E’ coordinatore della Divisione Biometria del Pattern Recognition and Applications Laboratory (PRA Lab) diretto dal Prof. Fabio Roli. L’attività di ricerca è incentrata sulle tecnologie biometriche per la sicurezza informatica. In particolare si occupa di classificazione e verifica di impronte digitali e volti, rilevazione di contraffazioni e sistemi multimodali. Ha al suo attivo oltre ottanta pubblicazioni fra riviste, atti di conferenze e congressi, capitoli di libro, tutte di impatto internazionale.
E’ revisore di progetti, riviste e conferenze internazionali.
E’ team leader e responsabile di progetti di ricerca internazionali pubblici (FP-European Union) e privati (Crossmatch) nonché progetti nazionali (PRIN, RAS) e locali (“Giovani Ricercatori”) e di collaborazione con il RaCIS di Cagliari.
He is team leader of the Biometric Unit of the Pattern Recognition and Applications Laboratory (PRA Lab) leaded by Prof. Fabio Roli. His research activity is focused on the biometric Technologies for information security. In particular, identification, verification and vulnerability analysis of fingerprint and face, multi-modal biometric systems. He has co-authored more than one hundred of publications in journal, conference proceedings and books chapters. He also co-authored the voice “Antispoofing: Multimodal” in the last edition of Encyclopedia of Biometrics.
He acts as referee for international projects, journals and conferences.
He is in charge of national and international research projects.
|Titolo:||G-CNV: A GPU-based tool for preparing data to detect CNVs with read-depth methods|
|Data di pubblicazione:||2015|
|Abstract:||Copy number variations (CNVs) are the most prevalent types of structural variations (SVs) in the human genome and are involved in a wide range of common human diseases. Different computational methods have been devised to detect this type of SVs and to study how they are implicated in human diseases. Recently, computational methods based on high-throughput sequencing (HTS) are increasingly used. The majority of these methods focus on mapping short-read sequences generated from a donor against a reference genome to detect signatures distinctive of CNVs. In particular, read-depth based methods detect CNVs by analyzing genomic regions with significantly different read-depth from the other ones. The pipeline analysis of these methods consists of four main stages: (i) data preparation, (ii) data normalization, (iii) CNV regions identification, and (iv) copy number estimation. However, available tools do not support most of the operations required at the first two stages of this pipeline. Typically, they start the analysis by building the read-depth signal from pre-processed alignments. Therefore, third-party tools must be used to perform most of the preliminary operations required to build the read-depth signal. These data-intensive operations can be efficiently parallelized on graphics processing units (GPUs). In this article, we present G-CNV, a GPU-based tool devised to perform the common operations required at the first two stages of the analysis pipeline. G-CNV is able to filter low-quality read sequences, to mask low-quality nucleotides, to remove adapter sequences, to remove duplicated read sequences, to map the short-reads, to resolve multiple mapping ambiguities, to build the read-depth signal, and to normalize it. G-CNV can be efficiently used as a third-party tool able to prepare data for the subsequent read-depth signal generation and analysis. Moreover, it can also be integrated in CNV detection tools to generate read-depth signals.|
|Tipologia:||1.1 Articolo in rivista|