Giovanni Nigita
Ohio State University, Molecular Virology, Immunology and Medical Genetics, Visiting postdoctoral researcher
He received his bachelor's degree in Applicated Computer Science from Catania University, in 2008. He received his master degree (summa cum laude) in Computer Science from Catania University and nominated for "Archimede Prize" for academic excellence, in 2010. He received his Ph.D. in Computer Science, University of Catania, under the supervision of Prof. A. Pulvirenti and Prof. A. Ferro.
He obtained a FIRC fellowship as guest researcher at the Ohio State University, Molecular Virology, Immunology and Medical Genetics.
Supervisors: Carlo M. Croce
He obtained a FIRC fellowship as guest researcher at the Ohio State University, Molecular Virology, Immunology and Medical Genetics.
Supervisors: Carlo M. Croce
less
InterestsView All (7)
Uploads
Papers
only a small fraction of human DNA encodes for proteins, as reported by the ENCODE
project. Several distinct classes of ncRNAs, such as transfer RNA, microRNA, and long
non-coding RNA, have been classified, each with its own three-dimensional folding and
specific function. As ncRNAs are highly abundant in living organisms and have been
discovered to play important roles in many biological processes, there has been an ever
increasing need to investigate the entire ncRNAome in further unbiased detail. Recently,
the advent of next-generation sequencing (NGS) technologies has substantially increased
the throughput of transcriptome studies, allowing an unprecedented investigation of
ncRNAs, as regulatory pathways and novel functions involving ncRNAs are now also
emerging. The huge amount of transcript data produced by NGS has progressively
required the development and implementation of suitable bioinformatics workflows,
complemented by knowledge-based approaches, to identify, classify, and evaluate the
expression of hundreds of ncRNAs in normal and pathological conditions, such as
cancer. In this mini-review, we present and discuss current bioinformatics advances in
the development of such computational approaches to analyze and classify the ncRNA
component of human transcriptome sequence data obtained from NGS technologies.
In this work we summarized the major computational aspects on predicting and understanding RNA editing events. We also investigate the detection of short motifs sequences characterizing RNA editing signals and the use of a logistic regression technique to model a predictor of editing site events. The last, called AIRlINER, is available as a web app at: http://alpha.dmi.unict.it/airliner/. Results and comparisons with the existing methods encourage our findings on both aspects.
only a small fraction of human DNA encodes for proteins, as reported by the ENCODE
project. Several distinct classes of ncRNAs, such as transfer RNA, microRNA, and long
non-coding RNA, have been classified, each with its own three-dimensional folding and
specific function. As ncRNAs are highly abundant in living organisms and have been
discovered to play important roles in many biological processes, there has been an ever
increasing need to investigate the entire ncRNAome in further unbiased detail. Recently,
the advent of next-generation sequencing (NGS) technologies has substantially increased
the throughput of transcriptome studies, allowing an unprecedented investigation of
ncRNAs, as regulatory pathways and novel functions involving ncRNAs are now also
emerging. The huge amount of transcript data produced by NGS has progressively
required the development and implementation of suitable bioinformatics workflows,
complemented by knowledge-based approaches, to identify, classify, and evaluate the
expression of hundreds of ncRNAs in normal and pathological conditions, such as
cancer. In this mini-review, we present and discuss current bioinformatics advances in
the development of such computational approaches to analyze and classify the ncRNA
component of human transcriptome sequence data obtained from NGS technologies.
In this work we summarized the major computational aspects on predicting and understanding RNA editing events. We also investigate the detection of short motifs sequences characterizing RNA editing signals and the use of a logistic regression technique to model a predictor of editing site events. The last, called AIRlINER, is available as a web app at: http://alpha.dmi.unict.it/airliner/. Results and comparisons with the existing methods encourage our findings on both aspects.