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Though single cell RNA sequencing (scRNA-seq) technologies have been well developed, the acquisition of large-scale single cell expression data may still lead to high costs. Single cell expression profile has its inherent sparse properties, which makes it compressible, thus providing opportunities for solutions. Here, by computational simulation as well as experiment of 54 single cells, we propose that expression profiles can be compressed from the dimension of samples by overlapped assigning each cell into plenty of pools. And we prove that expression profiles can be inferred from these pool expression data with overlapped pooling design and compressed sensing strategy. We also show that by combining this approach with plate-based scRNA-seq measurement, it can maintain its superiorities in gene detection sensitivity and individual identity and recover the expression profile with high precision, while saving about half of the library cost. This method can inspire novel conceptions on the measurement, storage or computation improvements for other compressible signals in many biological areas.

Abiotic stress responses are regulated critically at the transcriptional level. Clarifying the intricate mechanisms that regulate gene expression in response to abiotic stress is crucial and challenging. For this purpose, the factors that regulate gene expression and their binding sites in DNA should be determined. By using bioinformatics tools, the differentially expressed probe sets were studied. A meta-analysis of transcriptomic responses to several abio¬tic stresses in barley was performed. Motif enrichments revealed that AP2/ERF (APETALA2/Ethylene-Res¬pon¬sive Factor) has the most frequent binding sites. We found that the bHLH transcription factor family has the high¬est number of transcription factor members. Moreover, network construction revealed that AP2 has the highest number of connections with other genes, which indicates its critical role in abiotic stress responses. The present research further predicted 49 miRNAs belonging to 23 miRNA families. This study identified the probable conserved and enriched motifs, which might have a role in the regulation of differentially expressed genes under abiotic stresses. In addition to shedding light on gene expression regulation, a toolbox of available promoters for genetic engineering of crop plants under such abiotic stresses was developed.

Graphical abstract, Heart failure with preserved ejection fraction (HFpEF) is associated with multiple etiologic and pathophysiologic factors. HFpEF leads to significant cardiovascular morbidity and mortality. There are various reasons that fail to identify effective therapeutic interventions for HFpEF, primarily due to its clinical heterogeneity causing significant difficulties in determining physiologic and prognostic implications for this syndrome. Thus, identifying clinical subtypes using multi-omics data has great implications for efficient treatment and prognosis of HFpEF patients. Here we proposed to integrate mRNA, DNA methylation and microRNA (miRNA) expression data of HFpEF with a similarity network fusion (SNF) method following a network enhancement (ne-SNF) denoising technique to form a fused network. A spectral clustering method was then used to obtain clusters of patient subtypes. Experiments on HFpEF datasets demonstrated that ne-SNF significantly outperforms single data subtype analysis and other integrated methods. The identified subgroups were shown to have statistically significant differences in survival. Two HFpEF subtypes were defined: a high-risk group (16.8%) and a low-risk group (83.2%). The 5-year mortality rates were 63.3% and 33.0% for the high- and low-risk group, respectively. After adjusting for the effects of clinical covariates, HFpEF patients in the high-risk group were 2.43 times more likely to die than the low-risk group. A total of 157 differentially expressed (DE) mRNAs, 2199 abnormal methylations and 121 DE miRNAs were identified between two subtypes. They were also enriched in many HFpEF-related biological processes or pathways. The ne-SNF method provides a novel pipeline for subtype identification in integrated analysis of multi-omics data.

The present study investigates the role of network topology in lung adenocarcinoma (LUAD) development. Analysis of sex- and stage-specific whole-genome expression data revealed that co-expressed and highly connected prognostic genes common to all cancer stages form a small-world network in each stage of LUAD. These small-world networks are present within stage-specific scale-free networks, conserved across the cancer stages, and linked to cancer-specific events. The presence of small-world networks across the cancer stages presents a synchronized system in the disordered environment of cancer, resulting in the evolution of malignancy. Our study reported that these small-world networks are resilient to random and systematic attacks, indicating the least opportunity to introduce perturbations by drugs as a therapeutic intervention. We concluded that highly clustered small-world networks could be controlled through transcriptional modulation for the improved treatment of LUAD.

Candidatus Liberibacter asiaticus (CLas) has been associated with Huanglongbing, a lethal vector-borne disease affecting citrus crops worldwide. While comparative genomics has provided preliminary insights into the metabolic capabilities of this uncultured microorganism, a comprehensive functional characterization is currently lacking. Here, we reconstructed and manually curated genome-scale metabolic models for the six CLas strains A4, FL17, gxpsy, Ishi-1, psy62, and YCPsy, in addition to a model of the closest related culturable microorganism, L. crescens BT-1. Predictions about nutrient requirements and changes in growth phenotypes of CLas were confirmed using in vitro hairy root-based assays, while the L. crescens BT-1 model was validated using cultivation assays. Host-dependent metabolic phenotypes were revealed using expression data obtained from CLas-infected citrus trees and from the CLas-harboring psyllid Diaphorina citri Kuwayama. These results identified conserved and unique metabolic traits, as well as strain-specific interactions between CLas and its hosts, laying the foundation for the development of model-driven Huanglongbing management strategies.

This study aimed at exploring the genes that may be related to the prognosis of primary breast cancer (BC) patients. The gene expression microarray data, together with sample survival data were acquired from The Cancer Genome Atlas database. The top 20% genes according to expression value variance were subjected to hierarchical cluster analysis. Bootstrap methods were utilized to assess the stability of cluster. Cox regression was applied to screen genes related to the survival time of patients with BC, and the Beta-Uniform Mixture model was applied to adjust the significance of numerous tests. Further, ingenuity pathway analysis (IPA) was carried out to analyze the functions of the potential prognostic genes. Cluster analysis revealed that there were at least five stable BC subtypes, each with specific gene expression. Further, 42 survival time-associated genes were found (

Plant lipid transfer proteins (LTPs) are small basic proteins that play important roles in the regulation of various plant biological processes as well as the response to biotic and abiotic stresses. However, knowledge is limited on how this family of proteins is regulated in response to nematode infection in cucumber. In the present study, a total of 39 CsLTP_2 genes were identified by querying databases for cucumber-specific LTP_2 using a Hidden Markov Model approach and manual curation. The family has a five-cysteine motif (5CM) with the basic form CC-Xn-CXC-Xn-C, which differentiates it from typical nsLTPs. The members of CsLTP_2 were grouped into six families according to their structure and their phylogenetic relationships. Expression data of CsLTP_2 genes in 10 cucumber tissues indicated that they were tissue-specific genes. Two genes showed significant expression change in roots of resistant and susceptible lines during nematode infection, indicating their involvement in response to Meloidogyne incognita. This systematic analysis provides a foundation of knowledge for future studies of the biological roles of CsLTP_2 genes in cucumber in response to nematode infection and may help in the efforts to improve M. incognita-resistance breeding in cucumber.

—We have analyzed the gene expression dynamics in floral transition in the CDC Frontier chickpea cultivar. We provide a model, in several versions, to predict the expression dynamics of five flowering genes, taking the expression of their regulators as an input. The models were trained using the random forest method on the previously published expression data for ten flowering genes under the short- and long-day growing conditions. The resulting models correctly predict the dynamics of the average expression levels under long days. We show that the models for CDC Frontier mainly reproduce the regulatory interactions between the key genes described for the Arabidopsis thaliana model plant. Based on the analysis, we hypothesize that the short-day data and the long-day data contain qualitatively different information, which may be due to different regulatory modules that function in different conditions. For the regulators of the flower meristem identity genes AP1 and LFY, our models predict FTa3 as the main activator and TFL1c as the main repressor under long days.

Causality inference is the use of computational techniques to predict possible causal relationships for a set of variables, thereby forming a directed network. Causality inference in Gene Regulatory Networks (GRNs) is an important, yet challenging task due to the limits of available data and lack of efficiency in existing causality inference techniques. A number of techniques have been proposed and applied to infer causal relationships in various domains, although they are not specific to regulatory network inference. In this paper, we assess the effectiveness of methods for inferring causal GRNs. We introduce seven different inference methods and apply them to infer directed edges in GRNs. We use time-series expression data from the DREAM challenges to assess the methods in terms of quality of inference and rank them based on performance. The best method is applied to Breast Cancer data to infer a causal network. Experimental results show that Causation Entropy is best, however, highly time-consuming and not feasible to use in a relatively large network. We infer Breast Cancer GRN with the second-best method, Transfer Entropy. The topological analysis of the network reveals that top out-degree genes such as SLC39A5 which are considered central genes, play important role in cancer progression.

Background To detect changes in biological processes, samples are often studied at several time points. We examined expression data measured at different developmental stages, or more broadly, historical data. Hence, the main assumption of our proposed methodology was the independence between the examined samples over time. In addition, however, the examinations were clustered at each time point by measuring littermates from relatively few mother mice at each developmental stage. As each examination was lethal, we had an independent data structure over the entire history, but a dependent data structure at a particular time point. Over the course of these historical data, we wanted to identify abrupt changes in the parameter of interest - change points. Results In this study, we demonstrated the application of generalized hypothesis testing using a linear mixed effects model as a possible method to detect change points. The coefficients from the linear mixed model were used in multiple contrast tests and the effect estimates were visualized with their respective simultaneous confidence intervals. The latter were used to determine the change point(s). In small simulation studies, we modelled different courses with abrupt changes and compared the influence of different contrast matrices. We found two contrasts, both capable of answering different research questions in change point detection: The Sequen contrast to detect individual change points and the McDermott contrast to find change points due to overall progression. We provide the R code for direct use with provided examples. The applicability of those tests for real experimental data was shown with in-vivo data from a preclinical study. Conclusion Simultaneous confidence intervals estimated by multiple contrast tests using the model fit from a linear mixed model were capable to determine change points in clustered expression data. The confidence intervals directly delivered interpretable effect estimates representing the strength of the potential change point. Hence, scientists can define biologically relevant threshold of effect strength depending on their research question. We found two rarely used contrasts best fitted for detection of a possible change point: the Sequen and McDermott contrasts.

Description, The CaCTS algorithm nominates cancer cell master transcription factors and guides a model of ovarian cancer regulatory circuitry., Critical developmental “master transcription factors” (MTFs) can be subverted during tumorigenesis to control oncogenic transcriptional programs. Current approaches to identifying MTFs rely on ChIP-seq data, which is unavailable for many cancers. We developed the CaCTS (Cancer Core Transcription factor Specificity) algorithm to prioritize candidate MTFs using pan-cancer RNA sequencing data. CaCTS identified candidate MTFs across 34 tumor types and 140 subtypes including predictions for cancer types/subtypes for which MTFs are unknown, including e.g. PAX8, SOX17, and MECOM as candidates in ovarian cancer (OvCa). In OvCa cells, consistent with known MTF properties, these factors are required for viability, lie proximal to superenhancers, co-occupy regulatory elements globally, co-bind loci encoding OvCa biomarkers, and are sensitive to pharmacologic inhibition of transcription. Our predictions of MTFs, especially for tumor types with limited understanding of transcriptional drivers, pave the way to therapeutic targeting of MTFs in a broad spectrum of cancers.

Bimodal gene expression (where a gene expression distribution has two maxima) is associated with phenotypic diversity in different biological systems. A critical issue, thus, is the integration of expression and phenotype data to identify genuine associations. Here, we developed tools that allow both: i) the identification of genes with bimodal gene expression and ii) their association with prognosis in cancer patients from The Cancer Genome Atlas (TCGA). Bimodality was observed for 554 genes in expression data from 25 tumor types. Furthermore, 96 of these genes presented different prognosis when patients belonging to the two expression peaks were compared. The software to execute the method and the corresponding documentation are available at the Data access section.

Bacillus subtilis is a Gram-positive model bacterium with extensive documented annotation. However, with the rise of high-throughput techniques, the amount of complex data being generated every year has been increasing at a fast pace. Thus, having platforms ready to integrate and give a representation to these data becomes a priority. To address it, SubtiWiki (http://subtiwiki.uni-goettingen.de/) was created in 2008 and has been growing in data and viewership ever since. With millions of requests every year, it is the most visited B. subtilis database, providing scientists all over the world with curated information about its genes and proteins, as well as intricate protein–protein interactions, regulatory elements, expression data and metabolic pathways. However, there is still a large portion of annotation to be unveiled for some biological elements. Thus, to facilitate the development of new hypotheses for research, we have added a Homology section covering potential protein homologs in other organisms. Here, we present the recent developments of SubtiWiki and give a guided tour of our database and the current state of the data for this organism.

This study applied two mathematical algorithms, lattice up-stream targeting (LUST) and D-basis, to the identification of prognostic signatures from cancer gene expression data. The LUST algorithm l...

eQTL studies are essential for understanding genomic regulation. Effects of genetic variations on gene regulation are cell-type-specific and cellular-context-related, so studying eQTLs at a single-cell level is crucial. The ideal solution is to use both mutation and expression data from the same cells. However, current technology of such paired data in single cells is still immature. We present a new method, eQTLsingle, to discover eQTLs only with single cell RNA-seq (scRNA-seq) data, without genomic data. It detects mutations from scRNA-seq data and models gene expression of different genotypes with the zero-inflated negative binomial (ZINB) model to find associations between genotypes and phenotypes at single-cell level. On a glioblastoma and gliomasphere scRNA-seq dataset, eQTLsingle discovered hundreds of cell-type-specific tumor-related eQTLs, most of which cannot be found in bulk eQTL studies. Detailed analyses on examples of the discovered eQTLs revealed important underlying regulatory mechanisms. eQTLsingle is a unique powerful tool for utilizing the huge scRNA-seq resources for single-cell eQTL studies, and it is available for free academic use at https://github.com/horsedayday/eQTLsingle.

RNA-seq has become the standard tool for collecting genome-wide expression data in diverse fields, from quantitative genetics and medical genomics to ecology and developmental biology. However, RNA-seq library preparation is still prohibitive for many laboratories. Recently, the field of single-cell transcriptomics has reduced costs and increased throughput by adopting early barcoding and pooling of individual samples —producing a single final library containing all samples. In contrast, RNA-seq protocols where each sample is processed individually are significantly more expensive and lower throughput than single-cell approaches. Yet, many projects depend on individual library generation to preserve important samples or for follow-up re-sequencing experiments. Improving on currently available RNA-seq methods we have developed TM3’seq, a 3’-enriched library preparation protocol that uses Tn5 transposase and preserves sample identity at each step. TM3’seq is designed for high-throughput processing of individual samples (96 samples in 6h, with only 3h hands-on time) at a fraction of the cost of commercial kits ($1.5 per sample). The protocol was tested in a range of human andDrosophila melanogasterRNA samples, recovering transcriptomes of the same quality and reliability than the commercial NEBNext®kit. We expect that the cost-and time-efficient features of TM3’seq make large-scale RNA-seq experiments more permissive for the entire scientific community.

The repertoire of small noncoding RNAs (sncRNAs), particularly miRNAs, in animals is considered to be evolutionarily conserved. Studies on sncRNAs are often largely based on homology-based information, relying on genomic sequence similarity and excluding actual expression data. To obtain information on sncRNA expression (including miRNAs, snoRNAs, YRNAs and tRNAs), we performed low-input-volume next-generation sequencing of 500 pg of RNA from 21 animals at two German zoological gardens. Notably, none of the species under investigation were previously annotated in any miRNA reference database. Sequencing was performed on blood cells as they are amongst the most accessible, stable and abundant sources of the different sncRNA classes. We evaluated and compared the composition and nature of sncRNAs across the different species by computational approaches. While the distribution of sncRNAs in the different RNA classes varied significantly, general evolutionary patterns were maintained. In particular, miRNA sequences and expression were found to be even more conserved than previously assumed. To make the results available for other researchers, all data, including expression profiles at the species and family levels, and different tools for viewing, filtering and searching the data are freely available in the online resource ASRA (Animal sncRNA Atlas) at https://www.ccb.uni-saarland.de/asra/.

Introduction Numerous omics studies have been conducted to understand the molecular networks involved in Alzheimer's disease (AD), but the pathophysiology is still not completely understood; new approaches that enable neuroscientists to better interpret the results of omics analysis are required. Methods We have developed advanced methods to analyze and visualize publicly-available genomics and genetics data. The tools include a composite clinical-neuropathological score for defining AD, gene expression maps in the brain, and networks integrating omics data to understand the impact of polymorphisms on AD pathways. Results We have analyzed over 50 public human gene expression data sets, spanning 19 different brain regions and encompassing three separate cohorts. We integrated genome-wide association studies with expression data to identify important genes in the pathophysiology of AD, which provides further insight into the calcium signaling and calcineurin pathways. Discussion Biologists can use these freely-available tools to obtain a comprehensive, information-rich view of the pathways in AD.

Background: Gene Regulatory Network (GRN) inference algorithms aim to explore casual interactions between genes and transcriptional factors. High-throughput transcriptomics data including DNA microarray and single cell expression data contain complementary information in network inference. Objective: To enhance GRN inference, data integration across various types of expression data becomes an economic and efficient solution. Method: In this paper, a novel E-alpha integration rule-based ensemble inference algorithm is proposed to merge complementary information from microarray and single cell expression data. This paper implements a Gradient Boosting Tree (GBT) inference algorithm to compute importance scores for candidate gene-gene pairs. The proposed E-alpha rule quantitatively evaluates the credibility levels of each information source and determines the final ranked list. Results: Two groups of in silico gene networks are applied to illustrate the effectiveness of the proposed E-alpha integration. Experimental outcomes with size50 and size100 in silico gene networks suggest that the proposed E-alpha rule significantly improves performance metrics compared with single information source. Conclusion: In GRN inference, the integration of hybrid expression data using E-alpha rule provides a feasible and efficient way to enhance performance metrics than solely increasing sample sizes.

We constructed a lung cancer-specific database housing expression data and clinical data from over 6700 patients in 56 studies. Expression data from 23 genome-wide platforms were carefully processed and quality controlled, whereas clinical data were standardized and rigorously curated. Empowered by this lung cancer database, we created an open access web resource—the Lung Cancer Explorer (LCE), which enables researchers and clinicians to explore these data and perform analyses. Users can perform meta-analyses on LCE to gain a quick overview of the results on tumor vs non-malignant tissue (normal) differential gene expression and expression-survival association. Individual dataset-based survival analysis, comparative analysis, and correlation analysis are also provided with flexible options to allow for customized analyses from the user.

Adult patients with atopic dermatitis (AD) present relatively higher rates of major depressive disorder (MDD). However, the underlying mechanism is largely unknown. Here, we first conducted a systematic literature-based data mining to identify entities linking AD and MDD, including proteins, cells, functional classes, and small molecules. Then we conducted an AD-RNA expression data-based mega-analysis to test the expression variance of the genes that were regulators of MDD. After that, a Fisher Exact test-based pathway enrichment analysis (PEA) was performed to explore the AD-driven MDD-genetic regulators’ functionality. We identified 22 AD-driven entities that were up-stream MDD regulators, including 11 genes, seven small molecules, three functional classes, and one cell. AD could exert a promoting effect on the development of MDD. Four of the 11 genes demonstrated significant expression changes in AD patients in favor of the development of MDD. PEA results showed that AD mainly drives cytokine/chemokine regulation and neuroinflammatory response-related pathways to influence the pathological development of MDD. Our results supported the promotion role of AD in the pathological development of MDD, including the regulation of multiple genetic regulators of MDD involved in cytokine/chemokine regulation and inflammatory response.

To understand the role of host genetic variation in infection by SARS-CoV and influenza A virus we developed the Collaborative Cross Founder Expression Analysis (CCFEA), a shiny visualization tool using public RNAseq data from the collaborative cross (CC) founder strains (A/J, C57BL/6J, 129s1/SvImJ, NOD/ShILtJ, NZO/HILtJ, CAST/EiJ, PWK/PhJ, and WSB/EiJ) that underwent infection by either virus, linked with genetic analyses to define loci linked to infection, immunity, and disease phenotypes. Individual gene expression data is displayed across founders, viral infections and days post infection.Resource Table

Genome wide association studies (GWAS) have identified more than 180 variants associated with breast cancer risk, however the underlying functional mechanisms and biological pathways which confer disease susceptibility remain largely unknown. As gene expression traits are under genetic regulation we hypothesise that differences in gene expression variability may identify causal breast cancer susceptibility genes. We performed variable expression quantitative trait loci (veQTL) analysis using tissue-specific expression data from the Genotype-Tissue Expression (GTEx) Common Fund Project. veQTL analysis identified 70 associations (p –8) consisting of 60 genes and 27 breast cancer risk variants, including 55 veQTL that were observed in breast tissue only. Pathway analysis of genes associated with breast-specific veQTL revealed an enrichment of four genes (CYP11B1, CYP17A1 HSD3B2 and STAR) involved in the C21-steroidal biosynthesis pathway that converts cholesterol to breast-related hormones (e.g. oestrogen). Each of these four genes were significantly more variable in individuals homozygous for rs11075995 (A/A) breast cancer risk allele located in the FTO gene, which encodes an RNA demethylase. The A/A allele was also found associated with reduced expression of FTO, suggesting an epi-transcriptomic mechanism may underlie the dysregulation of genes involved in hormonal biosynthesis leading to an increased risk of breast cancer. These findings provide evidence that genetic variants govern high levels of expression variance in breast tissue, thus building a more comprehensive insight into the underlying biology of breast cancer risk loci.

Studies comparing single cell RNA-Seq (scRNA-Seq) data between conditions mainly focus on differences in the proportion of cell types or on differentially expressed genes. In many cases these differences are driven by changes in cell interactions which are challenging to infer without spatial information. To determine cell-cell interactions that differ between conditions we developed the Cell Interaction Network Inference (CINS) pipeline. CINS combines Bayesian network analysis with regression-based modeling to identify differential cell type interactions and the proteins that underlie them. We tested CINS on a disease case control and on an aging mouse dataset. In both cases CINS correctly identifies cell type interactions and the ligands involved in these interactions improving on prior methods suggested for cell interaction predictions. We performed additional mouse aging scRNA-Seq experiments which further support the interactions identified by CINS.

Objective: Modern medicine needs to shift from a wait and react, curative discipline to a preventative, interdisciplinary science aiming at providing personalized, systemic, and precise treatment plans to patients. To this purpose, we propose a “digital twin” of patients modeling the human body as a whole and providing a panoramic view over individuals' conditions.Methods: We propose a general framework that composes advanced artificial intelligence (AI) approaches and integrates mathematical modeling in order to provide a panoramic view over current and future pathophysiological conditions. Our modular architecture is based on a graph neural network (GNN) forecasting clinically relevant endpoints (such as blood pressure) and a generative adversarial network (GAN) providing a proof of concept of transcriptomic integrability.Results: We tested our digital twin model on two simulated clinical case studies combining information at organ, tissue, and cellular level. We provided a panoramic overview over current and future patient's conditions by monitoring and forecasting clinically relevant endpoints representing the evolution of patient's vital parameters using the GNN model. We showed how to use the GAN to generate multi-tissue expression data for blood and lung to find associations between cytokines conditioned on the expression of genes in the renin–angiotensin pathway. Our approach was to detect inflammatory cytokines, which are known to have effects on blood pressure and have previously been associated with SARS-CoV-2 infection (e.g., CXCR6, XCL1, and others).Significance: The graph representation of a computational patient has potential to solve important technological challenges in integrating multiscale computational modeling with AI. We believe that this work represents a step forward toward next-generation devices for precision and predictive medicine.

Detecting cancer dependencies is key to disease treatment. Recent efforts have mapped gene dependencies and drug sensitivities in hundreds of cancer cell lines. These data allow us to learn for the first time models of tumor vulnerabilities and apply them to suggest novel drug targets. Here we devise novel deep learning methods for predicting gene dependencies and drug sensitivities from gene expression measurements. By combining dimensionality reduction strategies, we are able to learn accurate models that outperform simpler neural networks or linear models.

The advent of single-cell sequencing started a new era of transcriptomic and genomic research, advancing our knowledge of the cellular heterogeneity and dynamics. Cell type annotation is a crucial step in analyzing single-cell RNA sequencing data, yet manual annotation is time-consuming and partially subjective. As an alternative, tools have been developed for automatic cell type identification. Different strategies have emerged to ultimately associate gene expression profiles of single cells with a cell type either by using curated marker gene databases, correlating reference expression data, or transferring labels by supervised classification. In this review, we present an overview of the available tools and the underlying approaches to perform automated cell type annotations on scRNA-seq data.

The Gene Expression Database (GXD; www.informatics.jax.org/expression.shtml) is an extensive and well-curated community resource of mouse developmental gene expression information. For many years, GXD has collected and integrated data from RNA in situ hybridization, immunohistochemistry, RT-PCR, northern blot, and western blot experiments through curation of the scientific literature and by collaborations with large-scale expression projects. Since our last report in 2019, we have continued to acquire these classical types of expression data; developed a searchable index of RNA-Seq and microarray experiments that allows users to quickly and reliably find specific mouse expression studies in ArrayExpress (https://www.ebi.ac.uk/arrayexpress/) and GEO (https://www.ncbi.nlm.nih.gov/geo/); and expanded GXD to include RNA-Seq data. Uniformly processed RNA-Seq data are imported from the EBI Expression Atlas and then integrated with the other types of expression data in GXD, and with the genetic, functional, phenotypic and disease-related information in Mouse Genome Informatics (MGI). This integration has made the RNA-Seq data accessible via GXD’s enhanced searching and filtering capabilities. Further, we have embedded the Morpheus heat map utility into the GXD user interface to provide additional tools for display and analysis of RNA-Seq data, including heat map visualization, sorting, filtering, hierarchical clustering, nearest neighbors analysis and visual enrichment.

Different pathway analysis methods are frequently applied to cancer gene expression data to identify dysregulated pathways. In most cases these methods infer pathway activity changes based on the gene expression of pathway members. However, pathways are constituted by signaling proteins, and their activity - not their abundance - defines the activity of the pathway; the association between gene expression and protein activity is in turn limited and not well characterised. Other methods infer pathway activity from the expression of the genes whose transcription is regulated by the pathway of interest, which seems a more adequate proxy of activity. Despite these potential limitations, membership based pathway methods are frequently used and often provide statistically significant results.Here, we submit that pathway based methods are not effective because of the correlation between the gene expression of pathway members and the activity of the pathway, but because pathway member gene sets overlap with the genes regulated by transcription factors (regulons). This implies that pathway methods do not inform about the activity of the pathway of interest, but instead the downstream effects of changes in the activities of transcription factors.To support our hypothesis, we show that the higher the overlap to transcription factor regulons, the higher the information value of pathway gene sets. Furthermore, removing these overlapping genes reduces the information content of pathway gene sets, but not vice versa. Our results suggest that results of classical pathway analysis methods should be interpreted with caution, and instead methods using pathway regulated genes for activity inference should be prioritised.Graphical abstract

Recent advances in genomic technologies have generated data on large-scale protein–DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has become a major challenge in genomic research. To solve this problem, we have developed a method called ConSReg, which provides a novel approach to integrate regulatory genomic data into predictive machine learning models of key regulatory genes. Using Arabidopsis as a model system, we tested our approach to identify regulatory genes in data sets from single cell gene expression and from abiotic stress treatments. Our results showed that ConSReg accurately predicted transcription factors that regulate differentially expressed genes with an average auROC of 0.84, which is 23.5–25% better than enrichment-based approaches. To further validate the performance of ConSReg, we analyzed an independent data set related to plant nitrogen responses. ConSReg provided better rankings of the correct transcription factors in 61.7% of cases, which is three times better than other plant tools. We applied ConSReg to Arabidopsis single cell RNA-seq data, successfully identifying candidate regulatory genes that control cell wall formation. Our methods provide a new approach to define candidate regulatory genes using integrated genomic data in plants.

Objective The aim of the study was to find the key genes, microRNAs (miRNAs) and transcription factors (TFs) and construct miRNA-target gene-TF regulatory networks to investigate the underlying molecular mechanism in colorectal adenoma (CRA). Methods Four mRNA expression datasets and one miRNA expression dataset were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs) were identified between CRA and normal samples. Moreover, functional enrichment analysis for DEGs was carried out utilizing the Cytoscape-plugin, known as ClueGO. These DEGs were mapped to STRING database to construct a protein-protein interaction (PPI) network. Then, a miRNA-target gene regulatory network was established to screen key DEMs. In addition, similar workflow of the analyses were also performed comparing the CRC samples with CRA ones to screen key DEMs. Finally, miRNA-target gene-TF regulatory networks were constructed for these key DEMs using iRegulon plug-in in Cytoscape. Results We identified 514 DEGs and 167 DEMs in CRA samples compared to healthy samples. Functional enrichment analysis revealed that these DEGs were significantly enriched in several terms and pathways, such as regulation of cell migration and bile secretion pathway. A PPI network was constructed including 325 nodes as well as 890 edges. A total of 59 DEGs and 65 DEMs were identified in CRC samples compared to CRA ones. In addition, Two key DEMs in CRA samples compared to healthy samples were identified, such as hsa-miR-34a and hsa-miR-96. One key DEM, hsa-miR-29c, which was identified when we compared the differentially expressed molecules found in the comparison CRA versus normal samples to the ones obtained in the comparison CRC versus CRA, was also identified in CRC samples compared to CRA ones. The miRNA-target gene-TF regulatory networks for these key miRNAs included two TFs, one TF and five TFs, respectively. Conclusion These identified key genes, miRNA, TFs and miRNA-target gene-TF regulatory networks associated with CRA, to a certain degree, may provide some hints to enable us to better understand the underlying pathogenesis of CRA.

Whole-genome expression data generated by microarray studies have shown promise for quantitative human health risk assessment. While numerous approaches have been developed to determine benchmark doses (BMDs) from probeset-level dose responses, sensitivity of the results to methods used for normalization of the data has not yet been systematically investigated. Normalization of microarray data converts raw hybridization signals to expression estimates that are expected to be proportional to the amounts of transcripts in the profiled specimens. Different approaches to normalization have been shown to greatly influence the results of some downstream analyses, including biological interpretation. In this study we evaluate the influence of microarray normalization methods on the transcriptomic BMDs. We demonstrate using in vivo data that the use of alternative pipelines for normalization of Affymetrix microarray data can have a considerable impact on the number of detected differentially expressed genes and pathways (processes) determined to be treatment responsive, which may lead to alternative interpretations of the data. In addition, we found that normalization can have a considerable effect (as much as ∼30-fold in this study) on estimation of the minimum biological potency (transcriptomic point of departure). We argue for consideration of alternative normalization methods and their data-informed selection to most effectively interpret microarray data for use in human health risk assessment.

Breast cancer prognosis is challenging due to the heterogeneity of the disease. Various computational methods using bulk RNA-seq data have been proposed for breast cancer prognosis. However, these methods suffer from limited performances or ambiguous biological relevance, as a result of the neglect of intra-tumor heterogeneity. Recently, single cell RNA-sequencing (scRNA-seq) has emerged for studying tumor heterogeneity at cellular levels. In this paper, we propose a novel method, scPrognosis, to improve breast cancer prognosis with scRNA-seq data. scPrognosis uses the scRNA-seq data of the biological process Epithelial-to-Mesenchymal Transition (EMT). It firstly infers the EMT pseudotime and a dynamic gene co-expression network, then uses an integrative model to select genes important in EMT based on their expression variation and differentiation in different stages of EMT, and their roles in the dynamic gene co-expression network. To validate and apply the selected signatures to breast cancer prognosis, we use them as the features to build a prediction model with bulk RNA-seq data. The experimental results show that scPrognosis outperforms other benchmark breast cancer prognosis methods that use bulk RNA-seq data. Moreover, the dynamic changes in the expression of the selected signature genes in EMT may provide clues to the link between EMT and clinical outcomes of breast cancer. scPrognosis will also be useful when applied to scRNA-seq datasets of different biological processes other than EMT., Author summary Various computational methods have been developed for breast cancer prognosis. However, those methods mainly use the gene expression data generated by the bulk RNA sequencing techniques, which average the expression level of a gene across different cell types. As breast cancer is a heterogenous disease, the bulk gene expression may not be the ideal resource for cancer prognosis. In this study, we propose a novel method to improve breast cancer prognosis using scRNA-seq data. The proposed method has been applied to the EMT scRNA-seq dataset for identifying breast cancer signatures for prognosis. In comparison with existing bulk expression data based methods in breast cancer prognosis, our method shows a better performance. Our single-cell-based signatures provide clues to the relation between EMT and clinical outcomes of breast cancer. In addition, the proposed method can also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.

Juglandaceae species are plants of great economic value and have been cultivated, domesticated, and utilized by human society for a long time. Their edible, nutrient-rich nuts and tough, durable wood have attracted the attention of botanists and breeders. With the advent of the genomics era, genome sequencing of the Juglandaceae family has been greatly accelerated, and a large amount of data has been generated. In this paper, we introduce the Portal of Juglandaceae (PJU), a tool to bring all these data together. The PJU contains genomes, gene-coding sequences, protein sequences, various types of annotation information, expression data, and miRNA data, which are configured with BLAST, JBrowse, and our self-developed synteny analysis tool. The PJU has a user-friendly and straightforward interface that performs a variety of query tasks with a few simple operations. In the future, we hope that the PJU will serve as a hub for the study of the Juglandaceae family.

Plant organ size and shape are major agronomic traits that depend on cell division and expansion, which are both regulated by complex gene networks. In several eudicot species belonging to the rosid clade, organ growth is controlled by a repressor complex consisting of PEAPOD (PPD) and KINASE-INDUCIBLE DOMAIN INTERACTING (KIX) proteins. The role of these proteins in asterids, which together with the rosids constitute most of the core eudicot species, is still unknown. We used CRISPR-Cas9 genome editing to target SlKIX8 and SlKIX9 in the asterid model species tomato (Solanum lycopersicum) and analyzed loss-of-function phenotypes. We found that loss of function of SlKIX8 and SlKIX9 led to the production of enlarged, dome-shaped leaves and that these leaves exhibited increased expression of putative SlPPD target genes. Unexpectedly, kix8 kix9 mutants carried enlarged fruits with increased pericarp thickness due to cell expansion. At the molecular level, protein interaction assays indicated that SlKIX8 and SlKIX9 act as adaptors between the SlPPD and SlTOPLESS co-repressor proteins. Our results show that KIX8 and KIX9 are regulators of organ growth in asterids and can provide strategies to improve important traits in produce such as thickness of the fruit flesh.One sentence summaryTwo transcriptional repressors negatively regulate organ growth in tomato with loss-of-function lines producing enlarged fruits due to an appearance of more expanded cells in the fruit flesh.

Objective. A growing body of emerging evidence indicates that metabolic processes play a pivotal role in the biological processes underlying acute myocardial infarction (AMI). The aim of the current study was to identify featured metabolism-related genes in patients with AMI using a support vector machine (SVM) and to further explore the value of these genes in the diagnosis of AMI. Methods. Gene microarray expression data related to AMI were downloaded from the GSE66360 dataset in the Gene Expression Omnibus (GEO) database. This data set consisted of 50 AMI samples and 49 normal controls that were randomly classified into a discovery cohort (21 AMI samples and 22 normal controls) and a validation cohort (28 AMI and 28 normal controls). We applied a machine learning method that combined SVM with recursive feature elimination (RFE) to discriminate AMI patients from normal controls. Based on this, an SVM classifier was constructed. Receiver operating characteristic (ROC) analysis was used to investigate the predictive value for the early diagnosis of AMI in the two cohorts and was then further verified in an independent external cohort. Results. Three metabolism-related genes were identified based on SVM-RFE (AKR1C3, GLUL, and PDE4B). The SVM classifier based on the three genes allowed for excellent discrimination between AMI and healthy samples in both the discovery cohort ( AUC = 0.989 ) and the validation cohort ( AUC = 0.964 ), and this was further confirmed in the GSE68060 dataset ( AUC = 0.839 ). Additionally, the SVM classifier allowed for perfect discrimination between recurrent AMI events and nonrecurrent events in the GSE68060 cohort ( AUC = 0.992 ). GO and KEGG pathway enrichment analysis of the identified featured genes revealed significant enrichment of specific metabolic pathways. Conclusion. The identified metabolism-related genes may play important roles in the development of AMI and may represent diagnostic and therapeutic biomarkers of AMI.

ArrayExpress (https://www.ebi.ac.uk/arrayexpress) is an archive of functional genomics data from a variety of technologies assaying functional modalities of a genome, such as gene expression or promoter occupancy. The number of experiments based on sequencing technologies, in particular RNA-seq experiments, has been increasing over the last few years and submissions of sequencing data have overtaken microarray experiments in the last 12 months. Additionally, there is a significant increase in experiments investigating single cells, rather than bulk samples, known as single-cell RNA-seq. To accommodate these trends, we have substantially changed our submission tool Annotare which, along with raw and processed data, collects all metadata necessary to interpret these experiments. Selected datasets are re-processed and loaded into our sister resource, the value-added Expression Atlas (and its component Single Cell Expression Atlas), which not only enables users to interpret the data easily but also serves as a test for data quality. With an increasing number of studies that combine different assay modalities (multi-omics experiments), a new more general archival resource the BioStudies Database has been developed, which will eventually supersede ArrayExpress. Data submissions will continue unchanged; all existing ArrayExpress data will be incorporated into BioStudies and the existing accession numbers and application programming interfaces will be maintained.

The mechanisms resulting in the breakdown of the coral symbiosis once the process of bleaching has been initiated remain unclear. Distinguishing the process of symbiont loss from the thermal stress response may shed light on the cellular and molecular pathways involved in each process. This study examined physiological changes and global gene expression patterns associated with white patch syndrome (WPS) in Porites lobata, which manifests in localized bleaching independent of thermal stress. In addition, a meta-analysis of global gene expression studies in other corals and anemones was used to contrast differential regulation as a result of disease and thermal stress from patterns correlated with symbiotic state. Symbiont density, chlorophyll a content, holobiont productivity, instant calcification rate, and total host protein content were uniformly reduced in WPS relative to healthy tissue. While expression patterns associated with WPS were secondary to fixed effects of source colony, specific functional enrichments combined with a lack of immune regulation suggest that the viral infection putatively giving rise to this condition affects symbiont rather than host cells. Expression in response to WPS also clustered independently of patterns in white syndrome impacted A. hyacinthus, further supporting a distinct aetiology of this syndrome. Expression patterns in WPS-affected tissues were significantly correlated with prior studies that examined short-term thermal stress responses independent of symbiotic state, suggesting that the majority of expression changes reflect a nonspecific stress response. Across studies, the magnitude and direction of expression change among particular functional enrichments suggests unique responses to stressor duration and highlights distinct responses to bleaching in an anemone model.

Selection of informative genes from high dimensional gene expression data has emerged as an important research area in genomics. Many gene selection techniques have been proposed so far are either based on relevancy or redundancy measure. Further, the performance of these techniques has been adjudged through post selection classification accuracy computed through a classifier using the selected genes. This performance metric may be statistically sound but may not be biologically relevant. A statistical approach, i.e. Boot-MRMR, was proposed based on a composite measure of maximum relevance and minimum redundancy, which is both statistically sound and biologically relevant for informative gene selection. For comparative evaluation of the proposed approach, we developed two biological sufficient criteria, i.e. Gene Set Enrichment with QTL (GSEQ) and biological similarity score based on Gene Ontology (GO). Further, a systematic and rigorous evaluation of the proposed technique with 12 existing gene selection techniques was carried out using five gene expression datasets. This evaluation was based on a broad spectrum of statistically sound (e.g. subject classification) and biological relevant (based on QTL and GO) criteria under a multiple criteria decision-making framework. The performance analysis showed that the proposed technique selects informative genes which are more biologically relevant. The proposed technique is also found to be quite competitive with the existing techniques with respect to subject classification and computational time. Our results also showed that under the multiple criteria decision-making setup, the proposed technique is best for informative gene selection over the available alternatives. Based on the proposed approach, an R Package, i.e. BootMRMR has been developed and available at https://cran.r-project.org/web/packages/BootMRMR. This study will provide a practical guide to select statistical techniques for selecting informative genes from high dimensional expression data for breeding and system biology studies.

The repertoire of 304 DNA-binding transcription factors (TFs) in Escherichia coli K-12 has been described recently, with 196 TFs experimentally characterized and 108 proteins predicted by sequence comparisons. Based on 303 expression profile patterns retrieved from the Colombos database 12 clusters were identified, including hypothetical and experimentally characterized TFs, using a spectral clustering algorithm based on a 3NN graph built using 14 principal components that represent 65% of the variance of the expression data. In a posterior step, clusters were characterized in terms of their associated overrepresented functions, based on KEGG, Supfam annotations and Pfam assignments among other functional categories using an enrichment test, reinforcing the notion that the identified clusters are functionally similar among them. Based on these data, the we identified 12 clusters in which hypothetical and known TFs share similar regulatory and physiological functions, such as module associations of toxin-antitoxin (TA) systems with DNA repair mechanisms, amino acid biosynthesis, and carbon metabolism/transport, among others. This analysis has increased our knowledge about gene regulation in E. coli K-12 and can be further expanded to other organisms. Keywords: Transcription factor, Escherichia coli, Gene expression, Hypothetical proteins, Spectral clustering

The aim of this study was to characterize the expression profiles at different time points in the PTSD-like syndrome rat hippocampus and perform analyses. PTSD rat models were made as reported by Rau, and we collected the hippocampus at different time points. The lncRNAs at different time points were compared by microarray and listed. We used quantitative real-time PCR to confirm the lncRNA profiling expression data. Bioinformatics analysis was performed on EU056364_P1. Compare with control, a total of 948 lncRNAs and 2514 mRNAs were found (fold-change > 2.0) among the four time points. Additionally, bioinformatics analysis of EU056364_P1 suggested it might be involved in memory development through the target gene Camk2a.This study revealed different lncRNAs expressed at different time points in PTSD and explored the targets of PTSD.

Background Pancreatic β-cells require a constant supply of zinc to maintain normal insulin secretory function. Following co-exocytosis with insulin, zinc is replenished via the Zrt- and Irt-like (ZIP; SLC39A) family of transporters. However the ZIP paralogues of particular importance for zinc uptake, and associations with β-cell function and Type 2 Diabetes remain largely unexplored. We retrieved and statistically analysed publically available microarray and RNA-seq datasets to perform a systematic review on the expression of β-cell SLC39A paralogues. We complemented results with experimental data on expression profiling of human islets and mouse β-cell derived MIN6 cells, and compared transcriptomic and proteomic sequence conservation between human, mouse and rat. Results The 14 ZIP paralogues have 73–98% amino sequence conservation between human and rodents. We identified 18 datasets for β-cell SLC39A analysis, which compared relative expression to non-β-cells, and expression in response to PDX-1 activity, cytokines, glucose and type 2 diabetic status. Published expression data demonstrate enrichment of transcripts for ZIP7 and ZIP9 transporters within rodent β-cells and of ZIP6, ZIP7 and ZIP14 within human β-cells, with ZIP1 most differentially expressed in response to cytokines and PDX-1 within rodent, and ZIP6 in response to diabetic status in human and glucose in rat. Our qPCR expression profiling data indicate that SLC39A6, −9, −13, and − 14 are the highest expressed paralogues in human β-cells and Slc39a6 and −7 in MIN6 cells. Conclusions Our systematic review, expression profiling and sequence alignment reveal similarities and potentially important differences in ZIP complements between human and rodent β-cells. We identify ZIP6, ZIP7, ZIP9, ZIP13 and ZIP14 in human and rodent and ZIP1 in rodent as potentially biologically important for β-cell zinc trafficking. We propose ZIP6 and ZIP7 are key functional orthologues in human and rodent β-cells and highlight these zinc importers as important targets for exploring associations between zinc status and normal physiology of β-cells and their decline in Type 2 Diabetes. Electronic supplementary material The online version of this article (10.1186/s12864-017-4119-2) contains supplementary material, which is available to authorized users.

Parkinson's disease (PD) is a progressive, degene-rative neurological disease, typically characterized by tremors and muscle rigidity. The present study aimed to identify differe-ntially expressed genes (DEGs) between patients with PD and healthy patients, and clarify their association with additional biological processes that may regulate factors that lead to PD. An integrated analysis of publicly available Gene Expression Omnibus datasets of PD was performed. DEGs were identified between PD and normal blood samples. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses, as well as protein‑protein interaction (PPI) networks were used to predict the functions of identified DEGs. Reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) was performed to validate the predicted expression levels of identified DEGs in whole blood samples obtained from patients with PD and normal healthy controls. A total of 292DEGs were identified between the PD and normal blood samples. Of these, 156 genes were significantly upregulated and 136 genes were significantly downregulated in PD samples following integrated analysis of four PD expression datasets. The 10 most upregulated and downregulated genes were used to construct a PPI network, where ubiquitin C‑terminal hydrolase L1 (UCHL1), 3‑phosphoinositide dependent protein kinase 1 (PDPK1) and protein kinase cAMP‑activated catalytic subunit β (PRKACB) demonstrated the highest connectivity in the network. DEGs were significantly enriched in amoebiasis, vascular smooth muscle contraction, and the Wnt and calcium signaling pathways. The expression levels of significant DEGs, UCHL1, PDPK1 and PRKACB were validated using RT‑qPCR analysis. The findings revealed that UCHL1 and PDPK1 were upregulated and PRKACB was downregulated in patients with PD when compared with normal healthy controls. In conclusion, the results indicate that the significant DEGs, including UCHL1, PDPK1 and PRKACB may be associated with the development of PD. In addition, these factors may be involved in various signaling pathways, including amoebiasis, vascular smooth muscle contraction and the Wnt and calcium signaling pathways.

In criminal and civil investigations, postmortem interval is used as evidence to help sort out circumstances at the time of human death. Many biological, chemical, and physical indicators can be used to determine the postmortem interval - but most are not accurate. Here, we sought to validate an experimental design to accurately predict the time of death by analyzing the expression of hundreds of upregulated genes in two model organisms, the zebrafish and mouse. In a previous study, the death of healthy adults was conducted under strictly controlled conditions to minimize the effects of confounding factors such as lifestyle and temperature. A total of 74,179 microarray probes were calibrated using the Gene Meter approach and the transcriptional profiles of 1063 genes that significantly increased in abundance were assembled into a time series spanning from life to 48 or 96h postmortem. In this study, the experimental design involved splitting the transcription profiles into training and testing datasets, randomly selecting groups of profiles, determining the modeling parameters of the genes to postmortem time using over- and/or perfectly-defined linear regression analyses, and calculating the fit (R2) and slope of predicted versus actual postmortem times. This design was repeated several thousand to million times to find the top predictive groups of gene transcription profiles. A group of eleven zebrafish genes yielded R2 of 1 and a slope of 0.99, while a group of seven mouse liver genes yielded a R2 of 0.98 and a slope of 0.97, and seven mouse brain genes yielded a R2 of 0.95 and a slope of 0.87. In all cases, groups of gene transcripts yielded better postmortem time predictions than individual gene transcripts. The significance of this study is two-fold: selected groups of gene transcripts provide accurate prediction of postmortem time, and the successfully validated experimental design can now be used to accurately predict postmortem time in cadavers.

Heterogeneity in transcriptional data hampers the identification of differentially expressed genes (DEGs) and understanding of cancer, essentially because current methods rely on cross-sample normalization and/or distribution assumption—both sensitive to heterogeneous values. Here, we developed a new method, Cross-Value Association Analysis (CVAA), which overcomes the limitation and is more robust to heterogeneous data than the other methods. Applying CVAA to a more complex pan-cancer dataset containing 5,540 transcriptomes discovered numerous new DEGs and many previously rarely explored pathways/processes; some of them were validated, both in vitro and in vivo, to be crucial in tumorigenesis, e.g., alcohol metabolism (ADH1B), chromosome remodeling (NCAPH) and complement system (Adipsin). Together, we present a sharper tool to navigate large-scale expression data and gain new mechanistic insights into tumorigenesis.