Covering Hierarchical Dirichlet Mixture Models on binary data to enhance genomic stratifications in onco-hematology.

Onco-hematological studies are increasingly adopting statistical mixture models to support the advancement of the genomically-driven classification systems for blood cancer. Targeting enhanced patients stratification based on the sole role of molecular biology attracted much interest and contributes to bring personalized medicine closer to reality. In onco-hematology, Hierarchical Dirichlet Mixture Models (HDMM) have become one of the preferred method to cluster the genomics data, that include the presence or absence of gene mutations and cytogenetics anomalies, into components. This work unfolds the standard workflow used in onco-hematology to improve patient stratification and proposes alternative approaches to characterize the components and to assign patient to them, as they are crucial tasks usually supported by a priori clinical knowledge. We propose (a) to compute the parameters of the multinomial components of the HDMM or (b) to estimate the parameters of the HDMM components as if they were Multivariate Fisher's Non-Central Hypergeometric (MFNCH) distributions. Then, our approach to perform patients assignments to the HDMM components is designed to essentially determine for each patient its most likely component. We show on simulated data that the patients assignment using the MFNCH-based approach can be superior, if not comparable, to using the multinomial-based approach. Lastly, we illustrate on real Acute Myeloid Leukemia data how the utilization of MFNCH-based approach emerges as a good trade-off between the rigorous multinomial-based characterization of the HDMM components and the common refinement of them based on a priori clinical knowledge.

Data Science for Health Image Alignment: A User-Friendly Open-Source ImageJ/Fiji Plugin for Aligning Multimodality/Immunohistochemistry/Immunofluorescence 2D Microscopy Images.

Most of the time, the deep analysis of a biological sample requires the acquisition of images at different time points, using different modalities and/or different stainings. This information gives morphological, functional, and physiological insights, but the acquired images must be aligned to be able to proceed with the co-localisation analysis. Practically speaking, according to Aristotle's principle, "The whole is greater than the sum of its parts", multi-modal image registration is a challenging task that involves fusing complementary signals. In the past few years, several methods for image registration have been described in the literature, but unfortunately, there is not one method that works for all applications. In addition, there is currently no user-friendly solution for aligning images that does not require any computer skills. In this work, DS4H Image Alignment (DS4H-IA), an open-source ImageJ/Fiji plugin for aligning multimodality, immunohistochemistry (IHC), and/or immunofluorescence (IF) 2D microscopy images, designed with the goal of being extremely easy to use, is described. All of the available solutions for aligning 2D microscopy images have also been revised. The DS4H-IA source code; standalone applications for MAC, Linux, and Windows; video tutorials; manual documentation; and sample datasets are publicly available.

Automated Prediction of Photographic Wound Assessment Tool in Chronic Wound Images.

Many automated approaches have been proposed in literature to quantify clinically relevant wound features based on image processing analysis, aiming at removing human subjectivity and accelerate clinical practice. In this work we present a fully automated image processing pipeline leveraging deep learning and a large wound segmentation dataset to perform wound detection and following prediction of the Photographic Wound Assessment Tool (PWAT), automatizing the clinical judgement of the adequate wound healing. Starting from images acquired by smartphone cameras, a series of textural and morphological features are extracted from the wound areas, aiming to mimic the typical clinical considerations for wound assessment. The resulting extracted features can be easily interpreted by the clinician and allow a quantitative estimation of the PWAT scores. The features extracted from the region-of-interests detected by our pre-trained neural network model correctly predict the PWAT scale values with a Spearman's correlation coefficient of 0.85 on a set of unseen images. The obtained results agree with the current state-of-the-art and provide a benchmark for future artificial intelligence applications in this research field.

Circumventing the curse of dimensionality in magnetic resonance fingerprinting through a deep learning approach.

Magnetic resonance fingerprinting (MRF) is a rapidly developing approach for fast quantitative MRI. A typical drawback of dictionary-based MRF is an explosion of the dictionary size as a function of the number of reconstructed parameters, according to the "curse of dimensionality", which determines an explosion of resource requirements. Neural networks (NNs) have been proposed as a feasible alternative, but this approach is still in its infancy. In this work, we design a deep learning approach to MRF using a fully connected network (FCN). In the first part we investigate, by means of simulations, how the NN performance scales with the number of parameters to be retrieved in comparison with the standard dictionary approach. Four MRF sequences were considered: IR-FISP, bSSFP, IR-FISP-B1 , and IR-bSSFP-B1 , the latter two designed to be more specific for B1+ parameter encoding. Estimation accuracy, memory usage, and computational time required to perform the estimation task were considered to compare the scalability capabilities of the dictionary-based and the NN approaches. In the second part we study optimal training procedures by including different data augmentation and preprocessing strategies during training to achieve better accuracy and robustness to noise and undersampling artifacts. The study is conducted using the IR-FISP MRF sequence exploiting both simulations and in vivo acquisitions. Results demonstrate that the NN approach outperforms the dictionary-based approach in terms of scalability capabilities. Results also allow us to heuristically determine the optimal training strategy to make an FCN able to predict T1 , T2 , and M0 maps that are in good agreement with those obtained with the original dictionary approach. k-SVD denoising is proposed and found to be critical as a preprocessing step to handle undersampled data.

The function of specialized pro-resolving endogenous lipid mediators, vitamins, and other micronutrients in the control of the inflammatory processes: Possible role in patients with SARS-CoV-2 related infection.

Inflammation is an essential protective response against harmful stimuli, such as invading pathogens, damaged cells, or irritants. Physiological inflammation eliminates pathogens and promotes tissue repair and healing. Effective immune response in humans depends on a tightly regulated balance among inflammatory and anti-inflammatory mechanisms involving both innate and adaptive arms of the immune system. Excessive inflammation can become pathological and induce detrimental effects. If this process is not self-limited, an inappropriate remodeling of the tissues and organs can occur and lead to the onset of chronic degenerative diseases. A wide spectrum of infectious and non-infectious agents may activate the inflammation, via the release of mediators and cytokines by distinct subtypes of lymphocytes and macrophages. Several molecular mechanisms regulate the onset, progression, and resolution of inflammation. All these steps, even the termination of this process, are active and not passive events. In particular, a complex interplay exists between mediators (belonging to the group of Eicosanoids), which induce the beginning of inflammation, such as Prostaglandins (PGE2), Leukotrienes (LT), and thromboxane A2 (TXA2), and molecules which display a key role in counteracting this process and in promoting its proper resolution. The latter group of mediators includes: ω-6 arachidonic acid (AA)-derived metabolites, such as Lipoxins (LXs), ω -3 eicosapentaenoic acid (EPA)-derived mediators, such as E-series Resolvins (RvEs), and ω -3 docosahexaenoic (DHA)-derived mediators, such as D-series Resolvins (RvDs), Protectins (PDs) and Maresins (MaRs). Overall, these mediators are defined as specialized pro-resolving mediators (SPMs). Reduced synthesis of these molecules may lead to uncontrolled inflammation with possible harmful effects. ω-3 fatty acids are widely used in clinical practice as rather inexpensive, safe, readily available supplemental therapy. Taking advantage of this evidence, several researchers are suggesting that SPMs may have beneficial effects in the complementary treatment of patients with severe forms of SARS-CoV-2 related infection, to counteract the "cytokine storm" observed in these individuals. Well-designed and sized trials in patients suffering from COVID-19 with different degrees of severity are needed to investigate the real impact in the clinical practice of this promising therapeutic approach.

Effectiveness of Semi-Supervised Active Learning in Automated Wound Image Segmentation.

Appropriate wound management shortens the healing times and reduces the management costs, benefiting the patient in physical terms and potentially reducing the healthcare system's economic burden. Among the instrumental measurement methods, the image analysis of a wound area is becoming one of the cornerstones of chronic ulcer management. Our study aim is to develop a solid AI method based on a convolutional neural network to segment the wounds efficiently to make the work of the physician more efficient, and subsequently, to lay the foundations for the further development of more in-depth analyses of ulcer characteristics. In this work, we introduce a fully automated model for identifying and segmenting wound areas which can completely automatize the clinical wound severity assessment starting from images acquired from smartphones. This method is based on an active semi-supervised learning training of a convolutional neural network model. In our work, we tested the robustness of our method against a wide range of natural images acquired in different light conditions and image expositions. We collected the images using an ad hoc developed app and saved them in a database which we then used for AI training. We then tested different CNN architectures to develop a balanced model, which we finally validated with a public dataset. We used a dataset of images acquired during clinical practice and built an annotated wound image dataset consisting of 1564 ulcer images from 474 patients. Only a small part of this large amount of data was manually annotated by experts (ground truth). A multi-step, active, semi-supervised training procedure was applied to improve the segmentation performances of the model. The developed training strategy mimics a continuous learning approach and provides a viable alternative for further medical applications. We tested the efficiency of our model against other public datasets, proving its robustness. The efficiency of the transfer learning showed that after less than 50 epochs, the model achieved a stable DSC that was greater than 0.95. The proposed active semi-supervised learning strategy could allow us to obtain an efficient segmentation method, thereby facilitating the work of the clinician by reducing their working times to achieve the measurements. Finally, the robustness of our pipeline confirms its possible usage in clinical practice as a reliable decision support system for clinicians.

Effectiveness of Biologically Inspired Neural Network Models in Learning and Patterns Memorization.

Purpose: In this work, we propose an implementation of the Bienenstock-Cooper-Munro (BCM) model, obtained by a combination of the classical framework and modern deep learning methodologies. The BCM model remains one of the most promising approaches to modeling the synaptic plasticity of neurons, but its application has remained mainly confined to neuroscience simulations and few applications in data science. Methods: To improve the convergence efficiency of the BCM model, we combine the original plasticity rule with the optimization tools of modern deep learning. By numerical simulation on standard benchmark datasets, we prove the efficiency of the BCM model in learning, memorization capacity, and feature extraction. Results: In all the numerical simulations, the visualization of neuronal synaptic weights confirms the memorization of human-interpretable subsets of patterns. We numerically prove that the selectivity obtained by BCM neurons is indicative of an internal feature extraction procedure, useful for patterns clustering and classification. The introduction of competitiveness between neurons in the same BCM network allows the network to modulate the memorization capacity of the model and the consequent model selectivity. Conclusions: The proposed improvements make the BCM model a suitable alternative to standard machine learning techniques for both feature selection and classification tasks.

Impact of concurrency on the performance of a whole exome sequencing pipeline.

BACKGROUND: Current high-throughput technologies-i.e. whole genome sequencing, RNA-Seq, ChIP-Seq, etc.-generate huge amounts of data and their usage gets more widespread with each passing year. Complex analysis pipelines involving several computationally-intensive steps have to be applied on an increasing number of samples. Workflow management systems allow parallelization and a more efficient usage of computational power. Nevertheless, this mostly happens by assigning the available cores to a single or few samples' pipeline at a time. We refer to this approach as naive parallel strategy (NPS). Here, we discuss an alternative approach, which we refer to as concurrent execution strategy (CES), which equally distributes the available processors across every sample's pipeline. RESULTS: Theoretically, we show that the CES results, under loose conditions, in a substantial speedup, with an ideal gain range spanning from 1 to the number of samples. Also, we observe that the CES yields even faster executions since parallelly computable tasks scale sub-linearly. Practically, we tested both strategies on a whole exome sequencing pipeline applied to three publicly available matched tumour-normal sample pairs of gastrointestinal stromal tumour. The CES achieved speedups in latency up to 2-2.4 compared to the NPS. CONCLUSIONS: Our results hint that if resources distribution is further tailored to fit specific situations, an even greater gain in performance of multiple samples pipelines execution could be achieved. For this to be feasible, a benchmarking of the tools included in the pipeline would be necessary. It is our opinion these benchmarks should be consistently performed by the tools' developers. Finally, these results suggest that concurrent strategies might also lead to energy and cost savings by making feasible the usage of low power machine clusters.

COVID-19, what could sepsis, severe acute pancreatitis, gender differences, and aging teach us?

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes a potentially life-threatening disease, defined as Coronavirus Disease 19 (COVID-19). The most common signs and symptoms of this pathological condition include cough, fever, shortness of breath, and sudden onset of anosmia, ageusia, or dysgeusia. The course of COVID-19 is mild or moderate in more than 80% of cases, but it is severe or critical in about 14% and 5% of infected subjects respectively, with a significant risk of mortality. SARS-CoV-2 related infection is characterized by some pathogenetic events, resembling those detectable in other pathological conditions, such as sepsis and severe acute pancreatitis. All these syndromes are characterized by some similar features, including the coexistence of an exuberant inflammatory- as well as an anti-inflammatory-response with immune depression. Based on current knowledge concerning the onset and the development of acute pancreatitis and sepsis, we have considered these syndromes as a very interesting paradigm for improving our understanding of pathogenetic events detectable in patients with COVID-19. The aim of our review is: 1)to examine the pathogenetic mechanisms acting during the emergence of inflammatory and anti-inflammatory processes in human pathology; 2)to examine inflammatory and anti-inflammatory events in sepsis, acute pancreatitis, and SARS-CoV-2 infection and clinical manifestations detectable in patients suffering from these syndromes also according to the age and gender of these individuals; as well as to analyze the possible common and different features among these pathological conditions; 3)to obtain insights into our knowledge concerning COVID-19 pathogenesis. This approach may improve the management of patients suffering from this disease and it may suggest more effective diagnostic approaches and schedules of therapy, depending on the different phases and/or on the severity of SARS-CoV-2 infection.

The rationale for a multi-step therapeutic approach based on antivirals, drugs and nutrients with immunomodulatory activity in patients with coronavirus-SARS2-induced disease of different severities.

In December 2019, a novel human-infecting coronavirus, named Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2), was recognised to cause a pneumonia epidemic outbreak with different degrees of severity in Wuhan, Hubei Province in China. Since then, this epidemic has spread worldwide; in Europe, Italy has been involved. Effective preventive and therapeutic strategies are absolutely required to block this serious public health concern. Unfortunately, few studies about SARS-CoV-2 concerning its immunopathogenesis and treatment are available. On the basis of the assumption that the SARS-CoV-2 is genetically related to SARS-CoV (about 82 % of genome homology) and that its characteristics, like the modality of transmission or the type of the immune response it may stimulate, are still poorly known, a literature search was performed to identify the reports assessing these elements in patients with SARS-CoV-induced infection. Therefore, we have analysed: (1) the structure of SARS-CoV-2 and SARS-CoV; (2) the clinical signs and symptoms and pathogenic mechanisms observed during the development of acute respiratory syndrome and the cytokine release syndrome; (3) the modification of the cell microRNome and of the immune response in patients with SARS infection; and (4) the possible role of some fat-soluble compounds (such as vitamins A, D and E) in modulating directly or indirectly the replication ability of SARS-CoV-2 and host immune response.

Study of Wound Healing Dynamics by Single Pseudo-Particle Tracking in Phase Contrast Images Acquired in Time-Lapse.

Cellular contacts modify the way cells migrate in a cohesive group with respect to a free single cell. The resulting motion is persistent and correlated, with cells' velocities self-aligning in time. The presence of a dense agglomerate of cells makes the application of single particle tracking techniques to define cells dynamics difficult, especially in the case of phase contrast images. Here, we propose an original pipeline for the analysis of phase contrast images of the wound healing scratch assay acquired in time-lapse, with the aim of extracting single particle trajectories describing the dynamics of the wound closure. In such an approach, the membrane of the cells at the border of the wound is taken as a unicum, i.e., the wound edge, and the dynamics is described by the stochastic motion of an ensemble of points on such a membrane, i.e., pseudo-particles. For each single frame, the pipeline of analysis includes: first, a texture classification for separating the background from the cells and for identifying the wound edge; second, the computation of the coordinates of the ensemble of pseudo-particles, chosen to be uniformly distributed along the length of the wound edge. We show the results of this method applied to a glioma cell line (T98G) performing a wound healing scratch assay without external stimuli. We discuss the efficiency of the method to assess cell motility and possible applications to other experimental layouts, such as single cell motion. The pipeline is developed in the Python language and is available upon request.

Mediterranean diet intervention alters the gut microbiome in older people reducing frailty and improving health status: the NU-AGE 1-year dietary intervention across five European countries.

OBJECTIVE: Ageing is accompanied by deterioration of multiple bodily functions and inflammation, which collectively contribute to frailty. We and others have shown that frailty co-varies with alterations in the gut microbiota in a manner accelerated by consumption of a restricted diversity diet. The Mediterranean diet (MedDiet) is associated with health. In the NU-AGE project, we investigated if a 1-year MedDiet intervention could alter the gut microbiota and reduce frailty. DESIGN: We profiled the gut microbiota in 612 non-frail or pre-frail subjects across five European countries (UK, France, Netherlands, Italy and Poland) before and after the administration of a 12-month long MedDiet intervention tailored to elderly subjects (NU-AGE diet). RESULTS: Adherence to the diet was associated with specific microbiome alterations. Taxa enriched by adherence to the diet were positively associated with several markers of lower frailty and improved cognitive function, and negatively associated with inflammatory markers including C-reactive protein and interleukin-17. Analysis of the inferred microbial metabolite profiles indicated that the diet-modulated microbiome change was associated with an increase in short/branch chained fatty acid production and lower production of secondary bile acids, p-cresols, ethanol and carbon dioxide. Microbiome ecosystem network analysis showed that the bacterial taxa that responded positively to the MedDiet intervention occupy keystone interaction positions, whereas frailty-associated taxa are peripheral in the networks. CONCLUSION: Collectively, our findings support the feasibility of improving the habitual diet to modulate the gut microbiota which in turn has the potential to promote healthier ageing.

Recovery from 6-month spaceflight at the International Space Station: muscle-related stress into a proinflammatory setting.

The Sarcolab pilot study of 2 crewmembers, investigated before and after a 6-mo International Space Station mission, has demonstrated the substantial muscle wasting and weakness, along with disruption of muscle's oxidative metabolism. The present work aimed at evaluating the pro/anti-inflammatory status in the same 2 crewmembers (A, B). Blood circulating (c-)microRNAs (miRs), c-proteasome, c-mitochondrial DNA, and cytokines were assessed by real-time quantitative PCR or ELISA tests. Time series analysis was performed ( i.e., before flight and after landing) at 1 and 15 d of recovery (R+1 and R+15, respectively). C-biomarkers were compared with an age-matched control population and with 2-dimensional proteomic analysis of the 2 crewmembers' muscle biopsies. Striking differences were observed between the 2 crewmembers at R+1, in terms of inflamma-miRs (c-miRs-21-5p, -126-3p, and -146a-5p), muscle specific (myo)-miR-206, c-proteasome, and IL-6/leptin, thus making the 2 astronauts dissimilar to each other. Final recovery levels of c-proteasome, c-inflamma-miRs, and c-myo-miR-206 were not reverted to the baseline values in crewmember A. In both crewmembers, myo-miR-206 changed significantly after recovery. Muscle biopsy of astronaut A showed an impressive 80% increase of α-1-antitrypsin, a target of miR-126-3p. These results point to a strong stress response induced by spaceflight involving muscle tissue and the proinflammatory setting, where inflamma-miRs and myo-miR-206 mediate the systemic recovery phase after landing.-Capri, M., Morsiani, C., Santoro, A., Moriggi, M., Conte, M., Martucci, M., Bellavista, E., Fabbri, C., Giampieri, E., Albracht, K., Flück, M., Ruoss, S., Brocca, L., Canepari, M., Longa, E., Di Giulio, I., Bottinelli, R., Cerretelli, P., Salvioli, S., Gelfi, C., Franceschi, C., Narici, M., Rittweger, J. Recovery from 6-month spaceflight at the International Space Station: muscle-related stress into a proinflammatory setting.

Cytokine storm in aged people with CoV-2: possible role of vitamins as therapy or preventive strategy.

BACKGROUND: In December 2019, a novel human-infecting coronavirus, SARS-CoV-2, had emerged. The WHO has classified the epidemic as a "public health emergency of international concern". A dramatic situation has unfolded with thousands of deaths, occurring mainly in the aged and very ill people. Epidemiological studies suggest that immune system function is impaired in elderly individuals and these subjects often present a deficiency in fat-soluble and hydrosoluble vitamins. METHODS: We searched for reviews describing the characteristics of autoimmune diseases and the available therapeutic protocols for their treatment. We set them as a paradigm with the purpose to uncover common pathogenetic mechanisms between these pathological conditions and SARS-CoV-2 infection. Furthermore, we searched for studies describing the possible efficacy of vitamins A, D, E, and C in improving the immune system function. RESULTS: SARS-CoV-2 infection induces strong immune system dysfunction characterized by the development of an intense proinflammatory response in the host, and the development of a life-threatening condition defined as cytokine release syndrome (CRS). This leads to acute respiratory syndrome (ARDS), mainly in aged people. High mortality and lethality rates have been observed in elderly subjects with CoV-2-related infection. CONCLUSIONS: Vitamins may shift the proinflammatory Th17-mediated immune response arising in autoimmune diseases towards a T-cell regulatory phenotype. This review discusses the possible activity of vitamins A, D, E, and C in restoring normal antiviral immune system function and the potential therapeutic role of these micronutrients as part of a therapeutic strategy against SARS-CoV-2 infection.

Gender-specific association of body composition with inflammatory and adipose-related markers in healthy elderly Europeans from the NU-AGE study.

OBJECTIVES: The aim of this work was to examine the cross-sectional relationship between body composition (BC) markers for adipose and lean tissue and bone mass, and a wide range of specific inflammatory and adipose-related markers in healthy elderly Europeans. METHODS: A whole-body dual-energy X-ray absorptiometry (DXA) scan was made in 1121 healthy (65-79 years) women and men from five European countries of the "New dietary strategies addressing the specific needs of elderly population for a healthy aging in Europe" project (NCT01754012) cohort to measure markers of adipose and lean tissue and bone mass. Pro-inflammatory (IL-6, IL-6Rα, TNF-α, TNF-R1, TNF-R2, pentraxin 3, CRP, alpha-1-acid glycoprotein, albumin) and anti-inflammatory (IL-10, TGF-ß1) molecules as well as adipose-related markers such as leptin, adiponectin, ghrelin, and resistin were measured by magnetic bead-based multiplex-specific immunoassays and biochemical assays. RESULTS: BC characteristics were different in elderly women and men, and more favorable BC markers were associated with a better adipose-related inflammatory profile, with the exception of skeletal muscle mass index. No correlation was found with the body composition markers and circulating levels of some standard pro- and anti-inflammatory markers like IL-6, pentraxin 3, IL-10, TGF-ß1, TNF-α, IL-6Rα, glycoprotein 130, TNF-α-R1, and TNF-α-R2. CONCLUSIONS: The association between BC and inflammatory and adipose-related biomarkers is crucial in decoding aging and pathophysiological processes, such as sarcopenia. DXA can help in understanding how the measurement of fat and muscle is important, making the way from research to clinical practice. KEY POINTS: • Body composition markers concordantly associated positively or negatively with adipose-related and inflammatory markers, with the exception of skeletal muscle mass index. • No correlation was found with the body composition markers and circulating levels of some standard pro- and anti-inflammatory markers like IL-6, pentraxin 3, IL-10, TGF-ß1, TNF-α, IL-6Rα, gp130, TNF-α-R1, and TNF-α-R2. • Skeletal muscle mass index (SMI) shows a good correlation with inflammatory profile in age-related sarcopenia.

Systems medicine of inflammaging.

Systems Medicine (SM) can be defined as an extension of Systems Biology (SB) to Clinical-Epidemiological disciplines through a shifting paradigm, starting from a cellular, toward a patient centered framework. According to this vision, the three pillars of SM are Biomedical hypotheses, experimental data, mainly achieved by Omics technologies and tailored computational, statistical and modeling tools. The three SM pillars are highly interconnected, and their balancing is crucial. Despite the great technological progresses producing huge amount of data (Big Data) and impressive computational facilities, the Bio-Medical hypotheses are still of primary importance. A paradigmatic example of unifying Bio-Medical theory is the concept of Inflammaging. This complex phenotype is involved in a large number of pathologies and patho-physiological processes such as aging, age-related diseases and cancer, all sharing a common inflammatory pathogenesis. This Biomedical hypothesis can be mapped into an ecological perspective capable to describe by quantitative and predictive models some experimentally observed features, such as microenvironment, niche partitioning and phenotype propagation. In this article we show how this idea can be supported by computational methods useful to successfully integrate, analyze and model large data sets, combining cross-sectional and longitudinal information on clinical, environmental and omics data of healthy subjects and patients to provide new multidimensional biomarkers capable of distinguishing between different pathological conditions, e.g. healthy versus unhealthy state, physiological versus pathological aging.

Methods for the integration of multi-omics data: mathematical aspects.

BACKGROUND: Methods for the integrative analysis of multi-omics data are required to draw a more complete and accurate picture of the dynamics of molecular systems. The complexity of biological systems, the technological limits, the large number of biological variables and the relatively low number of biological samples make the analysis of multi-omics datasets a non-trivial problem. RESULTS AND CONCLUSIONS: We review the most advanced strategies for integrating multi-omics datasets, focusing on mathematical and methodological aspects.

Stochastic neutral modelling of the Gut Microbiota's relative species abundance from next generation sequencing data.

BACKGROUND: Interest in understanding the mechanisms that lead to a particular composition of the Gut Microbiota is highly increasing, due to the relationship between this ecosystem and the host health state. Particularly relevant is the study of the Relative Species Abundance (RSA) distribution, that is a component of biodiversity and measures the number of species having a given number of individuals. It is the universal behaviour of RSA that induced many ecologists to look for theoretical explanations. In particular, a simple stochastic neutral model was proposed by Volkov et al. relying on population dynamics and was proved to fit the coral-reefs and rain forests RSA. Our aim is to ascertain if this model also describes the Microbiota RSA and if it can help in explaining the Microbiota plasticity. RESULTS: We analyzed 16S rRNA sequencing data sampled from the Microbiota of three different animal species by Jeraldo et al. Through a clustering procedure (UCLUST), we built the Operational Taxonomic Units. These correspond to bacterial species considered at a given phylogenetic level defined by the similarity threshold used in the clustering procedure. The RSAs, plotted in the form of Preston plot, were fitted with Volkov's model. The model fits well the Microbiota RSA, except in the tail region, that shows a deviation from the neutrality assumption. Looking at the model parameters we were able to discriminate between different animal species, giving also a biological explanation. Moreover, the biodiversity estimator obtained by Volkov's model also differentiates the animal species and is in good agreement with the first and second order Hill's numbers, that are common evenness indexes simply based on the fraction of individuals per species. CONCLUSIONS: We conclude that the neutrality assumption is a good approximation for the Microbiota dynamics and the observation that Volkov's model works for this ecosystem is a further proof of the RSA universality. Moreover, the ability to separate different animals with the model parameters and biodiversity number are promising results if we think about future applications on human data, in which the Microbiota composition and biodiversity are in close relationships with a variety of diseases and life-styles.

Optimized pipeline of MuTect and GATK tools to improve the detection of somatic single nucleotide polymorphisms in whole-exome sequencing data.

BACKGROUND: Detecting somatic mutations in whole exome sequencing data of cancer samples has become a popular approach for profiling cancer development, progression and chemotherapy resistance. Several studies have proposed software packages, filters and parametrizations. However, many research groups reported low concordance among different methods. We aimed to develop a pipeline which detects a wide range of single nucleotide mutations with high validation rates. We combined two standard tools - Genome Analysis Toolkit (GATK) and MuTect - to create the GATK-LODN method. As proof of principle, we applied our pipeline to exome sequencing data of hematological (Acute Myeloid and Acute Lymphoblastic Leukemias) and solid (Gastrointestinal Stromal Tumor and Lung Adenocarcinoma) tumors. We performed experiments on simulated data to test the sensitivity and specificity of our pipeline. RESULTS: The software MuTect presented the highest validation rate (90 %) for mutation detection, but limited number of somatic mutations detected. The GATK detected a high number of mutations but with low specificity. The GATK-LODN increased the performance of the GATK variant detection (from 5 of 14 to 3 of 4 confirmed variants), while preserving mutations not detected by MuTect. However, GATK-LODN filtered more variants in the hematological samples than in the solid tumors. Experiments in simulated data demonstrated that GATK-LODN increased both specificity and sensitivity of GATK results. CONCLUSION: We presented a pipeline that detects a wide range of somatic single nucleotide variants, with good validation rates, from exome sequencing data of cancer samples. We also showed the advantage of combining standard algorithms to create the GATK-LODN method, that increased specificity and sensitivity of GATK results. This pipeline can be helpful in discovery studies aimed to profile the somatic mutational landscape of cancer genomes.