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Abstract Background Aggressive malignancies, such as pancreatic cancer, are increasingly impacting young, female populations. Our investigation centered on whether the observed trends in cancer incidence were unique to pancreatic cancer or indicative of a broader trend across various cancer types. To delve deeper into this phenomenon, we analyzed cancer incidence trends across different age and sex groups. Furthermore, we explored differences in cancer incidence within specific young subgroups aged 18 to 26 and 27 to 34, to better understand the emerging incidence trend among young individuals. Methods This study collected cancer incidence data from one of the Surveillance, Epidemiology, and End Results cancer registry databases (SEER22), with 10,183,928 total cases from 2000 to 2020. Data were analyzed through Joinpoint trend analysis approach to evaluate sex- and age-specific trends in cancer incidence. Exposure rates were reported as Average Annual Percentage Changes (AAPCs). Results The analysis revealed significant age and sex-specific disparities, particularly among individuals aged 18–26 and 27–34. Pancreatic cancer incidence rates increased more in females aged 18–26 (AAPC, 9.37% [95% CI, 7.36–11.41%]; p

Abstract Endothelial cells (EC) differentiate from multiple sources, including the cardiopharyngeal mesoderm, which gives rise also to cardiac and branchiomeric muscles. The enhancers activated during endothelial differentiation within the cardiopharyngeal mesoderm are not completely known. Here, we use a cardiogenic mesoderm differentiation model that activates an endothelial transcription program to identify endothelial regulatory elements activated in early cardiogenic mesoderm. Integrating chromatin remodeling and gene expression data with available single-cell RNA-seq data from mouse embryos, we identify 101 putative regulatory elements of EC genes. We then apply a machine-learning strategy, trained on validated enhancers, to predict enhancers. Using this computational assay, we determine that 50% of these sequences are likely enhancers, some of which are already reported. We also identify a smaller set of regulatory elements of well-known EC genes and validate them using genetic and epigenetic perturbation. Finally, we integrate multiple data sources and computational tools to search for transcriptional factor binding motifs. In conclusion, we show EC regulatory sequences with a high likelihood to be enhancers, and we validate a subset of them using computational and cell culture models. Motif analyses show that the core EC transcription factors GATA/ETS/FOS is a likely driver of EC regulation in cardiopharyngeal mesoderm.

Abstract Background Osteoarthritis (OA) is a multifactorial, hypertrophic, and degenerative condition involving the whole joint and affecting a high percentage of middle-aged people. It is due to a combination of factors, although the pivotal mechanisms underlying the disease are still obscure. Moreover, current treatments are still poorly effective, and patients experience a painful and degenerative disease course. Methods We used an integrative approach that led us to extract a consensus signature from a meta-analysis of three different OA cohorts. We performed a network-based drug prioritization to detect the most relevant drugs targeting these genes and validated in vitro the most promising candidates. We also proposed a risk score based on a minimal set of genes to predict the OA clinical stage from RNA-Seq data. Results We derived a consensus signature of 44 genes that we validated on an independent dataset. Using network analysis, we identified Resveratrol, Tenoxicam, Benzbromarone, Pirinixic Acid, and Mesalazine as putative drugs of interest for therapeutics in OA for anti-inflammatory properties. We also derived a list of seven gene-targets validated with functional RT-qPCR assays, confirming the in silico predictions. Finally, we identified a predictive subset of genes composed of DNER, TNFSF11, THBS3, LOXL3, TSPAN2, DYSF, ASPN and HTRA1 to compute the patient’s risk score. We validated this risk score on an independent dataset with a high AUC (0.875) and compared it with the same approach computed using the entire consensus signature (AUC 0.922). Conclusions The consensus signature highlights crucial mechanisms for disease progression. Moreover, these genes were associated with several candidate drugs that could represent potential innovative therapeutics. Furthermore, the patient’s risk scores can be used in clinical settings.

Abstract Background Imprinting Control Regions (ICRs) are CpG-rich sequences acquiring differential methylation in the female and male germline and maintaining it in a parental origin-specific manner in somatic cells. Despite their expected high mutation rate due to spontaneous deamination of methylated cytosines, ICRs show conservation of CpG-richness and CpG-containing transcription factor binding sites in mammalian species. However, little is known about the mechanisms contributing to the maintenance of a high density of methyl CpGs at these loci. Results To gain functional insights into the mechanisms for maintaining CpG methylation, we sought to identify the proteins binding the methylated allele of the ICRs by determining the interactors of ZFP57 that recognizes a methylated hexanucleotide motif of these DNA regions in mouse ESCs. By using a tagged approach coupled to LC–MS/MS analysis, we identified several proteins, including factors involved in mRNA processing/splicing, chromosome organization, transcription and DNA repair processes. The presence of the post-replicative mismatch-repair (MMR) complex components MSH2 and MSH6 among the identified ZFP57 interactors prompted us to investigate their DNA binding profile by chromatin immunoprecipitation and sequencing. We demonstrated that MSH2 was enriched at gene promoters overlapping unmethylated CpG islands and at repeats. We also found that both MSH2 and MSH6 interacted with the methylated allele of the ICRs, where their binding to DNA was mediated by the ZFP57/KAP1 complex. Conclusions Our findings show that the MMR complex is concentrated on gene promoters and repeats in mouse ESCs, suggesting that maintaining the integrity of these regions is a primary function of highly proliferating cells. Furthermore, the demonstration that MSH2/MSH6 are recruited to the methylated allele of the ICRs through interaction with ZFP57/KAP1 suggests a role of the MMR complex in the maintenance of the integrity of these regulatory regions and evolution of genomic imprinting in mammalian species.

Parent-of-origin-dependent gene expression of a few hundred human genes is achieved by differential DNA methylation of both parental alleles. This imprinting is required for normal development, and defects in this process lead to human disease. Induced pluripotent stem cells (iPSCs) serve as a valuable tool for in vitro disease modeling. However, a wave of de novo DNA methylation during reprogramming of iPSCs affects DNA methylation, thus limiting their use. The DNA methyltransferase 3B (DNMT3B) gene is highly expressed in human iPSCs; however, whether the hypermethylation of imprinted loci depends on DNMT3B activity has been poorly investigated. To explore the role of DNMT3B in mediating de novo DNA methylation at imprinted DMRs, we utilized iPSCs generated from patients with immunodeficiency, centromeric instability, facial anomalies type I (ICF1) syndrome that harbor biallelic hypomorphic DNMT3B mutations. Using a whole-genome array-based approach, we observed a gain of methylation at several imprinted loci in control iPSCs but not in ICF1 iPSCs compared to their parental fibroblasts. Moreover, in corrected ICF1 iPSCs, which restore DNMT3B enzymatic activity, imprinted DMRs did not acquire control DNA methylation levels, in contrast to the majority of the hypomethylated CpGs in the genome that were rescued in the corrected iPSC clones. Overall, our study indicates that DNMT3B is responsible for de novo methylation of a subset of imprinted DMRs during iPSC reprogramming and suggests that imprinting is unstable during a specific time window of this process, after which the epigenetic state at these regions becomes resistant to perturbation.

Perturbations of the cardiopharyngeal mesoderm can lead to congenital defects in individuals with 22q11.2 deletion syndrome. Here the authors use single cell RNA-sequencing to identify a multilineage primed population within the mesoderm, marked by Tbx1, which has bipotent properties to form cardiac and branchiomeric muscle cells.

Abstract ZFP57 is required to maintain the germline-marked differential methylation at imprinting control regions (ICRs) in mouse embryonic stem cells (ESCs). Although DNA methylation has a key role in genomic imprinting, several imprinted genes are controlled by different mechanisms, and a comprehensive study of the relationship between DMR methylation and imprinted gene expression is lacking. To address the latter issue, we differentiated wild-type and Zfp57 -/- hybrid mouse ESCs into neural precursor cells (NPCs) and evaluated allelic expression of imprinted genes. In mutant NPCs, we observed a reduction of allelic bias of all the 32 genes that were imprinted in wild-type cells, demonstrating that ZFP57-dependent methylation is required for maintaining or acquiring imprinted gene expression during differentiation. Analysis of expression levels showed that imprinted genes expressed from the non-methylated chromosome were generally up-regulated, and those expressed from the methylated chromosome were down-regulated in mutant cells. However, expression levels of several imprinted genes acquiring biallelic expression were not affected, suggesting the existence of compensatory mechanisms that control their RNA level. Since neural differentiation was partially impaired in Zfp57-mutant cells, this study also indicates that imprinted genes and/or non-imprinted ZFP57-target genes are required for proper neurogenesis in cultured ESCs.

In this paper, we consider the problem of estimating the graphs of conditional dependencies between variables (i.e., graphical models) from multiple datasets under Gaussian settings. We present jewel 2.0, which improves our previous method jewel 1.0 by modeling commonality and class-specific differences in the graph structures and better estimating graphs with hubs, making this new approach more appealing for biological data applications. We introduce these two improvements by modifying the regression-based problem formulation and the corresponding minimization algorithm. We also present, for the first time in the multiple graphs setting, a stability selection procedure to reduce the number of false positives in the estimated graphs. Finally, we illustrate the performance of jewel 2.0 through simulated and real data examples. The method is implemented in the new version of the R package \({\texttt{jewel}}\).

Mass spectrometry is a widely applied technology with a strong impact in the proteomics field. MALDI-TOF is a combined technology in mass spectrometry with many applications in characterizing biological samples from different sources, such as the identification of cancer biomarkers, the detection of food frauds, the identification of doping substances in athletes’ fluids, and so on. The massive quantity of data, in the form of mass spectra, are often biased and altered by different sources of noise. Therefore, extracting the most relevant features that characterize the samples is often challenging and requires combining several computational methods. Here, we present GeenaR, a novel web tool that provides a complete workflow for pre-processing, analyzing, visualizing, and comparing MALDI-TOF mass spectra. GeenaR is user-friendly, provides many different functionalities for the analysis of the mass spectra, and supports reproducible research since it produces a human-readable report that contains function parameters, results, and the code used for processing the mass spectra. First, we illustrate the features available in GeenaR. Then, we describe its internal structure. Finally, we prove its capabilities in analyzing oncological datasets by presenting two case studies related to ovarian cancer and colorectal cancer. GeenaR is available at http://proteomics.hsanmartino.it/geenar/.

During last years "irreproducibility" became a general problem in omics data analysis due to the use of sophisticated and poorly described computational procedures. For avoiding misleading results, it is necessary to inspect and reproduce the entire data analysis as a unified product. Reproducible Research (RR) provides general guidelines for public access to the analytic data and related analysis code combined with natural language documentation, allowing third-parties to reproduce the findings. We developed easyreporting, a novel R/Bioconductor package, to facilitate the implementation of an RR layer inside reports/tools. We describe the main functionalities and illustrate the organization of an analysis report using a typical case study concerning the analysis of RNA-seq data. Then, we show how to use easyreporting in other projects to trace R functions automatically. This latter feature helps developers to implement procedures that automatically keep track of the analysis steps. Easyreporting can be useful in supporting the reproducibility of any data analysis project and shows great advantages for the implementation of R packages and GUIs. It turns out to be very helpful in bioinformatics, where the complexity of the analyses makes it extremely difficult to trace all the steps and parameters used in the study.

The T-box transcription factor TBX1 has critical roles in the cardiopharyngeal lineage and the gene is haploinsufficient in DiGeorge syndrome, a typical developmental anomaly of the pharyngeal apparatus. Despite almost two decades of research, if and how TBX1 function triggers chromatin remodeling is not known. Here, we explored genome-wide gene expression and chromatin remodeling in two independent cellular models of Tbx1 loss of function, mouse embryonic carcinoma cells P19Cl6, and mouse embryonic stem cells (mESCs). The results of our study revealed that the loss or knockdown of TBX1 caused extensive transcriptional changes, some of which were cell type-specific, some were in common between the two models. However, unexpectedly we observed only limited chromatin changes in both systems. In P19Cl6 cells, differentially accessible regions (DARs) were not enriched in T-BOX binding motifs; in contrast, in mESCs, 34% (n = 47) of all DARs included a T-BOX binding motif and almost all of them gained accessibility in Tbx1–/– cells. In conclusion, despite a clear transcriptional response of our cell models to loss of TBX1 in early cell differentiation, chromatin changes were relatively modest.

Identifying relevant genomic features that can act as prognostic markers for building predictive survival models is one of the central themes in medical research, affecting the future of personalized medicine and omics technologies. However, the high dimension of genome-wide omic data, the strong correlation among the features, and the low sample size significantly increase the complexity of cancer survival analysis, demanding the development of specific statistical methods and software. Here, we present a novel R package, COSMONET (COx Survival Methods based On NETworks), that provides a complete workflow from the pre-processing of omics data to the selection of gene signatures and prediction of survival outcomes. In particular, COSMONET implements (i) three different screening approaches to reduce the initial dimension of the data from a high-dimensional space p to a moderate scale d, (ii) a network-penalized Cox regression algorithm to identify the gene signature, (iii) several approaches to determine an optimal cut-off on the prognostic index (PI) to separate high- and low-risk patients, and (iv) a prediction step for patients’ risk class based on the evaluation of PIs. Moreover, COSMONET provides functions for data pre-processing, visualization, survival prediction, and gene enrichment analysis. We illustrate COSMONET through a step-by-step R vignette using two cancer datasets.

Population genetics focuses on the analysis of genetic differences within and between-group of individuals and the inference of the populations' structure. These analyses are usually carried out using Bayesian clustering or maximum likelihood estimation algorithms that assign individuals to a given population depending on specific genetic patterns. Although several tools were developed to perform population genetics analysis, their standard graphical outputs may not be sufficiently informative for users lacking interactivity and complete information. StructuRly aims to resolve this problem by offering a complete environment for population analysis. In particular, StructuRly combines the statistical power of the R language with the friendly interfaces implemented using the shiny libraries to provide a novel tool for performing population clustering, evaluating several genetic indexes, and comparing results. Moreover, graphical representations are interactive and can be easily personalized. StructuRly is available either as R package on GitHub, with detailed information for its installation and use and as shinyapps.io servers for those users who are not familiar with R and the RStudio IDE. The application has been tested on Linux, macOS and Windows operative systems and can be launched as a shiny app in every web browser.

Single-cell RNA-seq (scRNAseq) is a powerful tool to study heterogeneity of cells. Recently, several clustering based methods have been proposed to identify distinct cell populations. These methods are based on different statistical models and usually require to perform several additional steps, such as preprocessing or dimension reduction, before applying the clustering algorithm. Individual steps are often controlled by method-specific parameters, permitting the method to be used in different modes on the same datasets, depending on the user choices. The large number of possibilities that these methods provide can intimidate non-expert users, since the available choices are not always clearly documented. In addition, to date, no large studies have invistigated the role and the impact that these choices can have in different experimental contexts. This work aims to provide new insights into the advantages and drawbacks of scRNAseq clustering methods and describe the ranges of possibilities that are offered to users. In particular, we provide an extensive evaluation of several methods with respect to different modes of usage and parameter settings by applying them to real and simulated datasets that vary in terms of dimensionality, number of cell populations or levels of noise. Remarkably, the results presented here show that great variability in the performance of the models is strongly attributed to the choice of the user-specific parameter settings. We describe several tendencies in the performance attributed to their modes of usage and different types of datasets, and identify which methods are strongly affected by data dimensionality in terms of computational time. Finally, we highlight some open challenges in scRNAseq data clustering, such as those related to the identification of the number of clusters.

The High-throughput Chromosome Conformation Capture (Hi-C) technique combines the power of the Next Generation Sequencing technologies with chromosome conformation capture approach to study the 3D chromatin organization at the genome-wide scale. Although such a technique is quite recent, many tools are already available for pre-processing and analyzing Hi-C data, allowing to identify chromatin loops, topological associating domains and A/B compartments. However, only a few of them provide an exhaustive analysis pipeline or allow to easily integrate and visualize other omic layers. Moreover, most of the available tools are designed for expert users, who have great confidence with command-line applications. In this paper, we present HiCeekR (https://github.com/lucidif/HiCeekR), a novel R Graphical User Interface (GUI) that allows researchers to easily perform a complete Hi-C data analysis. With the aid of the Shiny libraries, it integrates several R/Bioconductor packages for Hi-C data analysis and visualization, guiding the user during the entire process. Here, we describe its architecture and functionalities, then illustrate its capabilities using a publicly available dataset.

In this paper, we consider the problem of estimating multiple Gaussian Graphical Models from high-dimensional datasets. We assume that these datasets are sampled from different distributions with the same conditional independence structure, but not the same precision matrix. We propose jewel, a joint data estimation method that uses a node-wise penalized regression approach. In particular, jewel uses a group Lasso penalty to simultaneously guarantee the resulting adjacency matrix’s symmetry and the graphs’ joint learning. We solve the minimization problem using the group descend algorithm and propose two procedures for estimating the regularization parameter. Furthermore, we establish the estimator’s consistency property. Finally, we illustrate our estimator’s performance through simulated and real data examples on gene regulatory networks.

Stem cell plasticity is crucial for early embryo development and the differentiation of stem cells. Here, the authors show that the extracellular protein Cripto sustains mouse ESC self-renewal and maintains mouse EpiSC as well as human ESC pluripotency and controls the metabolic reprogramming in ESCs to EpiSC transition.

Free-living bacteria grown under aerobic conditions were used to investigate, by next-generation RNA sequencing analysis, the transcriptional profiles of Sinorhizobium meliloti wild-type 1021 and its derivative, RD64, overproducing the main auxin indole-3-acetic acid (IAA). Among the upregulated genes in RD64 cells, we detected the main nitrogen-fixation regulator fixJ, the two intermediate regulators fixK and nifA, and several other genes known to be FixJ targets. The gene coding for the sigma factor RpoH1 and other genes involved in stress response, regulated in a RpoH1-dependent manner in S. meliloti, were also induced in RD64 cells. Under microaerobic condition, quantitative real-time polymerase chain reaction analysis revealed that the genes fixJL and nifA were up-regulated in RD64 cells as compared with 1021 cells. This work provided evidence that the overexpression of IAA in S. meliloti free-living cells induced many of the transcriptional changes that normally occur in nitrogen-fixing root nodule.

Deficit in transcription factor Tbx1 causes heart defects in humans and mice. Here the authors show that Tbx1 regulates gene expression by recruiting histone methyltransferases that affect chromatin marks, and that a drug inhibiting histone demethylation ameliorates the cardiovascular phenotype in Tbx1 haploinsufficient or hypomorphic mice.

Breast cancer is one of the most common invasive tumors causing high mortality among women. It is characterized by high heterogeneity regarding its biological and clinical characteristics. Several high-throughput assays have been used to collect genome-wide information for many patients in large collaborative studies. This knowledge has improved our understanding of its biology and led to new methods of diagnosing and treating the disease. In particular, system biology has become a valid approach to obtain better insights into breast cancer biological mechanisms. A crucial component of current research lies in identifying novel biomarkers that can be predictive for breast cancer patient prognosis on the basis of the molecular signature of the tumor sample. However, the high dimension and low sample size of data greatly increase the difficulty of cancer survival analysis demanding for the development of ad-hoc statistical methods. In this work, we propose novel screening-network methods that predict patient survival outcome by screening key survival-related genes and we assess the capability of the proposed approaches using METABRIC dataset. In particular, we first identify a subset of genes by using variable screening techniques on gene expression data. Then, we perform Cox regression analysis by incorporating network information associated with the selected subset of genes. The novelty of this work consists in the improved prediction of survival responses due to the different types of screenings (i.e., a biomedical-driven, data-driven and a combination of the two) before building the network-penalized model. Indeed, the combination of the two screening approaches allows us to use the available biological knowledge on breast cancer and complement it with additional information emerging from the data used for the analysis. Moreover, we also illustrate how to extend the proposed approaches to integrate an additional omic layer, such as copy number aberrations, and we show that such strategies can further improve our prediction capabilities. In conclusion, our approaches allow to discriminate patients in high-and low-risk groups using few potential biomarkers and therefore, can help clinicians to provide more precise prognoses and to facilitate the subsequent clinical management of patients at risk of disease.

Indole-3-acetic acid (IAA) is the main auxin acting as a phytohormone in many plant developmental processes. The ability to synthesize IAA is widely associated with plant growth-promoting rhizobacteria (PGPR). Several studies have been published on the potential application of PGPR to improve plant growth through the enhancement of their main metabolic processes. In this study, the IAA-overproducing Ensifer meliloti strain RD64 and its parental strain 1021 were used to inoculate Medicago sativa plants. After verifying that the endogenous biosynthesis of IAA did not lead to genomic changes during the initial phases of the symbiotic process, we analyzed whether the overproduction of bacterial IAA inside root nodules influenced, in a coordinated manner, the activity of the nitrogen-fixing apparatus and the photosynthetic function, which are the two processes playing a key role in legume plant growth and productivity. Higher nitrogen-fixing activity and a greater amount of total nitrogen (N), carbon (C), Rubisco, nitrogen-rich amino acids, soluble sugars, and organic acids were measured for RD64-nodulated plants compared to the plants nodulated by the wild-type strain 1021. Furthermore, the RD64-nodulated plants showed a biomass increase over time, with the highest increment (more than 60%) being reached at six weeks after infection. Our findings show that the RD64-nodulated plants need more substrate derived from photosynthesis to generate the ATP required for their increased nitrogenase activity. This high carbohydrate demand further stimulates the photosynthetic function with the production of molecules that can be used to promote plant growth. We thus speculate that the use of PGPR able to stimulate both C and N metabolism with a balanced C/N ratio represents an efficient strategy to obtain substantial gains in plant productivity.

Morphometric, biochemical and genetic analyses were conducted on Olea europaea L. of Campania, an area of Southern Italy highly suited to the cultivation of olive trees and the production of extra virgin olive oil (EVOO). We aimed to characterize the distribution of morphological, biochemical and genetic diversity in this area and to develop a practical tool to aid traceability of oils. Phenotypes were characterized using morphometric data of drupes and leaves; biochemical and genetic diversity were assessed on the basis of the fatty acid composition of the EVOOs and with microsatellite markers, respectively. We provide an open-source tool as a novel R package titled ‘OliveR’, useful in performing multivariate data analysis using a point and click interactive approach. These analyses highlight a clear correlation among the morphological, biochemical and genetic profiles of samples with four collection sites, and confirm that Southern Italy represents a wide reservoir of phenotypic and genetic variability.

Experiments of cell migration and chemotaxis assays have been classically performed in the so-called Boyden Chambers. A recent technology, xCELLigence Real Time Cell Analysis, is now allowing to monitor the cell migration in real time. This technology measures impedance changes caused by the gradual increase of electrode surface occupation by cells during the course of time and provide a Cell Index which is proportional to cellular morphology, spreading, ruffling and adhesion quality as well as cell number. In this paper we propose a macroscopic mathematical model, based on advection-reaction-diffusion partial differential equations, describing the cell migration assay using the real-time technology. We carried out numerical simulations to compare simulated model dynamics with data of observed biological experiments on three different cell lines and in two experimental settings: absence of chemotactic signals (basal migration) and presence of a chemoattractant. Overall we conclude that our minimal mathematical model is able to describe the phenomenon in the real time scale and numerical results show a good agreement with the experimental evidences.

A major goal of bioinformatics is the characterization of transcription factors and the transcriptional programs they regulate. Given the speed of genome sequencing, we would like to quickly annotate regulatory sequences in newly-sequenced genomes. In such cases, it would be helpful to predict sequence motifs by using experimental data from closely related model organism. Here we present a general algorithm that allow to identify transcription factor binding sites in one newly sequenced species by performing Bayesian regression on the annotated species. First we set the rationale of our method by applying it within the same species, then we extend it to use data available in closely related species. Finally, we generalise the method to handle the case when a certain number of experiments, from several species close to the species on which to make inference, are available. In order to show the performance of the method, we analyse three functionally related networks in the Ascomycota. Two gene network case studies are related to the G2/M phase of the Ascomycota cell cycle; the third is related to morphogenesis. We also compared the method with MatrixReduce and discuss other types of validation and tests. The first network is well known and provides a biological validation test of the method. The two cell cycle case studies, where the gene network size is conserved, demonstrate an effective utility in annotating new species sequences using all the available replicas from model species. The third case, where the gene network size varies among species, shows that the combination of information is less powerful but is still informative. Our methodology is quite general and could be extended to integrate other high-throughput data from model organisms.

Hybridization- and tag-based technologies have been successfully used in Down syndrome to identify genes involved in various aspects of the pathogenesis. However, these technologies suffer from several limits and drawbacks and, to date, information about rare, even though relevant, RNA species such as long and small non-coding RNAs, is completely missing. Indeed, none of published works has still described the whole transcriptional landscape of Down syndrome. Although the recent advances in high-throughput RNA sequencing have revealed the complexity of transcriptomes, most of them rely on polyA enrichment protocols, able to detect only a small fraction of total RNA content. On the opposite end, massive-scale RNA sequencing on rRNA-depleted samples allows the survey of the complete set of coding and non-coding RNA species, now emerging as novel contributors to pathogenic mechanisms. Hence, in this work we analysed for the first time the complete transcriptome of human trisomic endothelial progenitor cells to an unprecedented level of resolution and sensitivity by RNA-sequencing. Our analysis allowed us to detect differential expression of even low expressed genes crucial for the pathogenesis, to disclose novel regions of active transcription outside yet annotated loci, and to investigate a plethora of non-polyadenylated long as well as short non coding RNAs. Novel splice isoforms for a large subset of crucial genes, and novel extended untranslated regions for known genes--possibly novel miRNA targets or regulatory sites for gene transcription--were also identified in this study. Coupling the rRNA depletion of samples, followed by high-throughput RNA-sequencing, to the easy availability of these cells renders this approach very feasible for transcriptome studies, offering the possibility of investigating in-depth blood-related pathological features of Down syndrome, as well as other genetic disorders.