The highly conserved zinc finger CCCTC-binding factor (CTCF) regulates genomic imprinting and gene expression by acting as a transcriptional activator or repressor of promoters and insulator of enhancers. The multiple functions of CTCF are accomplished by co-association with other protein partners and are dependent on genomic context and tissue specificity. Despite the critical role of CTCF in the organization of genome structure, to date, only a subset of CTCF interaction partners have been identified. Here we present a large-scale identification of CTCF binding partners using affinity purification and high-resolution LC-MS/MS analysis. In addition to functional enrichment of specific protein families such as the ribosomal proteins and the DEAD box helicases, we identified novel high-confidence CTCF interactors that provide a still unexplored biochemical context for CTCF's multiple functions. One of the newly validated CTCF interactors is BRG1, the major ATPase subunit of the chromatin remodeling complex SWI/SNF, establishing a relationship between two master regulators of genome organization. This work significantly expands the current knowledge of the human CTCF interactome and represents an important resource to direct future studies aimed at uncovering molecular mechanisms modulating CTCF pleiotropic functions throughout the genome.

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

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.

Background A subset of individuals affected by imprinting disorders displays multi-locus imprinting disturbances (MLID). MLID has been associated with maternal-effect variants that alter the maintenance of methylation at germline-derived differentially methylated regions (gDMRs) in early embryogenesis. Pedigrees of individuals with MLID also include siblings with healthy phenotype. However, it is unknown if these healthy individuals have MLID themselves or if their methylation patterns differ from those associated with imprinting disorders, and in general, if MLID affects the clinical phenotype. Methods We have investigated gDMR methylation by locus-specific and whole-genome analyses in a family with multiple pregnancy losses, a child with Beckwith-Wiedemann syndrome (BWS) and a further child with no clinical diagnosis of imprinting disorder or other pathologies. Results We detected MLID with different methylation profiles in the BWS-affected and healthy siblings. Whole-exome sequencing demonstrated the presence of novel loss-of-function variants of NLRP5 in compound heterozygosity in the mother. The methylation profiles of the two siblings were compared with those of other cases with MLID and control groups by principal component analysis and unsupervised hierarchical clustering, but while their patterns were clearly separated from those of controls, we were unable to cluster those associated with specific clinical phenotypes among the MLID cases. Conclusion The identification of two novel maternal-effect variants of NLRP5 associated with poly-abortivity and MLID adds further evidence to the role of this gene in the maintenance of genomic imprinting in early embryos. Furthermore, our results demonstrate that within these pedigrees, MLID can also be present in the progeny with healthy phenotype, indicating that some sort of compensation occurs between altered imprinted loci in these individuals. The analysis of larger cohorts of patients with MLID is needed to formulate more accurate epigenotype-phenotype correlations.

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.

Purpose: Beckwith-Wiedemann syndrome (BWS) is a developmental disorder caused by dysregulation of the imprinted gene cluster of chromosome 11p15.5 and often associated with loss of methylation (LOM) of the imprinting center 2 (IC2) located in KCNQ1 intron 10. To unravel the etiological mechanisms underlying these epimutations, we searched for genetic variants associated with IC2 LOM. Methods: We looked for cases showing the clinical features of both BWS and long QT syndrome (LQTS), which is often associated with KCNQ1 variants. Pathogenic variants were identified by genomic analysis and targeted sequencing. Functional experiments were performed to link these pathogenic variants to the imprinting defect. Results: We found three rare cases in which complete IC2 LOM is associated with maternal transmission of KCNQ1 variants, two of which were demonstrated to affect KCNQ1 transcription upstream of IC2. As a consequence of KCNQ1 haploinsufficiency, these variants also cause LQTS on both maternal and paternal transmission. Conclusion: These results are consistent with the hypothesis that, similar to what has been demonstrated in mouse, lack of transcription across IC2 results in failure of methylation establishment in the female germline and BWS later in development, and also suggest a new link between LQTS and BWS that is important for genetic counseling.

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.

Hypothesis testing is a statistical decisional process that allows one to choose between two complementary possibilities on the basis of samples drawn from the population(s) of interest. The two possibilities are called the null and alternative hypothesis, respectively. For each decision, two types of errors might occur, i.e., rejecting the null hypothesis when it is true (Type I error) or accepting the null hypothesis when it is false (Type II error). The decision is taken by compromising the two error types. When multiple hypotheses are compared one also has to define and control the overall decisional error.

Linear regression models a dependent variable Y in terms of a linear combination of p independent variables X=[X1|...|Xp] and estimates the coefficients of the combination using independent observations (x_i,Y_i ),i=1,...,n. The Gauss-Markov conditions guarantees that the least squares estimate of the regression coefficients constitutes the best linear estimator. Under the assumption of white noise, it is possible to test the significance of each regression coefficient, evaluate the uncertainty/goodness of fit, and use the fitted model for predicting novel outcomes. When p>n, classical linear regression cannot be applied, and penalized approaches such as ridge regression, lasso or elastic net have to be used.

This BMC special issue collects a selection of eleven revised and extended papers presented at, or originated from, the 2015 and 2016 editions of the international meeting on Computational Intelligence methods for Bioinformatics and Biostatistics, CIBB2015 and CIBB2016, respectively.

Selection of papers from BBCC2017

Hypomorphic mutations in DNA-methyltransferase DNMT3B cause majority of the rare disorder Immunodeficiency, Centromere instability and Facial anomalies syndrome cases (ICF1). By unspecified mechanisms, mutant-DNMT3B interferes with lymphoid-specific pathways resulting in immune response defects. Interestingly, recent findings report that DNMT3B shapes intragenic CpG-methylation of highly-transcribed genes. However, how the DNMT3B-dependent epigenetic network modulates transcription and whether ICF1-specific mutations impair this process remains unknown. We performed a transcriptomic and epigenomic study in patient-derived B-cell lines to investigate the genome-scale effects of DNMT3B dysfunction. We highlighted that altered intragenic CpG-methylation impairs multiple aspects of transcriptional regulation, like alternative TSS usage, antisense transcription and exon splicing. These defects preferentially associate with changes of intragenic H3K4me3 and at lesser extent of H3K27me3 and H3K36me3. In addition, we highlighted a novel DNMT3B activity in modulating the self-regulatory circuit of sense-antisense pairs and the exon skipping during alternative splicing, through interacting with RNA molecules. Strikingly, altered transcription affects disease relevant genes, as for instance the memory-B cell marker CD27 and PTPRC genes, providing us with biological insights into the ICF1-syndrome pathogenesis. Our genome-scale approach sheds light on the mechanisms still poorly understood of the intragenic function of DNMT3B and DNA methylation in gene expression regulation.

Vaccination is the most successful and cost-effective method to prevent infectious diseases. However, many vaccine antigens have poor in vivo immunogenic potential and need adjuvants to enhance immune response. The application of systems biology to immunity and vaccinology has yielded crucial insights about how vaccines and adjuvants work. We have previously characterized two safe and powerful delivery systems derived from non-pathogenic prokaryotic organisms: E2 and fd filamentous bacteriophage systems. They elicit an in vivo immune response inducing CD8+ T-cell responses, even in absence of adjuvants or stimuli for dendritic cells' maturation. Nonetheless, a systematic and comparative analysis of the complex gene expression network underlying such activation is missing. Therefore, we compared the transcriptomes of ex vivo isolated bone marrow-derived dendritic cells exposed to these antigen delivery systems. Significant differences emerged, especially for genes involved in innate immunity, co-stimulation, and cytokine production. Results indicate that E2 drives polarization toward the Th2 phenotype, mainly mediated by Irf4, Ccl17, and Ccr4 over-expression. Conversely, fd-scalphaDEC-205 triggers Th1 T cells' polarization through the induction of Il12b, Il12rb, Il6, and other molecules involved in its signal transduction. The data analysis was performed using RNASeqGUI, hence, addressing the increasing need of transparency and reproducibility of computational analysis.

International initiatives such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) are collecting multiple datasets at different genome-scales with the aim of identifying novel cancer biomarkers and predicting survival of patients. To analyze such data, several statistical methods have been applied, among them Cox regression models. Although these models provide a good statistical framework to analyze omic data, there is still a lack of studies that illustrate advantages and drawbacks in integrating biological information and selecting groups of biomarkers. In fact, classical Cox regression algorithms focus on the selection of a single biomarker, without taking into account the strong correlation between genes. Even though network-based Cox regression algorithms overcome such drawbacks, such network-based approaches are less widely used within the life science community. In this article, we aim to provide a clear methodological framework on the use of such approaches in order to turn cancer research results into clinical applications. Therefore, we first discuss the rationale and the practical usage of three recently proposed network-based Cox regression algorithms (i.e., Net-Cox, AdaLnet, and fastcox). Then, we show how to combine existing biological knowledge and available data with such algorithms to identify networks of cancer biomarkers and to estimate survival of patients. Finally, we describe in detail a new permutation-based approach to better validate the significance of the selection in terms of cancer gene signatures and pathway/networks identification. We illustrate the proposed methodology by means of both simulations and real case studies. Overall, the aim of our work is two-fold. Firstly, to show how network-based Cox regression models can be used to integrate biological knowledge (e.g., multi-omics data) for the analysis of survival data. Secondly, to provide a clear methodological and computational approach for investigating cancers regulatory networks. International initiatives such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) are collecting multiple datasets at different genome-scales with the aim of identifying novel cancer biomarkers and predicting survival of patients. To analyze such data, several statistical methods have been applied, among them Cox regression models. Although these models provide a good statistical framework to analyze omic data, there is still a lack of studies that illustrate advantages and drawbacks in integrating biological information and selecting groups of biomarkers. In fact, classical Cox regression algorithms focus on the selection of a single biomarker, without taking into account the strong correlation between genes. Even though network-based Cox regression algorithms overcome such drawbacks, such network-based approaches are less widely used within the life science community. In this article, we aim to provide a clear methodological framework on the use of such approaches in order to turn cancer research results into clinical applications. Therefore, we first discuss the rationale and the practical usage of three recently proposed network-based Cox regression algorithms (i.e., Net-Cox, AdaLnet, and fastcox). Then, we show how to combine existing biological knowledge and available data with such algorithms to identify networks of cancer biomarkers and to estimate survival of patients. Finally, we describe in detail a new permutation based approach to better validate the significance of the selection in terms of cancer gene signatures and pathway/networks identification. We illustrate the proposed methodology by means of both simulations and real case studies. Overall, the aim of our work is two-fold. Firstly, to show how network-based Cox regression models can be used to integrate biological knowledge (e.g., multi-omics data) for the analysis of survival data. Secondly, to provide a clear methodological and computational approach for investigating cancers regulatory networks. Keywords: cancer, Cox model, high-dimensionality, gene expression, network, regularization, survival

Results: Teleosts living in different environments (freshwater and seawater) and with different lifestyles (migratory and non-migratory) were analyzed studying three variables: routine metabolic rate, gill area and genomic GC-content, none of them showing a phylogenetic signal among fish species. Routine metabolic rate, specific gill area and average genomic GC were higher in seawater than freshwater species. The same trend was observed comparing migratory versus non-migratory species. Crossing salinity and lifestyle, the active migratory species living in seawater show coincidentally the highest routine metabolic rate, the highest specific gill area and the highest average genomic GC content. Background: The DNA base composition is well known to be highly variable among organisms. Bio-physic studies on the effect of the GC increments on the DNA structure have shown that GC-richer DNA sequences are more bendable. The result was the keystone of the hypothesis proposing the metabolic rate as the major force driving the GC content variability, since an increased resistance to the torsion stress is mainly required during the transcription process to avoid DNA breakage. Hence, the aim of the present work is to test if both salinity and migration, suggested to affect the metabolic rate of teleostean fishes, affect the average genomic GC content as well. Moreover, since the gill surface has been reported to be a major morphological expression of metabolic rate, this parameter was also analyzed in the light of the above hypothesis.

We present the advancements and novelties recently introduced in RNASeqGUI, a graphical user interface that helps biologists to handle and analyse large data collected in RNA-Seq experiments. This work focuses on the concept of reproducible research and shows how it has been incorporated in RNASeqGUI to provide reproducible (computational) results. The novel version of RNASeqGUI combines graphical interfaces with tools for reproducible research, such as literate statistical programming, human readable report, parallel executions, caching, and interactive and web-explorable tables of results. These features allow the user to analyse big datasets in a fast, efficient, and reproducible way. Moreover, this paper represents a proof of concept, showing a simple way to develop computational tools for Life Science in the spirit of reproducible research.

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.

The human retina is a specialized tissue involved in light stimulus transduction. Despite its unique biology, an accurate reference transcriptome is still missing. Here, we performed gene expression analysis (RNA-seq) of 50 retinal samples from non-visually impaired post-mortem donors. We identified novel transcripts with high confidence (Observed Transcriptome (ObsT)) and quantified the expression level of known transcripts (Reference Transcriptome (RefT)). The ObsT included 77 623 transcripts (23 960 genes) covering 137 Mb (35 Mb new transcribed genome). Most of the transcripts (92%) were multi-exonic: 81% with known isoforms, 16% with new isoforms and 3% belonging to new genes. The RefT included 13 792 genes across 94 521 known transcripts. Mitochondrial genes were among the most highly expressed, accounting for about 10% of the reads. Of all the protein-coding genes in Gencode, 65% are expressed in the retina. We exploited inter-individual variability in gene expression to infer a gene co-expression network and to identify genes specifically expressed in photoreceptor cells. We experimentally validated the photoreceptors localization of three genes in human retina that had not been previously reported. RNA-seq data and the gene co-expression network are available online (http://retina.tigem.it).