Cancer is a multi-stage process often driven by progressive accumulation of genomic rearrangements that can result in cells acquiring cancer properties such as tumor invasive and metastatic behavior. Many genes associated with cancer are the result of complex somatically and inherited chromosomal rearrangements, resulting in aberrant transcripts or defects in transcription [1-5]. The classical approach for the identification of genome rearrangements such as G-banded cytogenetics, spectral karyotyping and FISH, are poor in sensitivity, while copy number array can identify just imbalanced breakpoints and do not describe the resulted genome structure produced by the events, which may cause the breakpoints. The aim of this project is to obtain, by the paired-end mapping (PEM) approach applied to the massive parallel sequencing, an high resolution virtual karyotype of the genome of a breast-cancer-patient of which we obtained previously the transcriptomic portrait [6].The introduction of massively parallel high throughput sequencing (HTS) techniques have created a broad range of new and exciting research applications by increasing the output sequencing data dramatically. In recent years, the continuous technical improvements of next-generation sequencing technology have made RNA sequencing (RNA-seq) particularly effective for the detection of gene fusions, which are involved in several diseases. Gene fusions are found in many cancer types, and they have proved to be prognostic biomarkers in several studies [7-9]. In addition, gene fusions have often a direct functional impact on the molecular processes in the cell [10].Several analysis steps are needed to process the data provided by the sequencer and to use them for robust gene fusion detection.We propose a workflow to analyze NGS paired-end sequences in order to identify possible candidates to be the results of a fusion between different genes, looking for fusion events occurring on the same chromosome (intra-chromosomal rearrangement).The basic idea is to map the reads onto the reference genome and to study the insert size length distribution of the paired-end, looking at its peak and select all the mapping pairs having an insert size value quite far from the observed peak. In this way we are sure to select paired-end sequences mapping on different regions of the genome far from each other connecting different genes.

When the reads obtained from high-throughput RNA sequencing are mapped against a reference database, a significant proportion of them - known as multireads - can map to more than one reference sequence. These multireads originate from gene duplications, repetitive regions or overlapping genes. Removing the multireads from the mapping results, in RNA-Seq analyses, causes an underestimation of the read counts, while estimating the real read count can lead to false positives during the detection of differentially expressed sequences.ResultsWe present an innovative approach to deal with multireads and evaluate differential expression events, entirely based on fuzzy set theory. Since multireads cause uncertainty in the estimation of read counts during gene expression computation, they can also influence the reliability of differential expression analysis results, by producing false positives. Our method manages the uncertainty in gene expression estimation by defining the fuzzy read counts and evaluates the possibility of a gene to be differentially expressed with three fuzzy concepts: over-expression, same-expression and under-expression. The output of the method is a list of differentially expressed genes enriched with information about the uncertainty of the results due to the multiread presence.We have tested the method on RNA-Seq data designed for case-control studies and we have compared the obtained results with other existing tools for read count estimation and differential expression analysis.ConclusionsThe management of multireads with the use of fuzzy sets allows to obtain a list of differential expression events which takes in account the uncertainty in the results caused by the presence of multireads. Such additional information can be used by the biologists when they have to select the most relevant differential expression events to validate with laboratory assays. Our method can be used to compute reliable differential expression events and to highlight possible false positives in the lists of differentially expressed genes computed with other tools.

Introduction : The Pediatric onset of Multiple Sclerosis (PedMS) occurs in up to 10% of all cases. Cognitive impairment is one of the frequent symptoms, exerting severe impact in patients' quality of life and school performances. The underlying pathogenic mechanisms are not fully understood, and molecular markers predictive of cognitive dysfunctions need to be identified. On these grounds, we searched for molecular signature/s (i.e., miRNAs and target genes) associated with cognitive impairment in a selected population of PedMS patients. Additionally, changes of their regional brain volumes associated with the miRNAs of interest were investigated. Methods: Nineteen PedMS subjects received a full cognitive evaluation; total RNA from peripheral blood samples was processed by next-generation sequencing followed by a bioinformatics/biostatistics analysis. Results: The expression of 11 miRNAs significantly correlated with the scores obtained at different cognitive tests; among the others, eight miRNAs correlated with the Trail Making Tests. The computational target prediction identified 337 genes targeted by the miRNAs of interest; a tangled network of molecular connections was hypothesized, where genes like BST1, NTNG2, SPTB, and STAB1, already associated with cognitive dysfunctions, were nodes of the net. Furthermore, the expression of some miRNAs significantly correlated with cerebral volumes, for example, four miRNAs with the cerebellum cortex. Conclusions: As far as we know, this is the first evaluation exploring miRNAs in the cognitive performances of PedMS. Although none of these results survived the multiple tests' corrections, we believe that they may represent a step forward the identification of biomarkers useful for monitoring and targeting the onset/progression of cognitive impairments in MS.

It is known from recent studies that more than 90% of human multi-exon genes are subject toAlternative Splicing (AS), a key molecular mechanism in which multiple transcripts may be generated from a singlegene. It is widely recognized that a breakdown in AS mechanisms plays an important role in cellular differentiationand pathologies. Polymerase Chain Reactions, microarrays and sequencing technologies have been applied to thestudy of transcript diversity arising from alternative expression. Last generation Affymetrix GeneChip Human Exon1.0 ST Arrays offer a more detailed view of the gene expression profile providing information on the AS patterns.The exon array technology, with more than five million data points, can detect approximately one million exons,and it allows performing analyses at both gene and exon level. In this paper we describe BEAT, an integrated userfriendlybioinformatics framework to store, analyze and visualize exon arrays datasets. It combines a datawarehouse approach with some rigorous statistical methods for assessing the AS of genes involved in diseases.Meta statistics are proposed as a novel approach to explore the analysis results. BEAT is available at http://beat.ba.itb.cnr.it.Results: BEAT is a web tool which allows uploading and analyzing exon array datasets using standard statisticalmethods and an easy-to-use graphical web front-end. BEAT has been tested on a dataset with 173 samples andtuned using new datasets of exon array experiments from 28 colorectal cancer and 26 renal cell cancer samplesproduced at the Medical Genetics Unit of IRCCS Casa Sollievo della Sofferenza.To highlight all possible AS events, alternative names, accession Ids, Gene Ontology terms and biochemicalpathways annotations are integrated with exon and gene level expression plots. The user can customize the resultschoosing custom thresholds for the statistical parameters and exploiting the available clinical data of the samplesfor a multivariate AS analysis.Conclusions: Despite exon array chips being widely used for transcriptomics studies, there is a lack of analysistools offering advanced statistical features and requiring no programming knowledge. BEAT provides a user-friendlyplatform for a comprehensive study of AS events in human diseases, displaying the analysis results with easilyinterpretable and interactive tables and graphics.

In previous reports, we had shown in Camelus dromedarius that diversity in T cell receptor gamma (TRG) and delta (TRD) variable domains can be generated by somatic hypermutation (SHM). In the present paper, we further the previous finding by analyzing 85 unique spleen cDNA sequences encoding a total of 331 mutations from a single animal, and comparing the properties of the mutation profiles of dromedary TRG and TRD variable domains. The transition preference and the significant mutation frequency in the AID motifs (dgyw/wrch and wa/tw) demonstrate a strong dependence of the enzymes mediating SHM in TRG and TRD genes of dromedary similar to that of immunoglobulin genes in mammals. Overall, results reveal no asymmetry in the motifs targeting, i.e. mutations are equally distributed among g:c and a:t base pairs and replacement mutations are favored at the AID motifs, whereas neutral mutations appear to be more prone to accumulate in bases outside of the motifs. A detailed analysis of clonal lineages in TRG and TRD cDNA sequences also suggests that clonal expansion of mutated productive rearrangements may be crucial in shaping the somatic diversification in the dromedary. This is confirmed by the fact that our structural models, computed by adopting a comparative procedure, are consistent with the possibility that, irrespective of where (in the CDR-IMGT or in FR-IMGT) the diversity was generated by mutations, both clonal expansion and selection seem to be strictly related to an enhanced structural stability of the 78 subunits. (C) 2014 Elsevier Ltd. All rights reserved.

Multiple sclerosis (MS) is a complex disease of the CNS that usually affects young adults, although 3-5% of cases are diagnosed in childhood and adolescence (hence called pediatric MS, PedMS). Genetic predisposition, among other factors, seems to contribute to the risk of the onset, in pediatric as in adult ages, but few studies have investigated the genetic 'environmentally naïve' load of PedMS. The main goal of this study was to identify circulating markers (miRNAs), target genes (mRNAs) and functional pathways associated with PedMS; we also verified the impact of miRNAs on clinical features, i.e. disability and cognitive performances. The investigation was performed in 19 PedMS and 20 pediatric controls (PCs) using a High-Throughput Next-generation Sequencing (HT-NGS) approach followed by an integrated bioinformatics/biostatistics analysis. Twelve miRNAs were significantly upregulated (let-7a-5p, let-7b-5p, miR-25-3p, miR-125a-5p, miR-942-5p, miR-221-3p, miR-652-3p, miR-182-5p, miR-185-5p, miR-181a-5p, miR-320a, miR-99b-5p) and 1 miRNA was downregulated (miR-148b-3p) in PedMS compared with PCs. The interactions between the significant miRNAs and their targets uncovered predicted genes (i.e. TNFSF13B, TLR2, BACH2, KLF4) related to immunological functions, as well as genes involved in autophagy-related processes (i.e. ATG16L1, SORT1, LAMP2) and ATPase activity (i.e. ABCA1, GPX3). No significant molecular profiles were associated with any PedMS demographic/clinical features. Both miRNAs and mRNA expressions predicted the phenotypes (PedMS-PC) with an accuracy of 92% and 91%, respectively. In our view, this original strategy of contemporary miRNA/mRNA analysis may help to shed light in the genetic background of the disease, suggesting further molecular investigations in novel pathogenic mechanisms.

Amyotrophic lateral sclerosis (ALS) is a progressive and fatal neurodegenerative disease. While genetics and other factors contribute to ALS pathogenesis, critical knowledge is still missing and validated biomarkers for monitoring the disease activity have not yet been identified. To address those aspects we carried out this study with the primary aim of identifying possible miRNAs/mRNAs dysregulation associated with the sporadic form of the disease (sALS). Additionally, we explored miRNAs as modulating factors of the observed clinical features. Study included 56 sALS and 20 healthy controls (HCs). We analyzed the peripheral blood samples of sALS patients and HCs with a high-throughput next-generation sequencing followed by an integrated bioinformatics/biostatistics analysis. Results showed that 38 miRNAs (let-7a-5p, let-7d-5p, let-7f-5p, let-7g-5p, let-7i-5p, miR-103a-3p, miR-106b-3p, miR-128-3p, miR-130a-3p, miR-130b-3p, miR-144-5p, miR-148a3p, miR-148b-3p, miR-15a-5p, miR-15b-5p, miR-151a-5p, miR-151b, miR-16-5p, miR-182-5p, miR-183-5p, miR-186-5p, miR-22-3p, miR-221-3p, miR-223-3p, miR23a- 3p, miR-26a-5p, miR-26b-5p, miR-27b-3p, miR-28-3p, miR-30b-5p, miR-30c-5p, miR-342-3p, miR-425-5p, miR-451a, miR-532-5p, miR-550a-3p, miR-584-5p, miR93- 5p) were significantly downregulated in sALS. We also found that different miRNAs profiles characterized the bulbar/spinal onset and the progression rate. This observation supports the hypothesis that miRNAs may impact the phenotypic expression of the disease. Genes known to be associated with ALS (e.g., PARK7, C9orf72, ALS2, MATR3, SPG11, ATXN2) were confirmed to be dysregulated in our study. We also identified other potential candidate genes like LGALS3 (implicated in neuroinflammation) and PRKCD (activated in mitochondrial-induced apoptosis). Some of the downregulated genes are involved in molecular bindings to ions (i.e., metals, zinc, magnesium) and in ions-related functions. The genes that we found upregulated were involved in the immune response, oxidation-reduction, and apoptosis. These findings may have important implication for the monitoring, e.g., of sALS progression and therefore represent a significant advance in the elucidation of the disease's underlying molecular mechanisms. The extensive multidisciplinary approach we applied in this study was critically important for its success, especially in complex disorders such as sALS, wherein access to genetic background is a major limitation.

The bottlenose dolphin (Tursiops truncatus) is a mammal that belongs to the Cetartiodactyla and have lived in marine ecosystems for nearly 60 millions years. Despite its popularity, our knowledge about its adaptive immunity and evolution is very limited. Furthermore, nothing is known about the genomics and evolution of dolphin antigen receptor immunity. Results: Here we report a evolutionary and expression study of Tursiops truncatus T cell receptor gamma (TRG) and alpha/delta (TRA/TRD) genes. We have identified in silico the TRG and TRA/TRD genes and analyzed the relevant mature transcripts in blood and in skin from four subjects. The dolphin TRG locus is the smallest and simplest of all mammalian loci as yet studied. It shows a genomic organization comprising two variable (V1 and V2), three joining (J1, J2 and J3) and a single constant (C), genes. Despite the fragmented nature of the genome assemblies, we deduced the TRA/TRD locus organization, with the recent TRDV1 subgroup genes duplications, as it is expected in artiodactyls. Expression analysis from blood of a subject allowed us to assign unambiguously eight TRAV genes to those annotated in the genomic sequence and to twelve new genes, belonging to five different subgroups. All transcripts were productive and no relevant biases towards TRAV-J rearrangements are observed. Blood and skin from four unrelated subjects expression data provide evidence for an unusual ratio of productive/unproductive transcripts which arise from the TRG V-J gene rearrangement and for a "public" gamma delta TR repertoire. The productive cDNA sequences, shared both in the same and in different individuals, include biases of the TRGV1 and TRGJ2 genes. The high frequency of TRGV1-J2/TRDV1- D1-J4 productive rearrangements in dolphins may represent an interesting oligo-clonal population comparable to that found in human with the TRGV9- JP/TRDV2-D-J T cells and in primates. Conclusions: Although the features of the TRG and TRA/TRD loci organization reflect those of the so far examined artiodactyls, genomic results highlight in dolphin an unusually simple TRG locus. The cDNA analysis reveal productive TRA/TRD transcripts and unusual ratios of productive/unproductive TRG transcripts. Comparing multiple different individuals, evidence is found for a "public" gamma delta TCR repertoire thus suggesting that in dolphins as in human the gamma delta TCR repertoire is accompanied by selection for public gamma chain.

A handheld laser-induced breakdown spectroscopy (LIBS) instrument is proposed as a novel tool that is able to provide information on the nature of meteorites and discriminate among iron, stone, stony-iron meteorites and meteor-wrongs. Further, a novel fuzzy logic-based inference algorithm is applied to broadband LIBS spectra for the identification of meteorites and their classification according to their origin and nature. The identification of meteorites is a decision-making problem based on a compromise among human experience, visual evidence and analytical data, which fuzzy logic is proved to be able to solve. The final model is able to correctly classify 25 out of 26 samples and provides a set of IF-THEN rules that describe how some selected wavelengths are involved in the classification task.

MicroRNAs (miRNAs) and transcription factors (TFs) play key roles in complex multifactorial diseases like multiple sclerosis (MS). Starting from the miRNomic profile previously associated with a cohort of pediatric MS (PedMS) patients, we applied a combined molecular and computational approach in order to verify published data in patients with adult-onset MS (AOMS). Six out of the 13 selected miRNAs (miR-320a, miR-125a-5p, miR-652-3p, miR-185-5p, miR-942-5p, miR-25-3p) were significantly upregulated in PedMS and AOMS patients, suggesting that they may be considered circulating biomarkers distinctive of the disease independently from age. A computational and unbiased miRNA-based screening of target genes not necessarily associated to MS was then performed in order to provide an extensive view of the genetic mechanisms underlying the disease. A comprehensive MS-specific miRNA-TF co-regulatory network was hypothesized; among others, SP1, RELA, NF-B, TP53, AR, MYC, HDAC1, and STAT3 regulated the transcription of 61 targets. Interestingly, NF-B and STAT3 cooperatively regulate the expression of immune response genes and control the cross-talk between inflammatory and immune cells. Further functional analysis will be performed on the identified critical hubs. Above all, in our view, this approach supports the need of multidisciplinary strategies for shedding light into the pathogenesis of MS.

Motivations. Metagenomics is experiencing an explosive improvement from the advent of high-throughput next-generation sequencing (NGS) technologies which allows an unprecedented large-scale identification of microorganisms living in almost every environment. In particular, the use of amplicon-based metagenomic approach to explore the diversity of fungal environmental communities is increasingly expanding. At the species level, a number of studies have used the non-conserved internal transcribed spacers (ITS) 1 and 2 of the ribosomal RNA genes cluster as genetic markers to explore the fungal taxonomic diversity. Particularly, ITS1 is gaining an increasing popularity as better discriminating species marker in Fungi because of its higher variability compared to ITS2. Starting from the total DNA extracted from any environmental sample, this locus can be easily amplified with taxonomically universal primers and sequenced by means of high-throughput next generation platforms. Reference databases and robust supporting taxonomies are crucial in assigning phylogenetic affiliation to the huge amount of produced sequences. Even if a large number of ITS1 sequences are collected in public databases, a specialized resource focused particularly on this region, where sequences identity, boundaries and taxonomic assignment are validated, is still needed at present. In this work we present ITSoneDB, a new comprehensive collection of ITS1 sequences belonging to Fungi Kingdom.Methods. ITSoneDB has been generated and populated using a multi-step Python workflow. In the first step the ribosomal RNA gene cluster sequences of Fungi including the target ITS1 region were retrieved from Genbank. Then, ITS1 start and end boundaries were extracted from the Features Tables annotations, if available. In order to infer, validate and, eventually, redesign the ITS1 location, Hidden Markov Model (HMM) profiles of flanking genes for 18S and 5.8S ribosomal RNA, generated from their reference alignments stored in RFAM database, were mapped on the entire collection of retrieved nucleotide sequences, by means of the hmmsearch tool from HMMER 3.0 package.Results. At present, ITSoneDB includes 405,433 taxonomically arranged sequence entries provided with ITS1 both start and end positions defined by GenBank annotations and/or HMM based method. ITSoneDB front-end is a JAVA platform-based website for data browsing and downloading. The database can be queried by species or taxon name, GenBank accession ID or by "expanding" the target rank on a detailed fungal taxonomical tree. The complete ITS1 sequences dataset collected in ITSoneDB is available in Fasta format and the users can extract and locally save all or selected queried ITS1 sequences for further analysis.

When the reads obtained from high-throughput sequencing are mapped against a reference database, some of them - known as multireads - can map to more than one reference sequence. This event occurs because genomes contains many repeated portions and reads are generally shorter than reference sequences. Removing the multireads from the mapping results causes an underestimation of the read counts, while estimating the real read count can lead to false positives during the detection of differentially expressed sequences.

Extracellular vesicles (EVs), nanoparticles originated from different cell types, seem to be implicated in several cellular activities. In the Central Nervous System (CNS), glia and neurons secrete EVs and recent studies have demonstrated that the intercellular communication mediated by EVs has versatile functional impact in the cerebral homeostasis. This essential role may be due to their proteins and RNAs cargo that possibly modify the phenotypes of the targeted cells. Despite the increasing importance of EVs, little is known about their fluctuations in physiological as well as in pathological conditions. Furthermore, only few studies have investigated the contents of contemporary EVs subgroups (microvesicles, MVs and exosomes, EXOs) with the purpose of discriminating between their features and functional roles. In order to possibly shed light on these issues, we performed a pilot study in which MVs and EXOs extracted from serum samples of a little cohort of subjects (patients with the first clinical evidence of CNS demyelination, also known as Clinically Isolated Syndrome and Healthy Controls) were submitted to deep small-RNA sequencing. Data were analysed by an in-home bioinformatics platform. In line with previous reports, distinct classes of non-coding RNAs have been detected in both the EVs subsets, offering interesting suggestions on their origins and functions. We also verified the feasibility of this extensive molecular approach, thus supporting its valuable use for the analysis of circulating biomarkers (e.g., microRNAs) in order to investigate and monitor specific diseases.

High-throughput technologies (HT), such as microarray and especially Next-Generation Sequencing (NGS) technologies, have provided tremendous potential for profiling protein-coding and non- protein coding RNAs (ncRNAs). Recent reports of the ENCODE project underline that while 80% of the human genome is transcribed, only 2% is protein coding, suggesting that the vast majority of the genome is transcribed as non-protein-coding RNA.We present the development of a web-based bioinformatics platform, nc-aReNA, for the mapping, classification and annotation of human and mouse ncRNAs from HT-NGS data. The platform is based on a data-warehouse approach and workflow environment that includes data quality control, genome and nc-RNAome sequence alignment, differential expression profiling analysis and statistics of classified data.MethodsThe nc-aReNA architecture is based on a modular analysis pipeline, flanked by a data-warehouse, for the classification and annotation of small-RNAseqdata. The pipeline takes in input the sequenced reads in FASTQ format. After the initial steps of adaptor removal and quality check, the input reads are mapped to an in-house non-redundant ncRNA reference database (http://ncRNAdb.ba.itb.cnr.it) which collects and integrates ncRNA gene lists, from MGI (Mouse Genome Informatics) and HGNC (Human Genome Nomenclature Committee), with sequences and biotype annotations from VEGA (Vertebrate Genome Annotation), ENSEMBL, RefSeq, RFam (for tRNA sequence) and miRBase (for miRNA). NGS reads mapped in this step are classified by using Sequence Ontology (SO) (Eilbeck K. et al., 2005). Unmapped reads are aligned to the reference genome and tagged to the corresponding genomic locus.Integrated statistics are used for RPM (Reads Per Million), fold changes and False Discovery Rate (FDR) corrected p-values calculation and differential expression analysis of all (or user-chosen) ncRNA classes, by comparing two or more experimental conditions or time-courses data.An additional module, called "miRNA identification", provides the analysis of all unmapped miRNA-like reads by mean of the miRDeep2 software.All the analysis results and annotation are stored in a data-warehouse implemented with Infobright (http://www.infobright.org). A user-friendly web-based Graphical User Interface (GUI), developed by using the JAVA platform, guides the user in the submission process and displays results in tables and graphs.ResultsThe main features of the nc-aReNA are:- identification and classification of reads in known functional ncRNA categories in SO;- identification and filtering of reads mapping to ribosomal RNAs and mtDNA transcripts;- RPMs calculation for each known ncRNA;- the export of user-selected classesof ncRNA for further specific investigation;- quantification of ncRNAs expression and differential expression analysis for all identified ncRNAclasses;- graphical visualization of sample expression profiles;- additional annot

MOTIVATION:The recent availability of next generation sequencing (NGS) technologies, has provided the scientific community with an unprecedented opportunity for large-scale analysis of genome in a large number of organisms. One of the most challenging task for bioinformaticians is to develop tools that provide biologists with an easy access to curated and non-redundant collections of sequence data.Non-coding RNAs, for a long time believed to be not-functional, are emerging as the most large and important family of gene regulators.METHODS:NonCode aReNA DataBase is a comprehensive and non-redundant source of manually curated and automatically annotated ncRNA transcripts collected from major public resources.The database is built through a set of ETL (Extraction Transformation Loading) automated processes which extracts and collects data from VEGA, ENSEMBL, RefSeq, miRBase, GtRNAdb and piRNABank. The automatic process guarantees also recurring updates.The identification of redundant sequences is made by analyzing both cross-link references and sequence similarity. Furthermore non-coding RNA sequences have been classified in diverse biotypes and associated to Sequence Ontology terms.NonCode aReNA DataBase is originally developed as a component of a bigger project, represented by a datawarehouse and an analysis workflow, for the functional annotation of ncRNAs from NGS data.RESULTS:NonCode aReNA Database is currently available as a web-resource at http://ncrnadb.ba.itb.cnr.it/. The database can be queried by using multi-criteria and ontological search, through an easy-to-use web interface. Query results can be exported as non-redundant collections of ncRNA transcripts.Currently NonCode aReNA DataBase contains 134,908 human ncRNAs classified in 24 biotypes, and next updates will include transcripts of Mus musculus and Arabidopsis thaliana

Children born small for gestational age (SGA) are at increased risk of metabolic dysfunction. Dysregulation of specific microRNAs (miRNAs) contributes to aberrant gene expression patterns underlying metabolic dysfunction. Objective: We aimed to determine and compare circulating miRNA (c-miRNA) profile of SGA and appropriate for gestational age (AGA) children with obesity and with normal weight, in order to identify biomarkers for early detection of increased risk of developing metabolic dysfunction in SGA and AGA children with obesity. Methods: Small non-coding RNAs from serum of 15 SGA children with obesity (OB-SGA), 10 SGA children with normal weight (NW-SGA), 17 AGA children with obesity (OB-AGA) and 12 AGA children with normal weight (NW-AGA) (mean age 11.2 ± 2.6) have been extracted and sequenced in order to detect and quantify miRNA expression profiles. Results: RNA-seq analyses showed 28 miRNAs dysregulated in OB-SGA vs. NW-SGA and 19 miRNAs dysregulated in OB-AGA vs. NW-AGA. Among these, miR-92a-3p, miR-122-5p, miR-423-5p, miR-484, miR-486-3p and miR-532-5p were up regulated, and miR-181b-5p was down regulated in both OB-SGA and OB-AGA compared with normal weight counterparts. Pathway analysis and miRNA target prediction suggested that these miRNAs were particularly involved in insulin signalling, glucose transport, insulin resistance, cholesterol and lipid metabolism. Conclusion: We identified a specific profile of c-miRNAs in SGA and AGA children with obesity compared with SGA and AGA children with normal weight. These c-miRNAs could represent specific biomarkers for early detection of increased risk of developing metabolic dysfunction in SGA and AGA children with obesity.

PlantPIs is a web querying system for a database collection of plant protease inhibitors data. Protease inhibitors in plants are naturally occurring proteins that inhibit the function of endogenous and exogenous proteases. In this paper the design and development of a web framework providing a clear and very flexible way of querying plant protease inhibitors data is reported. The web resource is based on a relational database, containing data of plants protease inhibitors publicly accessible, and a graphical user interface providing all the necessary browsing tools, including a data exporting function. PlantPIs contains information extracted principally from MEROPS database, filtered, annotated and compared with data stored in other protein and gene public databases, using both automated techniques and domain expert evaluations. The data are organized to allow a flexible and easy way to access stored information. The database is accessible at http://www.plantpis.ba.itb.cnr.it/.

Metagenomics is providing an unprecedented access to the environmental microbial diversity. The amplicon-basedmetagenomics approach involves the PCR-targeted sequencing of a genetic locus fitting different features. Namely,it must be ubiquitous in the taxonomic range of interest, variable enough to discriminate between different speciesbut flanked by highly conserved sequences, and of suitable size to be sequenced through next-generation platforms.The internal transcribed spacers 1 and 2 (ITS1 and ITS2) of the ribosomal DNA operon and one or morehyper-variable regions of 16S ribosomal RNA gene are typically used to identify fungal and bacterial species, respectively.In this context, reliable reference databases and taxonomies are crucial to assign amplicon sequence reads tothe correct phylogenetic ranks. Several resources provide consistent phylogenetic classification of publicly available16S ribosomal DNA sequences, whereas the state of ribosomal internal transcribed spacers reference databases isnotably less advanced. In this review, we aim to give an overview of existing reference resources for both types ofmarkers, highlighting strengths and possible shortcomings of their use for metagenomics purposes. Moreover, wepresent a new database, ITSoneDB, of well annotated and phylogenetically classified ITS1 sequences to be used asa reference collection in metagenomic studies of environmental fungal communities. ITSoneDB is available for downloadand browsing at http://itsonedb.ba.itb.cnr.it/.

The recent availability of high throughput tech- nologies, like next generation sequencing (NGS) platforms, has providedthescientific community with an unprecedented opportunity for large- scale analysis of genome in a large number of organisms.However,among others, one of the most challenging task for bioinformaticians is to developtools that providebiologists withaneasy access to curated and non-redundant collec- tions of sequence data.Non-coding RNAs, for a long time believed tobe not-functional, are emerging as themost large and important family of gene regulators. NonCode aReNA Database is a comprehensive and non-redundant source ofmanually curated and automatically annotated ncRNA transcripts. Originally developed as a component of a big- ger project, composed by a datawarehouse for the functional annotation of ncRNAs fromNGS data, NonCode aReNA DB is currently availableas a web-resource at http://ncrnadb.ba.itb.cnr. it/. Sequences have been classified in diverse biotypes and associated to SequenceOntology terms. The database can be queried by using multi-criteria and ontological search, through an easy-to-use web interface, and data exported as non-redundant collections of transcripts an- notated in VEGA, ENSEMBL, RefSeq, miRBase, GtRNAdb and piRNABank. The database is up- dated through an automatic pipeline and last updatewasonJanuary 2015. PresentlyNonCode aReNA DB contains 134,908 human ncRNAs clas- sified in 24 biotypes, and next update will include transcripts ofMusmusculus and Arabidopsis thal- iana.AcknowledgementsThis work was supported by the Italian MIUR Flagship Project "Epigen".