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Details of talk

TitleMultiscale approaches for analyses of high-throughput sequencing data
PresenterHeejung Shim (The University of Melbourne)
Author(s)Heejung Shim
SessionBiostatistics and Bioinformatics
Time13:00:00 2017-09-26

Identification of differences between multiple groups in cellular phenotypes
measured by high-throughput sequencing assays is frequently encountered in
genomics applications. For example, common problems include detecting
differences in transcription factor binding/chromatin accessibility across
tissues/conditions using ChIP-seq/ATAC-seq data. These high-throughput
sequencing data provide high resolution measurements on how traits vary along
the whole genome in each sample. However, typical analyses fail to exploit the
full potential of these high resolution measurements, instead aggregating the
data at coarser resolutions, such as genes, or windows of fixed length.
Previously, we developed a wavelet-based (normal-based) multi-scale method,
WaveQTL, that better exploits the high-resolution information, and demonstrated
that WaveQTL has more power than a simpler window-based method. Motivated by
this, we developed another multi-scale method, multiseq, that models the count
nature of the sequencing data directly, making the method potentially perform
well at small sample sizes or for low read counts. In this talk, first I will
present key ideas behind multi-scale approaches for analyses of high-throughput
sequencing data. Then, I will introduce the second method, multiseq, and
demonstrate that multiseq has better power than negative binomial based window
methods. Finally, I will discuss how multi-scale models can be used in
applications to other biological questions.