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High-Throughput Sequencing technologies are transforming many research fields, including the analysis of phage display libraries. The phage display technology coupled with deep sequencing was introduced more than a decade ago and holds the potential to circumvent the traditional laborious picking and testing of individual phage rescued clones. However, from a bioinformatics point of view, the analysis of this kind of data was always performed by adapting tools designed for other purposes, thus not considering the noise background typical of the ‘interactome sequencing’ approach and the heterogeneity of the data. InteractomeSeq is a web server allowing data analysis of protein domains (‘domainome’) or epitopes (‘epitome’) from either Eukaryotic or Prokaryotic genomic phage libraries generated and selected by following an Interactome sequencing approach. InteractomeSeq allows users to upload raw sequencing data and to obtain an accurate characterization of domainome/epitome profiles after setting the parameters required to tune the analysis. The release of this tool is relevant for the scientific and clinical community, because InteractomeSeq will fill an existing gap in the field of large-scale biomarkers profiling, reverse vaccinology, and structural/functional studies, thus contributing essential information for gene annotation or antigen identification. InteractomeSeq is freely available at https://InteractomeSeq.ba.itb.cnr.it/

The analysis of gene expression data is a complex task, and many tools and pipelines are available to handle big sequencing datasets for case-control (bivariate) studies. In some cases, such as pilot or exploratory studies, the researcher needs to compare more than two groups of samples consisting of a few replicates. Both standard statistical bioinformatic pipelines and innovative deep learning models are unsuitable for extracting interpretable patterns and information from such datasets. In this work, we apply a combination of fuzzy rule systems and genetic algorithms to analyze a dataset composed of 21 samples and 6 classes, useful for approaching the study of expression profiles in ovarian cancer, compared to other ovarian diseases. The proposed method is capable of performing a feature selection among genes that is guided by the genetic algorithm, and of building a set of if-then rules that explain how classes can be distinguished by observing changes in the expression of selected genes. After testing several parameters, the final model consists of 10 genes involved in the molecular pathways of cancer and 10 rules that correctly classify all samples.

Summary ITSoneWB (ITSone WorkBench) is a Galaxy-based bioinformatic environment where comprehensive and high-quality reference data are connected with established pipelines and new tools in an automated and easy-to-use service targeted at global taxonomic analysis of eukaryotic communities based on Internal Transcribed Spacer 1 variants high-throughput sequencing. Availability and implementation ITSoneWB has been deployed on the INFN-Bari ReCaS cloud facility and is freely available on the web at http://itsonewb.cloud.ba.infn.it/galaxy. Supplementary information Supplementary data are available at Bioinformatics online.

A holistic understanding of environmental communities is the new challenge of metagenomics. Accordingly, the amplicon-based or metabarcoding approach, largely applied to investigate bacterial microbiomes, is moving to the eukaryotic world too. Indeed, the analysis of metabarcoding data may provide a comprehensive assessment of both bacterial and eukaryotic composition in a variety of environments, including human body. In this respect, whereas hypervariable regions of the 16S rRNA are the de facto standard barcode for bacteria, the Internal Transcribed Spacer 1 (ITS1) of ribosomal RNA gene cluster has shown a high potential in discriminating eukaryotes at deep taxonomic levels. As metabarcoding data analysis rely on the availability of a well-curated barcode reference resource, a comprehensive collection of ITS1 sequences supplied with robust taxonomies, is highly needed. To address this issue, we created ITSoneDB (available at http://itsonedb.cloud.ba.infn.it/) which in its current version hosts 985 240 ITS1 sequences spanning over 134 000 eukaryotic species. Each ITS1 is mapped on the NCBI reference taxonomy with its start and end positions precisely annotated. ITSoneDB has been developed in agreement to the FAIR guidelines by enabling the users to query and download its content through a simple web-interface and access relevant metadata by cross-linking to European Nucleotide Archive.

The structural and conformational organization of chromosomes is crucial for gene expression regulation in eukaryotes and prokaryotes as well. Up to date, gene expression data generated using either microarray or RNA-sequencing are available for many bacterial genomes. However, differential gene expression is usually investigated with methods considering each gene independently, thus not taking into account the physical localization of genes along a bacterial chromosome. Here, we present WoPPER, a web tool integrating gene expression and genomic annotations to identify differentially expressed chromosomal regions in bacteria. RNA-sequencing or microarray-based gene expression data are provided as input, along with gene annotations. The user can select genomic annotations from an internal database including 2780 bacterial strains, or provide custom genomic annotations. The analysis produces as output the lists of positionally related genes showing a coordinated trend of differential expression. Graphical representations, including a circular plot of the analyzed chromosome, allow intuitive browsing of the results. The analysis procedure is based on our previously published R-package PREDA. The release of this tool is timely and relevant for the scientific community, as WoPPER will fill an existing gap in prokaryotic gene expression data analysis and visualization tools. WoPPER is open to all users and can be reached at the following URL: https://WoPPER.ba.itb.cnr.it

The winemaking process produces a huge number of pomaces that generally are used for energy purposes. Further valuable applications such as health-promoting properties are still under investigation. The seeds of the white berries of Mantonico and Pecorello cv. were extracted in a Soxhlet apparatus, using n-hexane and chloroform as solvents. Extracts were characterized by NMR and GC-MS analyses. They were assayed in vitro as wound healing and anti-inflammatory agents in HaCaT and RAW 264.7 cell lines, respectively. n-hexane Mantonico extract resulted in the most interesting wound healing sample, while n-hexane Pecorello, containing a good number of carotenoids, resulted in a good anti-inflammatory candidate. These preliminary findings underlined the benefit of grape seed extracts valorization due to their health-promoting properties.

Attention Deficit Hyperactivity Disorder (ADHD) is a childhood-onset neurodevelopmental disorder, whose etiology and pathogenesis are still largely unknown. In order to uncover novel regulatory networks and molecular pathways possibly related to ADHD, we performed an integrated miRNA and mRNA expression profiling analysis in peripheral blood samples of children with ADHD and age-matched typically developing (TD) children. The expression levels of 13 miRNAs were evaluated with microfluidic qPCR, and differentially expressed (DE) mRNAs were detected on an Illumina HiSeq 2500 genome analyzer. The miRNA targetome was identified using an integrated approach of validated and predicted interaction data extracted from seven different bioinformatic tools. Gene Ontology (GO) and pathway enrichment analyses were carried out. Results showed that six miRNAs (miR-652-3p, miR-942-5p, let-7b-5p, miR-181a-5p, miR-320a, and miR-148b-3p) and 560 genes were significantly DE in children with ADHD compared to TD subjects. After correction for multiple testing, only three miRNAs (miR-652-3p, miR-148b-3p, and miR-942-5p) remained significant. Genes known to be associated with ADHD (e.g., B4GALT2, SLC6A9 TLE1, ANK3, TRIO, TAF1, and SYNE1) were confirmed to be significantly DE in our study. Integrated miRNA and mRNA expression data identified critical key hubs involved in ADHD. Finally, the GO and pathway enrichment analyses of all DE genes showed their deep involvement in immune functions, reinforcing the hypothesis that an immune imbalance might contribute to the ADHD etiology. Despite the relatively small sample size, in this study we were able to build a complex miRNA-target interaction network in children with ADHD that might help in deciphering the disease pathogenesis. Validation in larger samples should be performed in order to possibly suggest novel therapeutic strategies for treating this complex disease.

Farid Chemat : GREEN Team Extraction; International audience; Full analytical data of Ecuadorian cocoa wastes (raw shells) and beans (as benchmark), are herein reported. A detailed characterization of production residues may pave the road to a zero-waste strategy for the cocoa industry. Multiple analytical techniques have been exploited to define the composition of the matrices, among them: elemental analyses, FTIR, Py-GC/MS/FID and UHPLC-ESI-MS/MS. Quali-quantitative data of carbohydrates, lipids, lignin, poly-phenols, alkaloids and proteins have been obtained by Py-GC/MS/FID and UHPLC-ESI-MS/MS. Assignations are fully supported by literature references. The FAMEs composition of lipophilic UAE extract is also reported for sake of comparison with cocoa butter. This data collection completes a wider valorization work, "Cocoa bean shell waste valorisation; extraction from lab to pilot-scale cavitational reactors" (Grillo et al., 2018).

International audience; The use of zero-waste processes to integrate food-waste valorisation into the circular economy equation is currently one of the hottest topics in sustainability research. This goal is still far from being fully achieved despite the release of a number of patents and papers that deal with the topic. The present work aims to valorise cocoa shells, one of the main by-product of the roasting process, in order to enhance the effective extraction of high added value compounds by means of green protocols. The high potential added value of the residual waste has been demonstrated via a direct analytical comparison of extracts and bean composition. A range of raw matrix extraction procedures have been investigated in order to define the best solvent and technology; ultrasound (US) and hydrodynamic cavitation (HC) were compared with conventional methods. The high-energy microenvironments generated by cavitation substantially promote fast biomass deconstruction with low energy consumption. The optimized protocol couples a HC reactor with a ternary water/ethanol/hexane mixture, simultaneously providing a hydrophilic product, which is rich in methylxanthines and polyphenols, and a lipid layer. Sequential milling and sieving pretreatment provided an enriched shell fraction via the partial removal of husk fibres (54.45 vs. 81.36 w/w % total fibres). The disposal of the latter reduces mass balance, but is rerouted into animal feedstock components and crop mulching. The protocols herein reported produce valuable extracts, which are rich in antioxidant flavanols (catechins and epicatechins), theobromine (32.7 ± 0.12 mg/g shells), caffeine (1.76 ± 0.08 mg/g shells) and cocoa butter, in a simple and easy manner. This new valorisation process afforded 20.5 w/w % and 15.8 w/w % hydrophilic and lipophilic fractions, respectively, when scaled up to function in a pilot flow reactor. The fatty acids, obtained in remarkable yield (forming the 96.4 w/w % of the total light part) well match the commercial cocoa butter profile. The antioxidant extract shows an impressive total phenolic content of 197.4 mg/g extract (gallic acid eq.), with a radical scavenging activity of 62.0 ± 3.1 μg/mL (expressed in DPPH EC50). This work should facilitate industrial design for the convenient recovery of cocoa by-products as part of a zero-waste strategy.

Folding reporters are proteins with easily identifiable phenotypes, such as antibiotic resistance, whose folding and function is compromised when fused to poorly folding proteins or random open reading frames. We have developed a strategy where, by using TEM-1 β-lactamase (the enzyme conferring ampicillin resistance) on a genomic scale, we can select collections of correctly folded protein domains from the coding portion of the DNA of any intronless genome. The protein fragments obtained by this approach, the so called "domainome", will be well expressed and soluble, making them suitable for structural/functional studies. By cloning and displaying the "domainome" directly in a phage display system, we have showed that it is possible to select specific protein domains with the desired binding properties (e.g., to other proteins or to antibodies), thus providing essential experimental information for gene annotation or antigen identification. The identification of the most enriched clones in a selected polyclonal population can be achieved by using novel next-generation sequencing technologies (NGS). For these reasons, we introduce deep sequencing analysis of the library itself and the selection outputs to provide complete information on diversity, abundance and precise mapping of each of the selected fragment. The protocols presented here show the key steps for library construction, characterization, and validation.

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.

Background 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. Results We 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. Conclusions The 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. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1195-2) contains supplementary material, which is available to authorized users.

Nowadays DNA meta-barcoding is a powerful instrument capable of quickly discovering the biodiversity of an environmental sample by integrating the DNA barcoding approach with High Throughput Sequencing technologies. It mainly consists of the parallel reading of informative genomic fragment/s able to discriminate living entities. Although this approach has been widely studied, it still needs optimization in some necessary steps requested in its advanced accomplishment. A fundamental element concerns the standardization of bioinformatic analyses pipelines. The aim of the present study was to underline a number of critical parameters of laboratory material preparation and taxonomic assignment pipelines in DNA meta-barcoding experiments using the cytochrome oxidase subunit-I (coxI) barcode region, known as a suitable molecular marker for animal species identification. We compared nine taxonomic assignment pipelines, including a custom in-house method, based on Hidden Markov Models. Moreover, we evaluated the potential influence of universal primers amplification bias in qPCR, as well as the correlation between GC content with taxonomic assignment results. The pipelines were tested on a community of known terrestrial invertebrates collected by pitfall traps from a chestnut forest in Italy. Although the present analysis was not exhaustive and needs additional investigation, our results suggest some potential improvements in laboratory material preparation and the introduction of additional parameters in taxonomic assignment pipelines. These include the correct setup of OTU clustering threshold, the calibration of GC content affecting sequencing quality and taxonomic classification, as well as the evaluation of PCR primers amplification bias on the final biodiversity pattern. Thus, careful attention and further validation/optimization of the above-mentioned variables would be required in a DNA meta-barcoding experimental routine.

High-performance Next-Generation Sequencing (NGS) has become a widely used technology to characterize case-control comparison studies for RNA transcripts, such as mRNAs and small non-coding RNAs. The first step in the analysis strategies is mapping NGS reads against a reference database and a critical issue emerges in this phase: the problem of multireads. In this paper we present a novel approach to represent and quantify read mapping ambiguities through the use of fuzzy sets and possibility theory. The aim of this work is to obtain a list of candidate differential expression events, providing a description of the uncertainty of the results due to multiread presence. In a preliminary experiment on HeLa cells, the method correctly detected the possibility of false positiveness, while on a case-control study of human endobronchial biopsies, the method identified 11 genes with possible different expression, four of them with an uncertain fold change. This last result was confirmed by FDR adjusted Fisher's test, while DESeq2 did not provide significant differences between case and control.

The 5' and 3' untranslated regions of eukaryotic mRNAs play crucial roles in the post-transcriptional regulation of gene expression through the modulation of nucleo-cytoplasmic mRNA transport, translation efficiency, subcellular localization and message stability. UTRdb is a curated database of 5' and 3' untranslated sequences of eukaryotic mRNAs, derived from several sources of primary data. Experimentally validated functional motifs are annotated (and also collated as the UTRsite database) and cross-links to genomic and protein data are provided. The integration of UTRdb with genomic and protein data has allowed the implementation of a powerful retrieval resource for the selection and extraction of UTR subsets based on their genomic coordinates and/or features of the protein encoded by the relevant mRNA (e.g. GO term, PFAM domain, etc.). All internet resources implemented for retrieval and functional analysis of 5' and 3' untranslated regions of eukaryotic mRNAs are accessible at http://www.ba.itb.cnr.it/UTR/.

Background It is known from recent studies that more than 90% of human multi-exon genes are subject to Alternative Splicing (AS), a key molecular mechanism in which multiple transcripts may be generated from a single gene. It is widely recognized that a breakdown in AS mechanisms plays an important role in cellular differentiation and pathologies. Polymerase Chain Reactions, microarrays and sequencing technologies have been applied to the study of transcript diversity arising from alternative expression. Last generation Affymetrix GeneChip Human Exon 1.0 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 user-friendly bioinformatics framework to store, analyze and visualize exon arrays datasets. It combines a data warehouse 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 statistical methods and an easy-to-use graphical web front-end. BEAT has been tested on a dataset with 173 samples and tuned using new datasets of exon array experiments from 28 colorectal cancer and 26 renal cell cancer samples produced 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 biochemical pathways annotations are integrated with exon and gene level expression plots. The user can customize the results choosing custom thresholds for the statistical parameters and exploiting the available clinical data of the samples for a multivariate AS analysis. Conclusions Despite exon array chips being widely used for transcriptomics studies, there is a lack of analysis tools offering advanced statistical features and requiring no programming knowledge. BEAT provides a user-friendly platform for a comprehensive study of AS events in human diseases, displaying the analysis results with easily interpretable and interactive tables and graphics.

Background In the scientific biodiversity community, it is increasingly perceived the need to build a bridge between molecular and traditional biodiversity studies. We believe that the information technology could have a preeminent role in integrating the information generated by these studies with the large amount of molecular data we can find in bioinformatics public databases. This work is primarily aimed at building a bioinformatic infrastructure for the integration of public and private biodiversity data through the development of GIDL, an Intelligent Data Loader coupled with the Molecular Biodiversity Database. The system presented here organizes in an ontological way and locally stores the sequence and annotation data contained in the GenBank primary database. Methods The GIDL architecture consists of a relational database and of an intelligent data loader software. The relational database schema is designed to manage biodiversity information (Molecular Biodiversity Database) and it is organized in four areas: MolecularData, Experiment, Collection and Taxonomy. The MolecularData area is inspired to an established standard in Generic Model Organism Databases, the Chado relational schema. The peculiarity of Chado, and also its strength, is the adoption of an ontological schema which makes use of the Sequence Ontology. The Intelligent Data Loader (IDL) component of GIDL is an Extract, Transform and Load software able to parse data, to discover hidden information in the GenBank entries and to populate the Molecular Biodiversity Database. The IDL is composed by three main modules: the Parser, able to parse GenBank flat files; the Reasoner, which automatically builds CLIPS facts mapping the biological knowledge expressed by the Sequence Ontology; the DBFiller, which translates the CLIPS facts into ordered SQL statements used to populate the database. In GIDL Semantic Web technologies have been adopted due to their advantages in data representation, integration and processing. Results and conclusions Entries coming from Virus (814,122), Plant (1,365,360) and Invertebrate (959,065) divisions of GenBank rel.180 have been loaded in the Molecular Biodiversity Database by GIDL. Our system, combining the Sequence Ontology and the Chado schema, allows a more powerful query expressiveness compared with the most commonly used sequence retrieval systems like Entrez or SRS.