SMS scnews item created by Shila Ghazanfar at Wed 22 Nov 2017 0807
Type: Seminar
Distribution: World
Expiry: 28 Nov 2017
Calendar1: 27 Nov 2017 1300-1400
CalLoc1: CPC Seminar Room Level 3
Auth: sheilag@psheilag2.pc (assumed)

Statistical Bioinformatics Seminar: Signal -- Machine learning annotation of branchpoints and in silico modelling of functional splicing events

The aim of the statistical bioinformatics seminar is to provide a forum for
people working within the broad area of computation and statistics and their
application to various aspects of biology to present their work and showcase
their ongoing projects. It is intended to foster the exchange of ideas and
build potential collaborations across multiple disciplines.

The seminars will be held at 1:00 pm on Monday in Charles Perkins Centre
Seminar Room (Level 3, large meeting room). The format of the talk is 30~45
minutes plus questions.

Speaker: Beth Signal (Garvan Institute of Medical Research)

Title: Machine learning annotation of branchpoints and in silico modelling
of functional splicing events.

Abstract:
RNA splicing is a key component of mature RNA transcript formation, required for the
removal of intronic regions and subsequent ligation of exonic regions. This process
can also allow for alternative splicing to occur, where different exonic regions are
ligated together to produce alternative RNA products.

The branchpoint element is one of the splicing sequence elements, required for the
first lariat-forming reaction in splicing. However current catalogues of human
branchpoints remain incomplete due to the difficulty in experimentally identifying
these elements. To address this limitation, we have developed a machine-learning
algorithm - branchpointer - to identify branchpoint elements solely from gene
annotations and genomic sequence. Using branchpointer, we annotate branchpoint
elements in 85% of human gene introns with sensitivity (61.8%) and specificity
(97.8%). In addition to annotation, branchpointer can evaluate the impact of
SNPs on branchpoint architecture to inform functional interpretation of genetic
variants. Branchpointer identifies all published deleterious branchpoint mutations
annotated in clinical variant databases, and finds thousands of additional clinical
and common genetic variants with similar predicted effects.

While alternative splicing can produce alternative RNA products, a large
proportion of these have little functional impact on open reading frames or
transcript stability. To address this limitation in the functional interpretation
of differential splicing analyses, we have developed software to model events in
silico and interpret their functional impact.

About the speaker: Beth is a PhD Student in the Clinical Genome Informatics group
at the Garvan Institute. Her current research is focused on developing
bioinformatics methods to understand how transcript splicing and expression is
controlled. She has a particular interest in using machine learning techniques
to study transcriptomic behaviour.


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