Transcriptomic meta-analysis characterizes molecular commonalities between psoriasis and obesity.

Despite the abundance of epidemiological evidence for the high comorbid rate between psoriasis and obesity, systematic approaches to common inflammatory mechanisms have not been adequately explored. We performed a meta-analysis of publicly available RNA-sequencing datasets to unveil putative mechanisms that are postulated to exacerbate both diseases, utilizing both late-stage, disease-specific meta-analyses and consensus gene co-expression network (cWGCNA). Single-gene meta-analyses reported several common inflammatory mechanisms fostered by the perturbed expression profile of inflammatory cells. Assessment of gene overlaps between both diseases revealed significant overlaps between up- (n = 170, P value = 6.07 × 10-65) and down-regulated (n = 49, P value = 7.1 × 10-7) genes, associated with increased T cell response and activated transcription factors. Our cWGCNA approach disentangled 48 consensus modules, associated with either the differentiation of leukocytes or metabolic pathways with similar correlation signals in both diseases. Notably, all our analyses confirmed the association of the perturbed T helper (Th)17 differentiation pathway in both diseases. Our novel findings through whole transcriptomic analyses characterize the inflammatory commonalities between psoriasis and obesity implying the assessment of several expression profiles that could serve as putative comorbid disease progression biomarkers and therapeutic interventions.

Transcriptional repression of lncRNA and miRNA subsets mediated by LRF during erythropoiesis.

Non-coding RNA (ncRNA) species, mainly long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have been currently imputed for lesser or greater involvement in human erythropoiesis. These RNA subsets operate within a complex circuit with other epigenetic components and transcription factors (TF) affecting chromatin remodeling during cell differentiation. Lymphoma/leukemia-related (LRF) TF exerts higher occupancy on DNA CpG rich sites and is implicated in several differentiation cell pathways and erythropoiesis among them and also directs the epigenetic regulation of hemoglobin transversion from fetal (HbF) to adult (HbA) form by intervening in the γ-globin gene repression. We intended to investigate LRF activity in the evolving landscape of cells' commitment to the erythroid lineage and specifically during HbF to HbA transversion, to qualify this TF as potential repressor of lncRNAs and miRNAs. Transgenic human erythroleukemia cells, overexpressing LRF and further induced to erythropoiesis, were subjected to expression analysis in high LRF occupancy genetic loci-producing lncRNAs. LRF abundance in genetic loci transcribing for studied lncRNAs was determined by ChIP-Seq data analysis. qPCRs were performed to examine lncRNA expression status. Differentially expressed miRNA pre- and post-erythropoiesis induction were assessed by next-generation sequencing (NGS), and their promoter regions were charted. Expression levels of lncRNAs were correlated with DNA methylation status of flanked CpG islands, and contingent co-regulation of hosted miRNAs was considered. LRF-binding sites were overrepresented in LRF overexpressing cell clones during erythropoiesis induction and exerted a significant suppressive effect towards lncRNAs and miRNA collections. Based on present data interpretation, LRF's multiplied binding capacity across genome is suggested to be transient and associated with higher levels of DNA methylation. KEY MESSAGES: During erythropoiesis, LRF displays extensive occupancy across genetic loci. LRF significantly represses subsets of lncRNAs and miRNAs during erythropoiesis. Promoter region CpG islands' methylation levels affect lncRNA expression. MiRNAs embedded within lncRNA loci show differential regulation of expression.

Pharmacogenetic Analysis of the MIR146A rs2910164 and MIR155 rs767649 Polymorphisms and Response to Anti-TNF Treatment in Patients with Crohn's Disease and Psoriasis.

The clinical heterogeneity regarding the response profile of the antitumor necrosis factor (anti-TNF) in patients with Crohn's disease (CD) and psoriasis (PsO) is attributed, amongst others, to genetic factors that influence the regulatory mechanisms which orchestrate the inflammatory response. Here, we investigated the possible associations between the MIR146A rs2910164 and MIR155 rs767649 variants and the response to anti-TNF therapy in a Greek cohort of 103 CD and 100 PsO patients. We genotyped 103 CD patients and 100 PsO patients via the PCR-RFLP method, utilizing the de novo formation of a restriction site for the SacI enzyme considering the MIR146A rs2910164, while Tsp45I was employed for the MIR155 rs767649 variant. Additionally, we investigated the potential functional role of the rs767649 variant, exploring in silico the alteration of transcription factor binding sites (TFBSs) mapped on its genomic location. Our single-SNP analysis displayed a significant association between the rare rs767649 A allele and response to therapy (Bonferroni-corrected p value = 0.012) in patients with PsO, a result further enhanced by the alteration in the IRF2 TFBS caused by the above allele. Our results highlight the protective role of the rare rs767649 A allele in the clinical remission of PsO, implying its utilization as a pharmacogenetic biomarker.

DeepTSS: multi-branch convolutional neural network for transcription start site identification from CAGE data.

BACKGROUND: The widespread usage of Cap Analysis of Gene Expression (CAGE) has led to numerous breakthroughs in understanding the transcription mechanisms. Recent evidence in the literature, however, suggests that CAGE suffers from transcriptional and technical noise. Regardless of the sample quality, there is a significant number of CAGE peaks that are not associated with transcription initiation events. This type of signal is typically attributed to technical noise and more frequently to random five-prime capping or transcription bioproducts. Thus, the need for computational methods emerges, that can accurately increase the signal-to-noise ratio in CAGE data, resulting in error-free transcription start site (TSS) annotation and quantification of regulatory region usage. In this study, we present DeepTSS, a novel computational method for processing CAGE samples, that combines genomic signal processing (GSP), structural DNA features, evolutionary conservation evidence and raw DNA sequence with Deep Learning (DL) to provide single-nucleotide TSS predictions with unprecedented levels of performance. RESULTS: To evaluate DeepTSS, we utilized experimental data, protein-coding gene annotations and computationally-derived genome segmentations by chromatin states. DeepTSS was found to outperform existing algorithms on all benchmarks, achieving 98% precision and 96% sensitivity (accuracy 95.4%) on the protein-coding gene strategy, with 96.66% of its positive predictions overlapping active chromatin, 98.27% and 92.04% co-localized with at least one transcription factor and H3K4me3 peak. CONCLUSIONS: CAGE is a key protocol in deciphering the language of transcription, however, as every experimental protocol, it suffers from biological and technical noise that can severely affect downstream analyses. DeepTSS is a novel DL-based method for effectively removing noisy CAGE signal. In contrast to existing software, DeepTSS does not require feature selection since the embedded convolutional layers can readily identify patterns and only utilize the important ones for the classification task. This study highlights the key role that DL can play in Molecular Biology, by removing the inherent flaws of experimental protocols, that form the backbone of contemporary research. Here, we show how DeepTSS can unleash the full potential of an already popular and mature method such as CAGE, and push the boundaries of coding and non-coding gene expression regulator research even further.

Machine-Learning-Assisted Analysis of TCR Profiling Data Unveils Cross-Reactivity between SARS-CoV-2 and a Wide Spectrum of Pathogens and Other Diseases.

During the last two years, the emergence of SARS-CoV-2 has led to millions of deaths worldwide, with a devastating socio-economic impact on a global scale. The scientific community's focus has recently shifted towards the association of the T cell immunological repertoire with COVID-19 progression and severity, by utilising T cell receptor sequencing (TCR-Seq) assays. The Multiplexed Identification of T cell Receptor Antigen (MIRA) dataset, which is a subset of the immunoACCESS study, provides thousands of TCRs that can specifically recognise SARS-CoV-2 epitopes. Our study proposes a novel Machine Learning (ML)-assisted approach for analysing TCR-Seq data from the antigens' point of view, with the ability to unveil key antigens that can accurately distinguish between MIRA COVID-19-convalescent and healthy individuals based on differences in the triggered immune response. Some SARS-CoV-2 antigens were found to exhibit equal levels of recognition by MIRA TCRs in both convalescent and healthy cohorts, leading to the assumption of putative cross-reactivity between SARS-CoV-2 and other infectious agents. This hypothesis was tested by combining MIRA with other public TCR profiling repositories that host assays and sequencing data concerning a plethora of pathogens. Our study provides evidence regarding putative cross-reactivity between SARS-CoV-2 and a wide spectrum of pathogens and diseases, with M. tuberculosis and Influenza virus exhibiting the highest levels of cross-reactivity. These results can potentially shift the emphasis of immunological studies towards an increased application of TCR profiling assays that have the potential to uncover key mechanisms of cell-mediated immune response against pathogens and diseases.

Contribution of the Environment, Epigenetic Mechanisms and Non-Coding RNAs in Psoriasis.

Despite the increasing research and clinical interest in the predisposition of psoriasis, a chronic inflammatory skin disease, the multitude of genetic and environmental factors involved in its pathogenesis remain unclear. This complexity is further exacerbated by the several cell types that are implicated in Psoriasis's progression, including keratinocytes, melanocytes and various immune cell types. The observed interactions between the genetic substrate and the environment lead to epigenetic alterations that directly or indirectly affect gene expression. Changes in DNA methylation and histone modifications that alter DNA-binding site accessibility, as well as non-coding RNAs implicated in the post-transcriptional regulation, are mechanisms of gene transcriptional activity modification and therefore affect the pathways involved in the pathogenesis of Psoriasis. In this review, we summarize the research conducted on the environmental factors contributing to the disease onset, epigenetic modifications and non-coding RNAs exhibiting deregulation in Psoriasis, and we further categorize them based on the under-study cell types. We also assess the recent literature considering therapeutic applications targeting molecules that compromise the epigenome, as a way to suppress the inflammatory cutaneous cascade.

Gene Expression Meta-Analysis of Potential Shared and Unique Pathways between Autoimmune Diseases under Anti-TNFα Therapy.

While anti-TNFα has been established as an effective therapeutic approach for several autoimmune diseases, results from clinical trials have uncovered heterogeneous patients' response to therapy. Here, we conducted a meta-analysis on the publicly available gene expression cDNA microarray datasets that examine the differential expression observed in response to anti-TNFα therapy with psoriasis (PsO), inflammatory bowel disease (IBD) and rheumatoid arthritis (RA). Five disease-specific meta-analyses and a single combined random-effects meta-analysis were performed through the restricted maximum likelihood method. Gene Ontology and Reactome Pathways enrichment analyses were conducted, while interactions between differentially expressed genes (DEGs) were determined with the STRING database. Four IBD, three PsO and two RA datasets were identified and included in our analyses through our search criteria. Disease-specific meta-analyses detected distinct pro-inflammatory down-regulated DEGs for each disease, while pathway analyses identified common inflammatory patterns involved in the pathogenesis of each disease. Combined meta-analyses further revealed DEGs that participate in anti-inflammatory pathways, namely IL-10 signaling. Our analyses provide the framework for a transcriptomic approach in response to anti-TNFα therapy in the above diseases. Elucidation of the complex interactions involved in such multifactorial phenotypes could identify key molecular targets implicated in the pathogenesis of IBD, PsO and RA.

DIANA-miRGen v4: indexing promoters and regulators for more than 1500 microRNAs.

Deregulation of microRNA (miRNA) expression plays a critical role in the transition from a physiological to a pathological state. The accurate miRNA promoter identification in multiple cell types is a fundamental endeavor towards understanding and characterizing the underlying mechanisms of both physiological as well as pathological conditions. DIANA-miRGen v4 (www.microrna.gr/mirgenv4) provides cell type specific miRNA transcription start sites (TSSs) for over 1500 miRNAs retrieved from the analysis of >1000 cap analysis of gene expression (CAGE) samples corresponding to 133 tissues, cell lines and primary cells available in FANTOM repository. MiRNA TSS locations were associated with transcription factor binding site (TFBSs) annotation, for >280 TFs, derived from analyzing the majority of ENCODE ChIP-Seq datasets. For the first time, clusters of cell types having common miRNA TSSs are characterized and provided through a user friendly interface with multiple layers of customization. DIANA-miRGen v4 significantly improves our understanding of miRNA biogenesis regulation at the transcriptional level by providing a unique integration of high-quality annotations for hundreds of cell specific miRNA promoters with experimentally derived TFBSs.

Multi-branch Convolutional Neural Network for Identification of Small Non-coding RNA genomic loci.

Genomic regions that encode small RNA genes exhibit characteristic patterns in their sequence, secondary structure, and evolutionary conservation. Convolutional Neural Networks are a family of algorithms that can classify data based on learned patterns. Here we present MuStARD an application of Convolutional Neural Networks that can learn patterns associated with user-defined sets of genomic regions, and scan large genomic areas for novel regions exhibiting similar characteristics. We demonstrate that MuStARD is a generic method that can be trained on different classes of human small RNA genomic loci, without need for domain specific knowledge, due to the automated feature and background selection processes built into the model. We also demonstrate the ability of MuStARD for inter-species identification of functional elements by predicting mouse small RNAs (pre-miRNAs and snoRNAs) using models trained on the human genome. MuStARD can be used to filter small RNA-Seq datasets for identification of novel small RNA loci, intra- and inter- species, as demonstrated in three use cases of human, mouse, and fly pre-miRNA prediction. MuStARD is easy to deploy and extend to a variety of genomic classification questions. Code and trained models are freely available at gitlab.com/RBP_Bioinformatics/mustard.

Solving the transcription start site identification problem with ADAPT-CAGE: a Machine Learning algorithm for the analysis of CAGE data.

Cap Analysis of Gene Expression (CAGE) has emerged as a powerful experimental technique for assisting in the identification of transcription start sites (TSSs). There is strong evidence that CAGE also identifies capping sites along various other locations of transcribed loci such as splicing byproducts, alternative isoforms and capped molecules overlapping introns and exons. We present ADAPT-CAGE, a Machine Learning framework which is trained to distinguish between CAGE signal derived from TSSs and transcriptional noise. ADAPT-CAGE provides highly accurate experimentally derived TSSs on a genome-wide scale. It has been specifically designed for flexibility and ease-of-use by only requiring aligned CAGE data and the underlying genomic sequence. When compared to existing algorithms, ADAPT-CAGE exhibits improved performance on every benchmark that we designed based on both annotation- and experimentally-driven strategies. This performance boost brings ADAPT-CAGE in the spotlight as a computational framework that is able to assist in the refinement of gene regulatory networks, the incorporation of accurate information of gene expression regulators and alternative promoter usage in both physiological and pathological conditions.

A Cosine Similarity-Based Method to Infer Variability of Chromatin Accessibility at the Single-Cell Level.

Cellular identity between generations of developing cells is propagated through the epigenome particularly via the accessible parts of the chromatin. It is now possible to measure chromatin accessibility at single-cell resolution using single-cell assay for transposase accessible chromatin (scATAC-seq), which can reveal the regulatory variation behind the phenotypic variation. However, single-cell chromatin accessibility data are sparse, binary, and high dimensional, leading to unique computational challenges. To overcome these difficulties, we developed PRISM, a computational workflow that quantifies cell-to-cell chromatin accessibility variation while controlling for technical biases. PRISM is a novel multidimensional scaling-based method using angular cosine distance metrics coupled with distance from the spatial centroid. PRISM takes differences in accessibility at each genomic region between single cells into account. Using data generated in our lab and publicly available, we showed that PRISM outperforms an existing algorithm, which relies on the aggregate of signal across a set of genomic regions. PRISM showed robustness to noise in cells with low coverage for measuring chromatin accessibility. Our approach revealed the previously undetected accessibility variation where accessible sites differ between cells but the total number of accessible sites is constant. We also showed that PRISM, but not an existing algorithm, can find suppressed heterogeneity of accessibility at CTCF binding sites. Our updated approach uncovers new biological results with profound implications on the cellular heterogeneity of chromatin architecture.

Identifying Pri-miRNA Transcription Start Sites.

MicroRNAs (miRNAs) are small non-coding RNAs that can regulate gene expression playing vital role in nearly all biological pathways. Even though miRNAs have been intensely studied for more than two decades, information regarding miRNA transcription regulation remains limited. The rapid cleavage of primary miRNA transcripts (pri-miRNAs) by Drosha in the nucleus hinders their identification with conventional RNA-seq approaches. Identifying the transcription start site (TSS) of miRNAs will enable genome-wide identification of their expression regulators, including transcription factors (TFs), other non-coding RNAs (ncRNAs) and epigenetic modifiers, providing significant breakthroughs in understanding the mechanisms underlying miRNA expression in development and disease. Here we present a protocol that utilizes microTSS, a versatile computational framework for accurate and single-nucleotide resolution miRNA TSS predictions as well as miRGen, a database of miRNA gene TSSs coupled with genome-wide maps of TF binding sites.

Lineage-Determining Transcription Factor TCF-1 Initiates the Epigenetic Identity of T Cells.

T cell development is orchestrated by transcription factors that regulate the expression of genes initially buried within inaccessible chromatin, but the transcription factors that establish the regulatory landscape of the T cell lineage remain unknown. Profiling chromatin accessibility at eight stages of T cell development revealed the selective enrichment of TCF-1 at genomic regions that became accessible at the earliest stages of development. TCF-1 was further required for the accessibility of these regulatory elements and at the single-cell level, it dictated a coordinate opening of chromatin in T cells. TCF-1 expression in fibroblasts generated de novo chromatin accessibility even at chromatin regions with repressive marks, inducing the expression of T cell-restricted genes. These results indicate that a mechanism by which TCF-1 controls T cell fate is through its widespread ability to target silent chromatin and establish the epigenetic identity of T cells.

The whole genome sequence of the Mediterranean fruit fly, Ceratitis capitata (Wiedemann), reveals insights into the biology and adaptive evolution of a highly invasive pest species.

BACKGROUND: The Mediterranean fruit fly (medfly), Ceratitis capitata, is a major destructive insect pest due to its broad host range, which includes hundreds of fruits and vegetables. It exhibits a unique ability to invade and adapt to ecological niches throughout tropical and subtropical regions of the world, though medfly infestations have been prevented and controlled by the sterile insect technique (SIT) as part of integrated pest management programs (IPMs). The genetic analysis and manipulation of medfly has been subject to intensive study in an effort to improve SIT efficacy and other aspects of IPM control. RESULTS: The 479 Mb medfly genome is sequenced from adult flies from lines inbred for 20 generations. A high-quality assembly is achieved having a contig N50 of 45.7 kb and scaffold N50 of 4.06 Mb. In-depth curation of more than 1800 messenger RNAs shows specific gene expansions that can be related to invasiveness and host adaptation, including gene families for chemoreception, toxin and insecticide metabolism, cuticle proteins, opsins, and aquaporins. We identify genes relevant to IPM control, including those required to improve SIT. CONCLUSIONS: The medfly genome sequence provides critical insights into the biology of one of the most serious and widespread agricultural pests. This knowledge should significantly advance the means of controlling the size and invasive potential of medfly populations. Its close relationship to Drosophila, and other insect species important to agriculture and human health, will further comparative functional and structural studies of insect genomes that should broaden our understanding of gene family evolution.

DIANA-mirExTra v2.0: Uncovering microRNAs and transcription factors with crucial roles in NGS expression data.

Differential expression analysis (DEA) is one of the main instruments utilized for revealing molecular mechanisms in pathological and physiological conditions. DIANA-mirExTra v2.0 (http://www.microrna.gr/mirextrav2) performs a combined DEA of mRNAs and microRNAs (miRNAs) to uncover miRNAs and transcription factors (TFs) playing important regulatory roles between two investigated states. The web server uses as input miRNA/RNA-Seq read count data sets that can be uploaded for analysis. Users can combine their data with 350 small-RNA-Seq and 65 RNA-Seq in-house analyzed libraries which are provided by DIANA-mirExTra v2.0.The web server utilizes miRNA:mRNA, TF:mRNA and TF:miRNA interactions derived from extensive experimental data sets. More than 450 000 miRNA interactions and 2 000 000 TF binding sites from specific or high-throughput techniques have been incorporated, while accurate miRNA TSS annotation is obtained from microTSS experimental/in silico framework. These comprehensive data sets enable users to perform analyses based solely on experimentally supported information and to uncover central regulators within sequencing data: miRNAs controlling mRNAs and TFs regulating mRNA or miRNA expression. The server also supports predicted miRNA:gene interactions from DIANA-microT-CDS for 4 species (human, mouse, nematode and fruit fly). DIANA-mirExTra v2.0 has an intuitive user interface and is freely available to all users without any login requirement.

DIANA-miRGen v3.0: accurate characterization of microRNA promoters and their regulators.

microRNAs (miRNAs) are small non-coding RNAs that actively fine-tune gene expression. The accurate characterization of the mechanisms underlying miRNA transcription regulation will further expand our knowledge regarding their implication in homeostatic and pathobiological networks. Aim of DIANA-miRGen v3.0 (http://www.microrna.gr/mirgen) is to provide for the first time accurate cell-line-specific miRNA gene transcription start sites (TSSs), coupled with genome-wide maps of transcription factor (TF) binding sites in order to unveil the mechanisms of miRNA transcription regulation. To this end, more than 7.3 billion RNA-, ChIP- and DNase-Seq next generation sequencing reads were analyzed/assembled and combined with state-of-the-art miRNA TSS prediction and TF binding site identification algorithms. The new database schema and web interface facilitates user interaction, provides advanced queries and innate connection with other DIANA resources for miRNA target identification and pathway analysis. The database currently supports 276 miRNA TSSs that correspond to 428 precursors and >19M binding sites of 202 TFs on a genome-wide scale in nine cell-lines and six tissues of Homo sapiens and Mus musculus.

DIANA-LncBase v2: indexing microRNA targets on non-coding transcripts.

microRNAs (miRNAs) are short non-coding RNAs (ncRNAs) that act as post-transcriptional regulators of coding gene expression. Long non-coding RNAs (lncRNAs) have been recently reported to interact with miRNAs. The sponge-like function of lncRNAs introduces an extra layer of complexity in the miRNA interactome. DIANA-LncBase v1 provided a database of experimentally supported and in silico predicted miRNA Recognition Elements (MREs) on lncRNAs. The second version of LncBase (www.microrna.gr/LncBase) presents an extensive collection of miRNA:lncRNA interactions. The significantly enhanced database includes more than 70 000 low and high-throughput, (in)direct miRNA:lncRNA experimentally supported interactions, derived from manually curated publications and the analysis of 153 AGO CLIP-Seq libraries. The new experimental module presents a 14-fold increase compared to the previous release. LncBase v2 hosts in silico predicted miRNA targets on lncRNAs, identified with the DIANA-microT algorithm. The relevant module provides millions of predicted miRNA binding sites, accompanied with detailed metadata and MRE conservation metrics. LncBase v2 caters information regarding cell type specific miRNA:lncRNA regulation and enables users to easily identify interactions in 66 different cell types, spanning 36 tissues for human and mouse. Database entries are also supported by accurate lncRNA expression information, derived from the analysis of more than 6 billion RNA-Seq reads.

DIANA-miRPath v3.0: deciphering microRNA function with experimental support.

The functional characterization of miRNAs is still an open challenge. Here, we present DIANA-miRPath v3.0 (http://www.microrna.gr/miRPathv3) an online software suite dedicated to the assessment of miRNA regulatory roles and the identification of controlled pathways. The new miRPath web server renders possible the functional annotation of one or more miRNAs using standard (hypergeometric distributions), unbiased empirical distributions and/or meta-analysis statistics. DIANA-miRPath v3.0 database and functionality have been significantly extended to support all analyses for KEGG molecular pathways, as well as multiple slices of Gene Ontology (GO) in seven species (Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Caenorhabditis elegans, Gallus gallus and Danio rerio). Importantly, more than 600 000 experimentally supported miRNA targets from DIANA-TarBase v7.0 have been incorporated into the new schema. Users of DIANA-miRPath v3.0 can harness this wealth of information and substitute or combine the available in silico predicted targets from DIANA-microT-CDS and/or TargetScan v6.2 with high quality experimentally supported interactions. A unique feature of DIANA-miRPath v3.0 is its redesigned Reverse Search module, which enables users to identify and visualize miRNAs significantly controlling selected pathways or belonging to specific GO categories based on in silico or experimental data. DIANA-miRPath v3.0 is freely available to all users without any login requirement.

DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions.

microRNAs (miRNAs) are short non-coding RNA species, which act as potent gene expression regulators. Accurate identification of miRNA targets is crucial to understanding their function. Currently, hundreds of thousands of miRNA:gene interactions have been experimentally identified. However, this wealth of information is fragmented and hidden in thousands of manuscripts and raw next-generation sequencing data sets. DIANA-TarBase was initially released in 2006 and it was the first database aiming to catalog published experimentally validated miRNA:gene interactions. DIANA-TarBase v7.0 (http://www.microrna.gr/tarbase) aims to provide for the first time hundreds of thousands of high-quality manually curated experimentally validated miRNA:gene interactions, enhanced with detailed meta-data. DIANA-TarBase v7.0 enables users to easily identify positive or negative experimental results, the utilized experimental methodology, experimental conditions including cell/tissue type and treatment. The new interface provides also advanced information ranging from the binding site location, as identified experimentally as well as in silico, to the primer sequences used for cloning experiments. More than half a million miRNA:gene interactions have been curated from published experiments on 356 different cell types from 24 species, corresponding to 9- to 250-fold more entries than any other relevant database. DIANA-TarBase v7.0 is freely available.