Seminar Details
2011-03-01 (15.00) : Analyzing the Whole Transcriptome by RNA-Seq data: Statistical and Computational Challenges
At Euler building
Organized by Mathematical Engineering
Speaker :
Claudia ANGELINI (Istituto per le Applicazioni del Calcolo "Mauro Picone", CNR)
Abstract :
The main goal of the whole transcriptome analyses is to identify, characterize and catalogue all the transcripts expressed within a
specific cell/tissue - at a particular stage - with the great potential to determine the correct splicing patterns and the precise structure of genes,
and to quantify the differential expression of transcripts in both physiological and pathological conditions. Until 2004, hybridization and tag-based
technologies, such as microarray, have allowed researchers to obtain intriguing insights into human genetics, even though microarray techniques suffer
from background cross-hybridization issues and a narrow detection range, and tag-based approaches require laborious time- and cost-effective steps
for the cloning of fragments prior sequencing. Recently, the introduction of massively parallel sequencing on Next Generation Sequencing (NGS) platforms
has completely revolutionized the way of thinking in molecular biology.
Among the different application of NGS platforms, RNA-Seq is probably one of the most complex of the various sequencing protocols developed so far.
RNA-Seq has clear advantages over the existing approaches. First, RNA-Seq is not limited to the detection of known transcripts, thus allowing the
identification, characterization and quantification of new splice isoforms. In addition, it permits researchers to determine the correct gene annotation,
also defining the transcriptional boundaries of genes. Other advantages of RNA-Seq are the low "background signal", the absence of an upper limit
for quantification and consequently, the larger dynamic range of expression levels over which transcripts can be detected. RNA-Seq data also show high
levels of reproducibility.
In this seminar we will illustrate some of the computational tools available for the analysis of RNA-Seq data and discuss the main challenges to face
when analyzing RNA-Seq experiments. In particular we will focus on 1) Identification and quantification of transcriptional regions. 2) Identification and
quantification of isoforms. 3) Detection of differential expressed events between two or more experimental conditions. 4) Connection between RNA-seq
and ChIP-seq data.