspection using a combination of prior biological knowledge (e.g. marker genes and morphology) and computational techniques (e.g. PCA and clustering). Due to the incompleteness of our current knowledge and the subjectivity involved in this process, a small amount of cells may be subject to mislabelling.

Results

Here, we propose a semi-supervised learning framework, named scReClassify, for ‘post hoc’ cell type identification from scRNA-seq datasets. Starting from an initial cell type annotation with potentially mislabelled cells, scReClassify first performs dimension reduction using PCA and next applies a semi-supervised learning method to learn and subsequently reclassify cells that are likely mislabelled initially to the most probable cell types. By using both simulated and real-world experimental datasets that profiled various tissues and biological systems, we demonstrate that scReClassify is able to accurately identify and reclassify misclassified cells to their correct cell types.

Conclusions

scReClassify can be used for scRNA-seq data as a post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure. It is implemented as an R package and is freely available fromhttps://github.com/SydneyBioX/scReClassify
Identification of highly conserved, serotype-specific dengue virus sequences: implications for vaccine design
Authors: Chong Li Chuin , Khan Asif M.
Published: December 2019

Background

The sequence diversity of dengue virus (DENV) is one of the challenges in developing an effective vaccine against the virus. Highly conserved, serotype-specific (HCSS), immune-relevant DENV sequences are attractive candidates for vaccine design, and represent an alternative to the approach of selecting pan-DENV conserved sequences. The former aims to limit the number of possible cross-reactive epitope variants in the population, while the latter aims to limit the cross-reactivity between the serotypes to favour a serotype-specific response. Herein, we performed a large-scale systematic study to map and characterise HCSS sequences in the DENV proteome.

Methods

All reported DENV protein sequence data for each serotype was retrieved from the NCBI Entrez Protein (nr) Database (txid: 12637). The downloaded sequences were then separated according to the individual serotype proteins by use of BLASTp search, and subsequently removed for duplicates and co-aligned across the serotypes. Shannon’s entropy and mutual information (MI) analyses, by use of AVANA, were performed to measure the diversity within and between the serotype proteins to identify HCSS nonamers. The sequences were evaluated for the presence of promiscuous T-cell epitopes by use of NetCTLpan 1.1 and NetMHCIIpan 3.2 server for human leukocyte antigen (HLA) class I and class II supertypes, respectively. The predicted epitopes were matched to reported epitopes in the Immune Epitope Database.

Results

A total of 2321 nonamers met the HCSS selection criteria of entropy < 0.25 and MI > 0.8. Concatenating these resulted in a total of 337 HCSS sequences. DENV4 had the most number of HCSS nonamers; NS5, NS3 and E proteins had among the highest, with none in the C and only one in prM. The HCSS sequences were immune-relevant; 87 HCSS sequences were both reported T-cell epitopes/ligands in human and predicted epitopes, supporting the accuracy of the predictions. A number of the HCSS clustered as immunological hotspots and exhibited putative promiscuity beyond a single HLA supertype. The HCSS sequences represented, on average, ~ 40% of the proteome length for each serotype; more than double of pan-DENV sequences (conserved across the four serotypes), and thus offer a larger choice of sequences for vaccine target selection. HCSS sequences of a given serotype showed significant amino acid difference to all the variants of the other serotypes, supporting the notion of serotype-specificity.

Conclusion

This work provides a catalogue of HCSS sequences in the DENV proteome, as candidates for vaccine target selection. The methodology described herein provides a framework for similar application to other pathogens.
Genome-wide identification, phylogeny, and expression analysis of the SBP-box gene family in Euphorbiaceae
Authors: Li Jing , Gao Xiaoyang , Sang Shiye , Liu Changning
Published: December 2019

Background

Euphorbiaceae is one of the largest families of flowering plants. Due to its exceptional growth form diversity and near-cosmopolitan distribution, it has attracted much interest since ancient times. SBP-box (SBP) genes encode plant-specific transcription factors that play critical roles in numerous biological processes, especially flower development. We performed genome-wide identification and characterization of SBP genes from four economically important Euphorbiaceae species.

Results

In total, 77 SBP genes were identified in four Euphorbiaceae genomes. The SBP proteins were divided into three length ranges and 10 groups. Group-6 was absent in Arabidopsis thaliana but conserved in Euphorbiaceae. Segmental duplication played the most important role in the expansion processes of Euphorbiaceae SBP genes, and all the duplicated genes were subjected to purify selection. In addition, about two-thirds of the Euphorbiaceae SBP genes are potential targets of miR156, and some miR-regulated SBP genes exhibited high intensity expression and differential expression in different tissues. The expression profiles related to different stress treatments demonstrated broad involvement of Euphorbiaceae SBP genes in response to various abiotic factors and hormonal treatments.

Conclusions

In this study, 77 SBP genes were identified in four Euphorbiaceae species, and their phylogenetic relationships, protein physicochemical characteristics, duplication, tissue and stress response expression, and potential roles in Euphorbiaceae development were studied. This study lays a foundation for further studies of Euphorbiaceae SBP genes, providing valuable information for future functional exploration of Euphorbiaceae SBP genes.
The transcriptome variations of Panaxnotoginseng roots treated with different forms of nitrogen fertilizers
Authors: Ou Xiaohong , Li Shipeng , Liao Peiran , Cui Xiuming , Zheng Binglian , Yang Ye , Liu Dahui , Zheng Yun
Published: December 2019

Background

The sensitivity of plants to ammonia is a worldwide problem that limits crop production. Excessive use of ammonium as the sole nitrogen source results in morphological and physiological disorders, and retarded plant growth.

Results

In this study we found that the root growth ofPanax notoginsengwas inhibited when only adding ammonium nitrogen fertilizer, but the supplement of nitrate fertilizer recovered the integrity, activity and growth of root. Twelve RNA-seq profiles in four sample groups were produced and analyzed to identify deregulated genes in samples with different treatments. In comparisons to NH${~}_{4}^{+}$4+treated samples,ACLA-3gene is up-regulated in samples treated with NO${~}_{3}^{-}$3−and with both NH$_{4}^{+}$4+and NO${~}_{3}^{-}$3−, which is further validated by qRT-PCR in another set of samples. Subsequently, we show that the some key metabolites in the TCA cycle are also significantly enhanced when introducing NO${~}_{3}^{-}$3−. These potentially enhance the integrity and recover the growth ofPanax notoginsengroots.

Conclusion

These results suggest that the activated TCA cycle, as demonstrated by up-regulation ofACLA-3and several key metabolites in this cycle, contributes to the increasedPanax notoginsengroot yield when applying both ammonium and nitrate fertilizer.
Diet and irradiation effects on the bacterial community composition and structure in the gut of domesticated teneral and mature Queensland fruit fly, Bactrocera tryoni (Diptera: Tephritidae)
Authors: Woruba Deane N. , Morrow Jennifer L. , Reynolds Olivia L. , Chapman Toni A. , Collins Damian P. , Riegler Markus
Published: December 2019

Background

Mass-rearing, domestication and gamma irradiation of tephritid fruit flies used in sterile insect technique (SIT) programmes can negatively impact fly quality and performance. Symbiotic bacteria supplied as probiotics to mass-reared fruit flies may help to overcome some of these issues. However, the effects of tephritid ontogeny, sex, diet and irradiation on their microbiota are not well known.

Results

We have used next-generation sequencing to characterise the bacterial community composition and structure within Queensland fruit fly,Bactrocera tryoni(Froggatt),by generating 16S rRNA gene amplicon libraries derived from the guts of 58 individual teneral and mature, female and male, sterile and fertile adult flies reared on artificial larval diets in a laboratory or mass-rearing environment, and fed either a full adult diet (i.e. sugar and yeast hydrolysate) or a sugar only adult diet. Overall, the amplicon sequence read volume in tenerals was low and smaller than in mature adult flies. Operational taxonomic units (OTUs), belonging to the families Enterobacteriaceae (8 OTUs) and Acetobacteraceae (1 OTU) were most prevalent. Enterobacteriaceae dominated laboratory-reared tenerals from a colony fed a carrot-based larval diet, while Acetobacteraceae dominated mass-reared tenerals from a production facility colony fed a lucerne chaff based larval diet. As adult flies matured, Enterobacteriaceae became dominant irrespective of larval origin. The inclusion of yeast in the adult diet strengthened this shift away from Acetobacteraceae towards Enterobacteriaceae. Interestingly, irradiation increased 16S rRNA gene sequence read volume.

Conclusions

Our findings suggest that bacterial populations in fruit flies experience significant bottlenecks during metamorphosis. Gut bacteria in teneral flies were less abundant and less diverse, and impacted by colony origin. In contrast, mature adult flies had selectively increased abundances for some gut bacteria, or acquired these bacteria from the adult diet and environment. Furthermore, irradiation augmented bacterial abundance in mature flies. This implies that either some gut bacteria were compensating for damage caused by irradiation or irradiated flies had lost their ability to regulate bacterial load. Our findings suggest that the adult stage prior to sexual maturity may be ideal to target for probiotic manipulation of fly microbiota to increase fly performance in SIT programmes.
Correction to: Fetal macrosomia and its associated factors among singleton live-births in private clinics in Mekelle city, Tigray, Ethiopia
Authors: Tela Freweini Gebrearegay , Bezabih Afework Mulugeta , Adhanu Amaha Kahsay , Tekola Kidanemaryam Berhe
Published: December 2019

Following publication of the original article [1], we have been notified that the name of one author was spelled incorrectly as Kidanemariam Berhe Tekola, when the correct spelling is Kidanemaryam Berhe Tekola.
Neuropsychiatric decompensation in adolescents and adults with Phelan-McDermid syndrome: a systematic review of the literature
Authors: Kolevzon Alexander , Delaby Elsa , Berry-Kravis Elizabeth , Buxbaum Joseph D. , Betancur Catalina
Published: December 2019

Phelan-McDermid syndrome (PMS) is caused by haploinsufficiency of theSHANK3gene on chromosome 22q13.33 and is characterized by intellectual disability, hypotonia, severe speech impairments, and autism spectrum disorder. Emerging evidence indicates that there are changes over time in the phenotype observed in individuals with PMS, including severe neuropsychiatric symptoms and loss of skills occurring in adolescence and adulthood. To gain further insight into these phenomena and to better understand the long-term course of the disorder, we conducted a systematic literature review and identified 56 PMS cases showing signs of behavioral and neurologic decompensation in adolescence or adulthood (30 females, 25 males, 1 gender unknown). Clinical presentations included features of bipolar disorder, catatonia, psychosis, and loss of skills, occurring at a mean age of 20 years. There were no apparent sex differences in the rates of these disorders except for catatonia, which appeared to be more frequent in females (13 females, 3 males). Reports of individuals with point mutations inSHANK3exhibiting neuropsychiatric decompensation and loss of skills demonstrate that loss of one copy ofSHANK3is sufficient to cause these manifestations. In the majority of cases, no apparent cause could be identified; in others, symptoms appeared after acute events, such as infections, prolonged or particularly intense seizures, or changes in the individual’s environment. Several individuals had a progressive neurological deterioration, including one with juvenile onset metachromatic leukodystrophy, a severe demyelinating disorder caused by recessive mutations in theARSAgene in 22q13.33. These reports provide insights into treatment options that have proven helpful in some cases, and are reviewed herein. Our survey highlights how little is currently known about neuropsychiatric presentations and loss of skills in PMS and underscores the importance of studying the natural history in individuals with PMS, including both cross-sectional and long-term longitudinal analyses. Clearer delineation of these neuropsychiatric symptoms will contribute to their recognition and prompt management and will also help uncover the underlying biological mechanisms, potentially leading to improved interventions.