Mattia D'Antonio

ASPicDB: A database resource for alternative splicing analysis

MOTIVATION Alternative splicing has recently emerged as a key mechanism responsible for the expansion of transcriptome and proteome complexity in human and other organisms. Although several online resources devoted to alternative splicing analysis are available they may suffer from limitations related both to the computational methodologies adopted and to the extent of the annotations they provide that prevent the full exploitation of the available data. Furthermore, current resources provide limited query and download facilities. RESULTS ASPicDB is a database designed to provide access to reliable annotations of the alternative splicing pattern of human genes and to the functional annotation of predicted splicing isoforms. Splice-site detection and full-length transcript modeling have been carried out by a genome-wide application of the ASPic algorithm, based on the multiple alignments of gene-related transcripts (typically a Unigene cluster) to the genomic sequence, a strategy that greatly improves prediction accuracy compared to methods based on independent and progressive alignments. Enhanced query and download facilities for annotations and sequences allow users to select and extract specific sets of data related to genes, transcripts and introns fulfilling a combination of user-defined criteria. Several tabular and graphical views of the results are presented, providing a comprehensive assessment of the functional implication of alternative splicing in the gene set under investigation. ASPicDB, which is regularly updated on a monthly basis, also includes information on tissue-specific splicing patterns of normal and cancer cells, based on available EST sequences and their library source annotation. AVAILABILITY www.caspur.it/ASPicDB

WEP: a high-performance analysis pipeline for whole-exome data

BackgroundThe advent of massively parallel sequencing technologies (Next Generation Sequencing, NGS) profoundly modified the landscape of human genetics.In particular, Whole Exome Sequencing (WES) is the NGS branch that focuses on the exonic regions of the eukaryotic genomes; exomes are ideal to help us understanding high-penetrance allelic variation and its relationship to phenotype. A complete WES analysis involves several steps which need to be suitably designed and arranged into an efficient pipeline.Managing a NGS analysis pipeline and its huge amount of produced data requires non trivial IT skills and computational power.ResultsOur web resource WEP (Whole-Exome sequencing Pipeline web tool) performs a complete WES pipeline and provides easy access through interface to intermediate and final results. The WEP pipeline is composed of several steps:1) verification of input integrity and quality checks, read trimming and filtering; 2) gapped alignment; 3) BAM conversion, sorting and indexing; 4) duplicates removal; 5) alignment optimization around insertion/deletion (indel) positions; 6) recalibration of quality scores; 7) single nucleotide and deletion/insertion polymorphism (SNP and DIP) variant calling; 8) variant annotation; 9) result storage into custom databases to allow cross-linking and intersections, statistics and much more. In order to overcome the challenge of managing large amount of data and maximize the biological information extracted from them, our tool restricts the number of final results filtering data by customizable thresholds, facilitating the identification of functionally significant variants. Default threshold values are also provided at the analysis computation completion, tuned with the most common literature work published in recent years.ConclusionsThrough our tool a user can perform the whole analysis without knowing the underlying hardware and software architecture, dealing with both paired and single end data. The interface provides an easy and intuitive access for data submission and a user-friendly web interface for annotated variant visualization.Non-IT mastered users can access through WEP to the most updated and tested WES algorithms, tuned to maximize the quality of called variants while minimizing artifacts and false positives.The web tool is available at the following web address: http://www.caspur.it/wep

MitoZoa 2.0: a database resource and search tools for comparative and evolutionary analyses of mitochondrial genomes in Metazoa

The MITOchondrial genome database of metaZOAns (MitoZoa) is a public resource for comparative analyses of metazoan mitochondrial genomes (mtDNA) at both the sequence and genomic organizational levels. The main characteristics of the MitoZoa database are the careful revision of mtDNA entry annotations and the possibility of retrieving gene order and non-coding region (NCR) data in appropriate formats. The MitoZoa retrieval system enables basic and complex queries at various taxonomic levels using different search menus. MitoZoa 2.0 has been enhanced in several aspects, including: a re-annotation pipeline to check the correctness of protein-coding gene predictions; a standardized annotation of introns and of precursor ORFs whose functionality is post-transcriptionally recovered by RNA editing or programmed translational frameshifting; updates of taxon-related fields and a BLAST sequence similarity search tool. Database novelties and the definition of standard mtDNA annotation rules, together with the user-friendly retrieval system and the BLAST service, make MitoZoa a valuable resource for comparative and evolutionary analyses as well as a reference database to assist in the annotation of novel mtDNA sequences. MitoZoa is freely accessible at http://www.caspur.it/mitozoa.

RAP: RNA-Seq Analysis Pipeline, a new cloud-based NGS web application

BackgroundThe study of RNA has been dramatically improved by the introduction of Next Generation Sequencing platforms allowing massive and cheap sequencing of selected RNA fractions, also providing information on strand orientation (RNA-Seq). The complexity of transcriptomes and of their regulative pathways make RNA-Seq one of most complex field of NGS applications, addressing several aspects of the expression process (e.g. identification and quantification of expressed genes and transcripts, alternative splicing and polyadenylation, fusion genes and trans-splicing, post-transcriptional events, etc.).Moreover, the huge volume of data generated by NGS platforms introduces unprecedented computational and technological challenges to efficiently analyze and store sequence data and results.MethodsIn order to provide researchers with an effective and friendly resource for analyzing RNA-Seq data, we present here RAP (RNA-Seq Analysis Pipeline), a cloud computing web application implementing a complete but modular analysis workflow. This pipeline integrates both state-of-the-art bioinformatics tools for RNA-Seq analysis and in-house developed scripts to offer to the user a comprehensive strategy for data analysis. RAP is able to perform quality checks (adopting FastQC and NGS QC Toolkit), identify and quantify expressed genes and transcripts (with Tophat, Cufflinks and HTSeq), detect alternative splicing events (using SpliceTrap) and chimeric transcripts (with ChimeraScan). This pipeline is also able to identify splicing junctions and constitutive or alternative polyadenylation sites (implementing custom analysis modules) and call for statistically significant differences in genes and transcripts expression, splicing pattern and polyadenylation site usage (using Cuffdiff2 and DESeq).ResultsThrough a user friendly web interface, the RAP workflow can be suitably customized by the user and it is automatically executed on our cloud computing environment. This strategy allows to access to bioinformatics tools and computational resources without specific bioinformatics and IT skills. RAP provides a set of tabular and graphical results that can be helpful to browse, filter and export analyzed data, according to the user needs.

ExpEdit: a webserver to explore human RNA editing in RNA-Seq experiments

UNLABELLED ExpEdit is a web application for assessing RNA editing in human at known or user-specified sites supported by transcript data obtained by RNA-Seq experiments. Mapping data (in SAM/BAM format) or directly sequence reads [in FASTQ/short read archive (SRA) format] can be provided as input to carry out a comparative analysis against a large collection of known editing sites collected in DARNED database as well as other user-provided potentially edited positions. Results are shown as dynamic tables containing University of California, Santa Cruz (UCSC) links for a quick examination of the genomic context. AVAILABILITY ExpEdit is freely available on the web at http://www.caspur.it/ExpEdit/.

MetaShot: an accurate workflow for taxon classification of host-associated microbiome from shotgun metagenomic data

Summary: Shotgun metagenomics by high‐throughput sequencing may allow deep and accurate characterization of host‐associated total microbiomes, including bacteria, viruses, protists and fungi. However, the analysis of such sequencing data is still extremely challenging in terms of both overall accuracy and computational efficiency, and current methodologies show substantial variability in misclassification rate and resolution at lower taxonomic ranks or are limited to specific life domains (e.g. only bacteria). We present here MetaShot, a workflow for assessing the total microbiome composition from host‐associated shotgun sequence data, and show its overall optimal accuracy performance by analyzing both simulated and real datasets. Availability and Implementation: https://github.com/bfosso/MetaShot Contact: graziano.pesole@uniba.it Supplementary information: Supplementary data are available at Bioinformatics online.

The transcriptional response of mammalian cancer cells to irradiation is dominated by a cell cycle signature which is strongly attenuated in non-cancer cells and tissues.

Abstract Purpose: Our goal was to identify genes showing a general transcriptional response to irradiation in mammalian cells and to analyze their response in function of dose, time and quality of irradiation and of cell type. Materials and methods: We used a modified MIAME (Minimal Information About Microarray Experiments) protocol to import microarray data from 177 different irradiation conditions in the Radiation Genes database and performed cut-off-based selections and hierarchical gene clustering. Results: We identified a set of 29 genes which respond to a wide range of irradiation conditions in different cell types and tissues. Functional analysis of the negatively modulated genes revealed a dominant signature of mitotic cell cycle regulation which appears both dose and time-dependent. This signature is prominent in cancer cells and highly proliferating tissues but it is strongly attenuated in non cancer cells. Conclusions: The transcriptional response of mammalian cancer cells to irradiation is dominated by a mitotic cell cycle signature both dose and time-dependent. This core response, which is present in cancer cells and highly proliferating tissues such as skin, blood and lymph node, is weaker or absent in non-cancer cells and in liver and spleen. CDKN1A (cyclin-dependent kinase inhibitor 1A) appears as the most generally induced mammalian gene and its response (mostly dose- and time-independent) seems to go beyond the typical DNA damage response.

HT-RLS: High-Throughput Web Tool for Analysis of DNA Microarray Data Using RLS classifiers

Gene expression from DNA microarray data offers biologists and pathologists the possibility to deal with the problem of disease (e. g. cancer) diagnosis and prognosis from a quantitative point of view. Microarray data provide a snapshot of the molecular status of a sample of cells in a given tissue, returning the expression levels of thousands of genes simultaneously. Several mathematical methods from learning theory, such as Regularized Least Squares (RLS) classifiers or Support Vector Machines (SVM), have been extensively adopted to classify gene expression data. These methods can be useful to answer some relevant questions such as 1) what is the right amount of data to build an accurate classifier? 2) How many and which genes are correlated with a specific pathology? The computational analysis to statistically estimate the accuracy of the chosen models is particularly time consuming, burning several days of CPU time and without high-throughput or high- performance tools becomes practically unfeasible to obtain results in a reasonable time for biomedical community. We have implemented an independent, flexible and scalable platform, for a high-throughput large-scale microarray gene expression data analysis and classification, based on R tool for statistical computing. It integrates databases and computational intensive algorithms, based on RLS classifiers and a powerful web client for data training and graphical visualization of predicted results. Our platform provides statistically significant answers to the study of the gene expression by means of microarray data and supplying useful information to relevant questions in the diagnosis and prognosis of diseases in a reasonable time. The web resource is available free of charge for academic and non-profit institutions.