Alfonso Monaco

BioMaS: a modular pipeline for Bioinformatic analysis of Metagenomic AmpliconS.

BackgroundSubstantial advances in microbiology, molecular evolution and biodiversity have been carried out in recent years thanks to Metagenomics, which allows to unveil the composition and functions of mixed microbial communities in any environmental niche. If the investigation is aimed only at the microbiome taxonomic structure, a target-based metagenomic approach, here also referred as Meta-barcoding, is generally applied. This approach commonly involves the selective amplification of a species-specific genetic marker (DNA meta-barcode) in the whole taxonomic range of interest and the exploration of its taxon-related variants through High-Throughput Sequencing (HTS) technologies. The accessibility to proper computational systems for the large-scale bioinformatic analysis of HTS data represents, currently, one of the major challenges in advanced Meta-barcoding projects.ResultsBioMaS (Bioinformatic analysis of Metagenomic AmpliconS) is a new bioinformatic pipeline designed to support biomolecular researchers involved in taxonomic studies of environmental microbial communities by a completely automated workflow, comprehensive of all the fundamental steps, from raw sequence data upload and cleaning to final taxonomic identification, that are absolutely required in an appropriately designed Meta-barcoding HTS-based experiment. In its current version, BioMaS allows the analysis of both bacterial and fungal environments starting directly from the raw sequencing data from either Roche 454 or Illumina HTS platforms, following two alternative paths, respectively. BioMaS is implemented into a public web service available at https://recasgateway.ba.infn.it/ and is also available in Galaxy at http://galaxy.cloud.ba.infn.it:8080 (only for Illumina data).ConclusionBioMaS is a friendly pipeline for Meta-barcoding HTS data analysis specifically designed for users without particular computing skills. A comparative benchmark, carried out by using a simulated dataset suitably designed to broadly represent the currently known bacterial and fungal world, showed that BioMaS outperforms QIIME and MOTHUR in terms of extent and accuracy of deep taxonomic sequence assignments.

DTI measurements for Alzheimer’s classification

Diffusion tensor imaging (DTI) is a promising imaging technique that provides insight into white matter microstructure integrity and it has greatly helped identifying white matter regions affected by Alzheimers disease (AD) in its early stages. DTI can therefore be a valuable source of information when designing machine-learning strategies to discriminate between healthy control (HC) subjects, AD patients and subjects with mild cognitive impairment (MCI). Nonetheless, several studies have reported so far conflicting results, especially because of the adoption of biased feature selection strategies. In this paper we firstly analyzed DTI scans of 150 subjects from the Alzheimers disease neuroimaging initiative (ADNI) database. We measured a significant effect of the feature selection bias on the classification performance (p-value  <  0.01), leading to overoptimistic results (10% up to 30% relative increase in AUC). We observed that this effect is manifest regardless of the choice of diffusion index, specifically fractional anisotropy and mean diffusivity. Secondly, we performed a test on an independent mixed cohort consisting of 119 ADNI scans; thus, we evaluated the informative content provided by DTI measurements for AD classification. Classification performances and biological insight, concerning brain regions related to the disease, provided by cross-validation analysis were both confirmed on the independent test.

A Multiplex Network Model to Characterize Brain Atrophy in Structural MRI

We developed a multiplex network approach for the description and recognition of structural brain changes in the context of the early diagnosis of Alzheimer disease (AD). Our techniques can supply a convenient mathematical framework to model structural inter- and intra-subject brain similarities in magnetic resonance images (MRI) within Alzheimer disease studies. We used a set of 100 structural T1 brain scans, from subjects of the Alzheimer’s Disease Neuroimaging Initiative, including AD patients, normal controls (NC) and mild cognitive impairment (MCI) subjects. We evaluated the classification performances including the comparison of two state-of-the-art techniques, Random Forests (RF) and Support Vector Machines (SVM) . Our results show that multiplex networks can significantly improve the classification performance obtained only with the use of structural features. They can also effectively distinguish NC, MCI and AD patterns with an area under the receiver-operating-characteristic curve (AUC) \(\ge 0.89 \pm 0.04\).

MSA-PAD: DNA multiple sequence alignment framework based on PFAM accessed domain information

Here we present the MSA-PAD application, a DNA multiple sequence alignment framework that uses PFAM protein domain information to align DNA sequences encoding either single or multiple protein domains. MSA-PAD has two alignment options: gene and genome mode.

A complex network approach reveals a pivotal substructure of genes linked to schizophrenia

Research on brain disorders with a strong genetic component and complex heritability, such as schizophrenia, has led to the development of brain transcriptomics. This field seeks to gain a deeper understanding of gene expression, a key factor in exploring further research issues. Our study focused on how genes are associated amongst each other. In this perspective, we have developed a novel data-driven strategy for characterizing genetic modules, i.e., clusters of strongly interacting genes. The aim was to uncover a pivotal community of genes linked to a target gene for schizophrenia. Our approach combined network topological properties with information theory to highlight the presence of a pivotal community, for a specific gene, and to simultaneously assess the information content of partitions with the Shannon’s entropy based on betweenness. We analyzed the publicly available BrainCloud dataset containing post-mortem gene expression data and focused on the Dopamine D2 receptor, encoded by the DRD2 gene. We used four different community detection algorithms to evaluate the consistence of our approach. A pivotal DRD2 community emerged for all the procedures applied, with a considerable reduction in size, compared to the initial network. The stability of the results was confirmed by a Dice index ≥80% within a range of tested parameters. The detected community was also the most informative, as it represented an optimization of the Shannon entropy. Lastly, we verified the strength of connection of the DRD2 community, which was stronger than any other randomly selected community and even more so than the Weighted Gene Co-expression Network Analysis module, commonly considered the standard approach for such studies. This finding substantiates the conclusion that the detected community represents a more connected and informative cluster of genes for the DRD2 community, and therefore better elucidates the behavior of this module of strongly related DRD2 genes. Because this gene plays a relevant role in Schizophrenia, this finding of a more specific DRD2 community will improve the understanding of the genetic factors related with this disorder.

Topological Measurements of DWI Tractography for Alzheimer’s Disease Detection

Neurodegenerative diseases affect brain morphology and connectivity, making complex networks a suitable tool to investigate and model their effects. Because of its stereotyped pattern Alzheimers disease (AD) is a natural benchmark for the study of novel methodologies. Several studies have investigated the network centrality and segregation changes induced by AD, especially with a single subject approach. In this work, a holistic perspective based on the application of multiplex network concepts is introduced. We define and assess a diagnostic score to characterize the brain topology and measure the disease effects on a mixed cohort of 52 normal controls (NC) and 47 AD patients, from Alzheimers Disease Neuroimaging Initiative (ADNI). The proposed topological score allows an accurate NC-AD classification: the average area under the curve (AUC) is 95% and the 95% confidence interval is 92%–99%. Besides, the combination of topological information and structural measures, such as the hippocampal volumes, was also investigated. Topology is able to capture the disease signature of AD and, as the methodology is general, it can find interesting applications to enhance our insight into disease with more heterogeneous patterns.

Topological Complex Networks Properties for Gene Community Detection Strategy: DRD2 Case Study

Gene interactions can suitably be modeled as communities through weighted complex networks. However, the problem to efficiently detect these communities , eventually gaining biological insight from them, is still an open question. This paper presents a novel data-driven strategy for community detection and tests it on the specific case study of DRD2 gene coding for the D2 dopamine receptor, which plays a prominent role in risk for Schizophrenia . We adopt a combined use of centrality and topological properties to detect an optimal network partition. We find that 21 genes belongs with our target community with probability \(P \ge 90\,\%\). The robustness of the partition is assessed with two independent methodologies: (i) fuzzy c-means and (ii) consensus analyses . We use the first one to measure how strong the membership of these genes to the DRD2 community is and the latter to confirm the stability of the detected partition. These results show an interesting reduction (\({\sim }80\,\%\)) of the target community size. Moreover, to allow this validation on different case studies, the proposed methodology is available on an open cloud infrastructure, according to the Software as a Service paradigm (SaaS).

Trial latencies estimation of event-related potentials in EEG by means of genetic algorithms

OBJECTIVE Event-related potentials (ERPs) are usually obtained by averaging thus neglecting the trial-to-trial latency variability in cognitive electroencephalography (EEG) responses. As a consequence the shape and the peak amplitude of the averaged ERP are smeared and reduced, respectively, when the single-trial latencies show a relevant variability. To date, the majority of the methodologies for single-trial latencies inference are iterative schemes providing suboptimal solutions, the most commonly used being the Woodys algorithm. APPROACH In this study, a global approach is developed by introducing a fitness function whose global maximum corresponds to the set of latencies which renders the trial signals most aligned as possible. A suitable genetic algorithm has been implemented to solve the optimization problem, characterized by new genetic operators tailored to the present problem. MAIN RESULTS The results, on simulated trials, showed that the proposed algorithm performs better than Woodys algorithm in all conditions, at the cost of an increased computational complexity (justified by the improved quality of the solution). Application of the proposed approach on real data trials, resulted in an increased correlation between latencies and reaction times w.r.t. the output from RIDE method. SIGNIFICANCE The above mentioned results on simulated and real data indicate that the proposed method, providing a better estimate of single-trial latencies, will open the way to more accurate study of neural responses as well as to the issue of relating the variability of latencies to the proper cognitive and behavioural correlates.

A Combined Approach of Multiscale Texture Analysis and Interest Point/Corner Detectors for Microcalcifications Diagnosis

Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic model for characterizing and discriminating tissue in normal/abnormal and benign/malign in digital mammograms, as support tool for the radiologists. We trained a Random Forest classifier on some textural features extracted on a multiscale image decomposition based on the Haar wavelet transform combined with the interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg), respectively. We tested the proposed model on 192 ROIs extracted from 176 digital mammograms of a public database. The model proposed was high performing in the prediction of the normal/abnormal and benign/malignant ROIs, with a median AUC value of \(98.46\%\) and \(94.19\%\), respectively. The experimental result was comparable with related work performance.

A novel approach to brain connectivity reveals early structural changes in Alzheimer’s disease

OBJECTIVE Recent studies have shown that complex networks along with diffusion weighted imaging (DWI) can be efficient and promising techniques for early detection of structural pathological changes in Alzheimers disease. Besides, connectivity studies, specifically assessing the organization of a graph and its topology, could represent the best chance to discover how brain activity is shaped and driven. Accordingly, we propose a methodology to evaluate how Alzheimers disease affects brain networks through a novel way to look at graph connectivity. In fact, we use the combination of network features related to brain organization with network features related to the variations in connectivity between several subjects. APPROACH From a DWI brain scan we reconstruct a probabilistic tractography by evaluating the number of white matter fibers connecting two anatomical districts, thus obtaining a weighted undirected network. The nodes of this network are the cerebral regions provided by the reference brain atlas, the weights are the intensity of linkage among them. We investigated brain connectivity graphs retrieved from a set of 222 publicly available DWI scans from the Alzheimers Disease Neuroimaging Initiative (ADNI): 47 Alzheimers disease (AD) patients, 52 normal controls (NC) and 123 mild cognitive impairment (MCI) subjects, this latter cohort includes 85 early and 38 late MCI subjects, EMCI and LMCI respectively. MAIN RESULTS The proposed brain connectivity approach effectively characterizes Alzheimers disease, especially in its early stages. In fact, MCI is a prodromal phase of Alzheimers disease. We report a [Formula: see text] accuracy for the discrimination of NC and AD subjects and accuracies of [Formula: see text] and [Formula: see text] for the discrimination of MCI from respectively NC and AD subjects. SIGNIFICANCE Our complex network approach offers an innovative and effective instrument to model brain connectivity and detect in DWI tractographies early changes due to Alzheimers.