DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations.

Inferring gene regulatory networks (GRNs) allows us to obtain a deeper understanding of cellular function and disease pathogenesis. Recent advances in single-cell RNA sequencing (scRNA-seq) technology have improved the accuracy of GRN inference. However, many methods for inferring individual GRNs from scRNA-seq data are limited because they overlook intercellular heterogeneity and similarities between different cell subpopulations, which are often present in the data. Here, we propose a deep learning-based framework, DeepGRNCS, for jointly inferring GRNs across cell subpopulations. We follow the commonly accepted hypothesis that the expression of a target gene can be predicted based on the expression of transcription factors (TFs) due to underlying regulatory relationships. We initially processed scRNA-seq data by discretizing data scattering using the equal-width method. Then, we trained deep learning models to predict target gene expression from TFs. By individually removing each TF from the expression matrix, we used pre-trained deep model predictions to infer regulatory relationships between TFs and genes, thereby constructing the GRN. Our method outperforms existing GRN inference methods for various simulated and real scRNA-seq datasets. Finally, we applied DeepGRNCS to non-small cell lung cancer scRNA-seq data to identify key genes in each cell subpopulation and analyzed their biological relevance. In conclusion, DeepGRNCS effectively predicts cell subpopulation-specific GRNs. The source code is available at https://github.com/Nastume777/DeepGRNCS.

Cluster correlation based method for lncRNA-disease association prediction.

BACKGROUND: In recent years, increasing evidences have indicated that long non-coding RNAs (lncRNAs) are deeply involved in a wide range of human biological pathways. The mutations and disorders of lncRNAs are closely associated with many human diseases. Therefore, it is of great importance to predict potential associations between lncRNAs and complex diseases for the diagnosis and cure of complex diseases. However, the functional mechanisms of the majority of lncRNAs are still remain unclear. As a result, it remains a great challenge to predict potential associations between lncRNAs and diseases. RESULTS: Here, we proposed a new method to predict potential lncRNA-disease associations. First, we constructed a bipartite network based on known associations between diseases and lncRNAs/protein coding genes. Then the cluster association scores were calculated to evaluate the strength of the inner relationships between disease clusters and gene clusters. Finally, the gene-disease association scores are defined based on disease-gene cluster association scores and used to measure the strength for potential gene-disease associations. CONCLUSIONS: Leave-One Out Cross Validation (LOOCV) and 5-fold cross validation tests were implemented to evaluate the performance of our method. As a result, our method achieved reliable performance in the LOOCV (AUCs of 0.8169 and 0.8410 based on Yang's dataset and Lnc2cancer 2.0 database, respectively), and 5-fold cross validation (AUCs of 0.7573 and 0.8198 based on Yang's dataset and Lnc2cancer 2.0 database, respectively), which were significantly higher than the other three comparative methods. Furthermore, our method is simple and efficient. Only the known gene-disease associations are exploited in a graph manner and further new gene-disease associations can be easily incorporated in our model. The results for melanoma and ovarian cancer have been verified by other researches. The case studies indicated that our method can provide informative clues for further investigation.

C3: connect separate connected components to form a succinct disease module.

BACKGROUND: Precise disease module is conducive to understanding the molecular mechanism of disease causation and identifying drug targets. However, due to the fragmentization of disease module in incomplete human interactome, how to determine connectivity pattern and detect a complete neighbourhood of disease based on this is still an open question. RESULTS: In this paper, we perform exploratory analysis leading to an important observation that through a few intermediate nodes, most separate connected components formed by disease-associated proteins can be effectively connected and eventually form a complete disease module. And based on the topological properties of these intermediate nodes, we propose a connect separate connected components (C3) method to detect a succinct disease module by introducing a relatively small number of intermediate nodes, which allows us to obtain more pure disease module than other methods. Then we apply C3 across a large corpus of diseases to validate this connectivity pattern of disease module. Furthermore, the connectivity of the perturbed genes in multi-omics data such as The Cancer Genome Atlas also fits this pattern. CONCLUSIONS: C3 tool is not only useful in detecting a clearly-defined connected disease neighbourhood of 299 diseases and cancer with multi-omics data, but also helpful in better understanding the interconnection of phenotypically related genes in different omics data and studying complex pathological processes.

Genome-wide identification of the essential protein-coding genes and long non-coding RNAs for human pan-cancer.

MOTIVATION: Genome-scale CRISPR/Cas9 system has been a democratized gene editing technique and widely used to investigate gene functions in some biological processes and diseases especially cancers. Aiming to characterize gene aberrations and assess their effects on cancer, we designed a pipeline to identify the essential genes for pan-cancer. METHODS: CRISPR screening data were used to identify the essential genes that were collected from published data and integrated by Robust Rank Aggregation algorithm. Then, hypergeometrics test and random walks with restart (RWR) were used to predict additional essential genes on broader scale. Finally, the expression status and potential roles of these genes were explored based on TCGA portal and regulatory network analysis. RESULTS: We collected 926 samples from 10 CRISPR-based screening studies involving 33 different types of cancer to identify cancer-essential genes, which consists of 799 protein-coding genes (PCGs) and 97 long non-coding RNAs (lncRNAs). Then, we constructed a 'bi-colored' network with both PCGs and lncRNAs and applied it to predict additional essential genes including 495 PCGs and 280 lncRNAs on a broader scale using hypergeometrics test and RWR. After obtaining all essential genes, we further investigated their potential roles in cancer and found that essential genes have higher and more stable expression levels, and are associated with multiple cancer-associated biological processes and survival time. The regulatory network analysis detected two intriguing modules of essential genes participating in the regulation of cell cycle and ribosome biogenesis in cancer. AVAILABILITY AND IMPLEMENTATION: . SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Long non-coding RNAs function annotation: a global prediction method based on bi-colored networks.

More and more evidences demonstrate that the long non-coding RNAs (lncRNAs) play many key roles in diverse biological processes. There is a critical need to annotate the functions of increasing available lncRNAs. In this article, we try to apply a global network-based strategy to tackle this issue for the first time. We develop a bi-colored network based global function predictor, long non-coding RNA global function predictor ('lnc-GFP'), to predict probable functions for lncRNAs at large scale by integrating gene expression data and protein interaction data. The performance of lnc-GFP is evaluated on protein-coding and lncRNA genes. Cross-validation tests on protein-coding genes with known function annotations indicate that our method can achieve a precision up to 95%, with a suitable parameter setting. Among the 1713 lncRNAs in the bi-colored network, the 1625 (94.9%) lncRNAs in the maximum connected component are all functionally characterized. For the lncRNAs expressed in mouse embryo stem cells and neuronal cells, the inferred putative functions by our method highly match those in the known literature.

DeepCBS: shedding light on the impact of mutations occurring at CTCF binding sites.

CTCF-mediated chromatin loops create insulated neighborhoods that constrain promoter-enhancer interactions, serving as a unit of gene regulation. Disruption of the CTCF binding sites (CBS) will lead to the destruction of insulated neighborhoods, which in turn can cause dysregulation of the contained genes. In a recent study, it is found that CTCF/cohesin binding sites are a major mutational hotspot in the cancer genome. Mutations can affect CTCF binding, causing the disruption of insulated neighborhoods. And our analysis reveals a significant enrichment of well-known proto-oncogenes in insulated neighborhoods with mutations specifically occurring in anchor regions. It can be assumed that some mutations disrupt CTCF binding, leading to the disruption of insulated neighborhoods and subsequent activation of proto-oncogenes within these insulated neighborhoods. To explore the consequences of such mutations, we develop DeepCBS, a computational tool capable of analyzing mutations at CTCF binding sites, predicting their influence on insulated neighborhoods, and investigating the potential activation of proto-oncogenes. Futhermore, DeepCBS is applied to somatic mutation data of liver cancer. As a result, 87 mutations that disrupt CTCF binding sites are identified, which leads to the identification of 237 disrupted insulated neighborhoods containing a total of 135 genes. Integrative analysis of gene expression differences in liver cancer further highlights three genes: ARHGEF39, UBE2C and DQX1. Among them, ARHGEF39 and UBE2C have been reported in the literature as potential oncogenes involved in the development of liver cancer. The results indicate that DQX1 may be a potential oncogene in liver cancer and may contribute to tumor immune escape. In conclusion, DeepCBS is a promising method to analyze impacts of mutations occurring at CTCF binding sites on the insulator function of CTCF, with potential extensions to shed light on the effects of mutations on other functions of CTCF.

Residue and dissipation dynamics of flusilazole in apple and soil.

A simple, quick and reliable residue analytical method for flusilazole in apple and soil was developed in this study. The samples were extracted with acetonitrile and determined by liquid chromatography with UV detection. The LOQ of the method was 0.02 mg/kg. The dissipation dynamic and final residues of flusilazole in apple and soil were studied using field trial method. The results of residual dynamics experiment showed that after the apple was treated by flusilazole at treble of recommended high dosage (3.75 g/kg H(2)O), the half-life times of flusilazole in apple and soil were 4.23-7.77 days and 3.04-5.14 days, respectively. Residues of flusilazole in apple at harvest time were all below 0.05 mg/kg at both recommended high dosage and 1.5 times of recommended high dosage.

CBP-JMF: An Improved Joint Matrix Tri-Factorization Method for Characterizing Complex Biological Processes of Diseases.

Multi-omics molecules regulate complex biological processes (CBPs), which reflect the activities of various molecules in living organisms. Meanwhile, the applications to represent disease subtypes and cell types have created an urgent need for sample grouping and associated CBP-inferring tools. In this paper, we present CBP-JMF, a practical tool primarily for discovering CBPs, which underlie sample groups as disease subtypes in applications. Differently from existing methods, CBP-JMF is based on a joint non-negative matrix tri-factorization framework and is implemented in Python. As a pragmatic application, we apply CBP-JMF to identify CBPs for four subtypes of breast cancer. The result shows significant overlapping between genes extracted from CBPs and known subtype pathways. We verify the effectiveness of our tool in detecting CBPs that interpret subtypes of disease.

Chlormequat residues and dissipation rates in cotton crops and soil.

A simple and reliable analytical method for chlormequat residues in cotton and soil was established in this study. The residual levels and dissipation rates of chlormequat in cotton crops and soil were determined by High Performance Liquid Chromatography-Mass Spectroscopy (HPLC-MS). The limit of quantification (LOQ) was 0.05, 0.1, 0.1mg/kg for soil, cotton seeds and cotton leaves, respectively. The mean half-life of chlormequat was 4.47 days in cotton plants and was 4.34 days in soil. The final residues of chlormequat in cotton seeds were below 0.5mg/kg (the MRL of China), while the chlormequat residues could not be detected in soil. Low residues in cotton seed and soil suggest that this pesticide may be safe to use under the recommended dosage.

Dissipation and residues of chlormequat in wheat and soil.

A specific, sensitive method was developed for the analysis of chlormequat in wheat and soil by high performance chromatography/mass spectrometry. The fortified recoveries of soil were from 75.08% to 96.55%, with RSD 3.34%-15.18%, the limit of detection of the analytical method was 0.05 ng at a signal-to-noise ratio of 3, and the limit of quantification was 0.05, 0.1, 0.5 mg/kg for soil, wheat plants and wheat grain, respectively. The degradation dynamics and final residues of chlormequat in Beijing and Changchun were investigated. The half-life of chlormequat in wheat plants were 3.15 days in Beijing and 4.56 days in Changchun, while the half-life in soil was 3.88 days in Beijing and 4.51 days in Changchun. The final residues of chlormequat in soil were not detectable, and the final residues of chlormequat in wheat grain were below 0.50 mg/kg except for 3.51 mg/kg from high dosage plot of Changchun. The fact that all the final residues were below 5 mg/kg (GB2763 in National standards of the People's Republic of China, maximum residue limits for pesticide in food, Beijing, 2005) suggested that chlormequat could be safely used in wheat crops with the suitable dosage and application.

Extracting Stage-Specific and Dynamic Modules Through Analyzing Multiple Networks Associated with Cancer Progression.

Determining the dynamics of pathways associated with cancer progression is critical for understanding the etiology of diseases. Advances in biological technology have facilitated the simultaneous genomic profiling of multiple patients at different clinical stages, thus generating the dynamic genomic data for cancers. Such data provide enable investigation of the dynamics of related pathways. However, methods for integrative analysis of dynamic genomic data are inadequate. In this study, we develop a novel nonnegative matrix factorization algorithm for dynamic modules ( NMF-DM), which simultaneously analyzes multiple networks for the identification of stage-specific and dynamic modules. NMF-DM applies the temporal smoothness framework by balancing the networks at the current stage and the previous stage. Experimental results indicate that the NMF-DM algorithm is more accurate than the state-of-the-art methods in artificial dynamic networks. In breast cancer networks, NMF-DM reveals the dynamic modules that are important for cancer stage transitions. Furthermore, the stage-specific and dynamic modules have distinct topological and biochemical properties. Finally, we demonstrate that the stage-specific modules significantly improve the accuracy of cancer stage prediction. The proposed algorithm provides an effective way to explore the time-dependent cancer genomic data.

lncSLdb: a resource for long non-coding RNA subcellular localization.

While long non-coding RNAs (lncRNAs) may play important roles in cellular function and biological process, we still know little about them. Growing evidences indicate that subcellular localization of lncRNAs may provide clues to their functionality. To facilitate researchers functionally characterize thousands of lncRNAs, we developed a database-driven application, lncSLdb, which stores and manages user-collected qualitative and quantitative subcellular localization information of lncRNAs from literature mining. The current release contains >11 000 transcripts from three species. Based on the accumulated region of lncRNAs, we classify transcripts into three basic localization types (nucleus, cytoplasm and nucleus/cytoplasm). In some conditions, the nucleus and cytoplasm types can be divided into three more accurate subtypes (chromosome, nucleoplasm and ribosome). Besides browsing and downloading data in lncSLdb, our system provides a set of comprehensive tools to search by gene symbols, genome coordinates or sequence similarity. We hope that lncSLdb will provide a convenient platform for researchers to investigate the functions and the molecular mechanisms of lncRNAs in the view of subcellular localization.

Advances in long noncoding RNAs: identification, structure prediction and function annotation.

Long noncoding RNAs (lncRNAs), generally longer than 200 nucleotides and with poor protein coding potential, are usually considered collectively as a heterogeneous class of RNAs. Recently, an increasing number of studies have shown that lncRNAs can involve in various critical biological processes and a number of complex human diseases. Not only the primary sequences of many lncRNAs are directly interrelated to a specific functional role, strong evidence suggests that their secondary structures are even more interrelated to their known functions. As functional molecules, lncRNAs have become more and more relevant to many researchers. Here, we review recent, state-of-the-art advances in the three levels (the primary sequence, the secondary structure and the function annotation) of the lncRNA research, as well as computational methods for lncRNA data analysis.

HPOSim: an R package for phenotypic similarity measure and enrichment analysis based on the human phenotype ontology.

BACKGROUND: Phenotypic features associated with genes and diseases play an important role in disease-related studies and most of the available methods focus solely on the Online Mendelian Inheritance in Man (OMIM) database without considering the controlled vocabulary. The Human Phenotype Ontology (HPO) provides a standardized and controlled vocabulary covering phenotypic abnormalities in human diseases, and becomes a comprehensive resource for computational analysis of human disease phenotypes. Most of the existing HPO-based software tools cannot be used offline and provide only few similarity measures. Therefore, there is a critical need for developing a comprehensive and offline software for phenotypic features similarity based on HPO. RESULTS: HPOSim is an R package for analyzing phenotypic similarity for genes and diseases based on HPO data. Seven commonly used semantic similarity measures are implemented in HPOSim. Enrichment analysis of gene sets and disease sets are also implemented, including hypergeometric enrichment analysis and network ontology analysis (NOA). CONCLUSIONS: HPOSim can be used to predict disease genes and explore disease-related function of gene modules. HPOSim is open source and freely available at SourceForge (https://sourceforge.net/p/hposim/).

Diversified Control Paths: A Significant Way Disease Genes Perturb the Human Regulatory Network.

BACKGROUND: The complexity of biological systems motivates us to use the underlying networks to provide deep understanding of disease etiology and the human diseases are viewed as perturbations of dynamic properties of networks. Control theory that deals with dynamic systems has been successfully used to capture systems-level knowledge in large amount of quantitative biological interactions. But from the perspective of system control, the ways by which multiple genetic factors jointly perturb a disease phenotype still remain. RESULTS: In this work, we combine tools from control theory and network science to address the diversified control paths in complex networks. Then the ways by which the disease genes perturb biological systems are identified and quantified by the control paths in a human regulatory network. Furthermore, as an application, prioritization of candidate genes is presented by use of control path analysis and gene ontology annotation for definition of similarities. We use leave-one-out cross-validation to evaluate the ability of finding the gene-disease relationship. Results have shown compatible performance with previous sophisticated works, especially in directed systems. CONCLUSIONS: Our results inspire a deeper understanding of molecular mechanisms that drive pathological processes. Diversified control paths offer a basis for integrated intervention techniques which will ultimately lead to the development of novel therapeutic strategies.

A New Role for LOC101928437 in Non-Syndromic Intellectual Disability: Findings from a Family-Based Association Test.

Non-syndromic intellectual disability (NSID) is mental retardation in persons of normal physical appearance who have no recognisable features apart from obvious deficits in intellectual functioning and adaptive ability; however, its genetic etiology of most patients has remained unknown. The main purpose of this study was to fine map and identify specific causal gene(s) by genotyping a NSID family cohort using a panel of markers encompassing a target region reported in a previous work. A total of 139 families including probands, parents and relatives were included in the household survey, clinical examinations and intelligence tests, recruited from the Qinba mountain region of Shannxi province, western China. A collection of 34 tagged single nucleotide polymorphisms (tSNPs) spanning five microsatellite marker (STR) loci were genotyped using an iPLEX Gold assay. The association between tSNPs and patients was analyzed by family-based association testing (FBAT) and haplotype analysis (HBAT). Four markers (rs5974392, rs12164331, rs5929554 and rs3116911) in a block that showed strong linkage disequilibrium within the first three introns of the LOC101928437 locus were found to be significantly associated with NSID (all P<0.01) by the FBAT method for a single marker in additive, dominant and recessive models. The results of haplotype tests of this block also revealed a significant association with NSID (all P<0.05) using 2-window and larger HBAT analyses. These results suggest that LOC101928437 is a novel candidate gene for NSID in Han Chinese individuals of the Qinba region of China. Although the biological function of the gene has not been well studied, knowledge about this gene will provide insights that will increase our understanding of NSID development.

A network based method for analysis of lncRNA-disease associations and prediction of lncRNAs implicated in diseases.

Increasing evidence has indicated that long non-coding RNAs (lncRNAs) are implicated in and associated with many complex human diseases. Despite of the accumulation of lncRNA-disease associations, only a few studies had studied the roles of these associations in pathogenesis. In this paper, we investigated lncRNA-disease associations from a network view to understand the contribution of these lncRNAs to complex diseases. Specifically, we studied both the properties of the diseases in which the lncRNAs were implicated, and that of the lncRNAs associated with complex diseases. Regarding the fact that protein coding genes and lncRNAs are involved in human diseases, we constructed a coding-non-coding gene-disease bipartite network based on known associations between diseases and disease-causing genes. We then applied a propagation algorithm to uncover the hidden lncRNA-disease associations in this network. The algorithm was evaluated by leave-one-out cross validation on 103 diseases in which at least two genes were known to be involved, and achieved an AUC of 0.7881. Our algorithm successfully predicted 768 potential lncRNA-disease associations between 66 lncRNAs and 193 diseases. Furthermore, our results for Alzheimer's disease, pancreatic cancer, and gastric cancer were verified by other independent studies.

Excess sludge reduction using pilot-scale lysis-cryptic growth system integrated ultrasonic/alkaline disintegration and hydrolysis/acidogenesis pretreatment.

A pilot-scale lysis-cryptic growth system was built and operated continuously for excess sludge reduction. Combined ultrasonic/alkaline disintegration and hydrolysis/acidogenesis were integrated into its sludge pretreatment system. Continuous operation showed that the observed biomass yield and the sludge reduction efficiency of the lysis-cryptic growth system were 0.27 kg VSS/kg COD consumed and 56.5%, respectively. The water quality of its effluent was satisfactory. The sludge pretreatment system performed well and its TCOD removal efficiency was 7.9% which contributed a sludge reduction efficiency of 2.1%. The SCOD, VFA, TN, NH(4)(+)-N, TP and pH in the supernatant of pretreated sludge were 1790 mg/L, 1530 mg COD/L, 261.1mg/L, 114.0mg/L, 93.1mg/L and 8.69, respectively. The total operation cost of the lysis-cryptic growth system was $ 0.186/m(3) wastewater, which was 11.4% less than that of conventional activated sludge (CAS) system without excess sludge pretreatment.

A computational method based on the integration of heterogeneous networks for predicting disease-gene associations.

The identification of disease-causing genes is a fundamental challenge in human health and of great importance in improving medical care, and provides a better understanding of gene functions. Recent computational approaches based on the interactions among human proteins and disease similarities have shown their power in tackling the issue. In this paper, a novel systematic and global method that integrates two heterogeneous networks for prioritizing candidate disease-causing genes is provided, based on the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein interactions. In this method, the association score function between a query disease and a candidate gene is defined as the weighted sum of all the association scores between similar diseases and neighbouring genes. Moreover, the topological correlation of these two heterogeneous networks can be incorporated into the definition of the score function, and finally an iterative algorithm is designed for this issue. This method was tested with 10-fold cross-validation on all 1,126 diseases that have at least a known causal gene, and it ranked the correct gene as one of the top ten in 622 of all the 1,428 cases, significantly outperforming a state-of-the-art method called PRINCE. The results brought about by this method were applied to study three multi-factorial disorders: breast cancer, Alzheimer disease and diabetes mellitus type 2, and some suggestions of novel causal genes and candidate disease-causing subnetworks were provided for further investigation.