ITSoneDB: a comprehensive collection of eukaryotic ribosomal RNA Internal Transcribed Spacer 1 (ITS1) sequences.

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.

Reference databases for taxonomic assignment in metagenomics.

Metagenomics is providing an unprecedented access to the environmental microbial diversity. The amplicon-based metagenomics 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 species but 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 more hyper-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 to the correct phylogenetic ranks. Several resources provide consistent phylogenetic classification of publicly available 16S ribosomal DNA sequences, whereas the state of ribosomal internal transcribed spacers reference databases is notably less advanced. In this review, we aim to give an overview of existing reference resources for both types of markers, highlighting strengths and possible shortcomings of their use for metagenomics purposes. Moreover, we present a new database, ITSoneDB, of well annotated and phylogenetically classified ITS1 sequences to be used as a reference collection in metagenomic studies of environmental fungal communities. ITSoneDB is available for download and browsing at http://itsonedb.ba.itb.cnr.it/.

BEAT: Bioinformatics Exon Array Tool to store, analyze and visualize Affymetrix GeneChip Human Exon Array data from disease experiments.

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.

GIDL: a rule based expert system for GenBank Intelligent Data Loading into the Molecular Biodiversity Database.

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.