Nico Sanna

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

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.

The mepsMAP Server. Mapping Epitopes on Protein Surface: Mining Annotated Proteins

For a growing number of biologists DNA or protein data are typically retrieved and managed on the Web, and not in the laboratory. A large number of bioinformatics datasets from primary and (thousands of) secondary databases are scattered on the Web in various formats. A biologist end-user might need to access and use tens of databases and tools every day. For this reason, the bioinformatics community is developing more and more service-oriented architectures (SOAs): software architecture of loosely coupled software services that can be accessed without knowledge of, or control over, their internal architecture. Data-processing and analysis tasks can be automated by having free access to bioinformatics Web services (WSs) that are the building blocks of the SOAs. In this paper we introduce a new bioinformatics Web server, mepsMAP (mapping epitopes on protein surface: Mining Annotated Proteins), developed to identify the recognition sites between antibodies and their cognate antigens. In some cases, the recognition site is represented by a continuous segment of the antigen sequence, but much more often the epitope is conformational, i.e. the antibody recognizes the location and type of exposed antigen side chains that are not necessarily contiguous in the antigens sequence, but brought together by its three-dimensional structure. A facility on the server allows the user to search putative conformational epitopes on protein surface, querying the system for proteins with a given annotation. The mepsMAP server has been implemented as a SOA composed by a database and a set of four WSs. We present here the software architecture of the system with a detailed description of the WS dataflow that has been optimized to provide the best computing performance while maintaining the easiest end-user access to the system via a Web interface.

A high performance grid-web service framework for the identification of 'conserved sequence tags'

The continuous increasing of computing power in biological research places a threshold to the single host use and suggests an approach based on distributed computing. An emerging solution is grid technology, which allows organization to make better use of existing computing resources by providing them with a single, transparent, aggregated source of computing power. Equally, bioinformatics analysis often involves many web services, allowing shared access to information and helping the biologist to design, describe, record complex experiments. A new generation of grid infrastructure, where web services are building blocks, allow managent of a web services workflow. This work shows a tool for the identification and functional annotation of Conserved Sequence Tags (CSTs) through cross-species genome comparisons, deployed on a Grid System Architecture, based on Web Services concepts and technologies.