SNIC
SUPR
SNIC SUPR
Elsasserlab
Dnr:

SNIC 2017/1-508

Type:

SNAC Medium

Principal Investigator:

Simon Elsässer

Affiliation:

Karolinska Institutet

Start Date:

2017-12-07

End Date:

2019-01-01

Primary Classification:

10610: Bioinformatics and Systems Biology (methods development to be 10203)

Allocation

Abstract

We use Uppmax extensively for genomics NGS data produced on our own NextSeq 500. The allocation is for mutiple projects in the lab: 1) MINUTE-ChIP - a pipeline developed in collaboration with WABI for analyzing our own multiplexed ChIP method MINUTE-ChIP. We have adapted and improved a protocol for multiplexed barcoded ChIP-Seq from Berstein lab (http://dx.doi.org/10.1016/j.molcel.2015.11.003) and are using it to study quantitative chromatin landscapes. Two main applications are currently active, and further collaborations are planned: 1a) RAPID - a method for studying the dynamics of histones and chromatin-associated factors using pulsed labeling as an additional dimension in a multiplexed ChIP-Seq experiment. (ERC funded, Manfred Grabherr, Philip Yung, Banushree Kumar, Angelo Salazar) 1b) Epigenomic instability induced by G4 quadruplets and elucidation of factors that prevent epigenetic instability. Uses MINUTE-ChIP and Unusual DNA structure mapping, using methods to sequence single stranded DNA, double strand breaks, G4 quadruplex structures (Cancerfonden, Jing Lyu) 1d) Cell cycle-dependent chromatin landscape (Philip Yung) 1c) Glioma stem cell chromatin landscape (Anna-Maria Katsori) 2) Transient transcriptomics (TT-Seq). Transient transcriptomes are profiled in a collaboration between Elsässer Lab (KI/SciLifeLab) and Cramer Lab (KI), using a novel method TT-Seq (Science, 2016). Data will be generated on NextSeq 500 and HiSeq 1000 instruments and analyzed with custom short read mapping pipeline. Aim is to provide first genome-wide transient transcript annotation for mouse ESC, and to perform functional genomics modulating chromatin and transcription factors. (Rui Shao) All the projects rely on processing of our own data together and meta analysis of available datasets. This is a combined project to continue b2015157, b2017078, b2017079