SMS scnews item created by Shila Ghazanfar at Mon 5 Mar 2018 1413
Type: Seminar
Distribution: World
Expiry: 20 Mar 2018
Calendar1: 19 Mar 2018 1300-1400
CalLoc1: CPC Level 3 Large Meeting Room
Auth: sheilag@psheilag2.pc (assumed)

Statistical Bioinformatics Seminar: Pascovici -- DIA/SWATH - challenges and opportunities for bioinformatics

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 Mondays at the Charles Perkins Centre, 
Seminar Room (Level 3, large meeting room). Seminars in 2018 will begin in March. 
The format of the talk is 30~45 minutes plus questions.

Monday March 19, 2018

Speaker: Dana Pascovici (Macquarie University)

Title: DIA/SWATH - challenges and opportunities for bioinformatics

Abstract: Protein quantitation using DIA/SWATH mass spectrometry has been growing 
in popularity over the last few years. From the point of view of the bioinformatics 
involved, on one hand the data resulting from such experiments is quite easy to 
analyse at least if the experiment is not too large, due to a much lower percentage 
of missing data, and data look and distribution that makes existing methodology 
from other areas quite easily applicable. Put plainly, extracted SWATH data is 
quite nice to work with. However, that is because much of the difficulty has been 
pushed underneath, at the level of the SWATH library building and data extraction, 
where it is somewhat hidden from view. 

In this talk we will describe SWATH and its place in the landscape of quantitative 
proteomics (including broad comparisons with label free and labelled techniques such 
as iTRAQ and TMT), and the many positive aspects of the resulting SWATH datasets, 
from the point of view of the data analyst. We will also focus on how SWATH data 
extraction usually relies on using high quality peptide MS/MS spectral libraries, 
however building such libraries to ensure good proteome coverage can be time 
consuming and expensive. In order to address this issue various computational 
approaches for merging archived or external libraries were created and 
evaluated, including efforts from our group. We will describe the appeal of such 
methods, the possible issues that can ensue and some approaches to tackle them in 
order to ensure that the proteins are reliably detected and their quantitation is 
consistent and reproducible. We will discuss these aspects in the context of 
several existing datasets, including a carefully designed spiked-in experiment, 
and a recently published large plasma proteomics experiment containing samples 
from neonates, young children and adults.

About the speaker: 
I am currently a Biostatistician at the Australian Proteome Analysis 
Facility at Macquarie University, where I help people generate biological 
insights out of their proteomics data, especially in the context of 
complex experiments. 

Working in a proteomics facility, our focus has been on generating 
reliable methods of interpreting and analysing data from a variety of 
platforms, lately emphasizing SWATH and TMT, and wherever possible 
incorporating them into software workflows.  Areas of particular relevance 
to us have been plasma proteomics, and plant proteomics of agriculturally 
important species.  Our work has benefitted from interactions with 
researchers, students and the APAF team of mass spectrometry specialists 
and analytical chemists. 

I come from a mathematical and computational background, having completed 
a bachelor degree in Mathematics and Computer Science at Dartmouth College 
in the US, followed by a PhD in Mathematics at MIT, and a brief stint of 
teaching at Purdue.  In Sydney I took a more practical turn and worked in 
the industry in the area of speech recognition, before settling into 
biostatistics for the past 13 years, both in the industry and research 

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