Category CBIO L09 Measuring Expression for Novel Variations in Enhancers using

STARR-seq Data

Abstract Enhancers, although not translated, help give rise to the variety of

unique cell types discoverable throughout the body. Enhancers

function through elevating transcription in both the forward and reverse

orientations in conjunction with transcription factors. Single Nucleotide

Polymorphisms (SNPs) have been statistically linked through Genome

Wide Association Studies (GWAS) to Type 2 Diabetes (T2D), although

the effect of other allelic differences surrounding such risk alleles has

yet to be investigated. As such, data gathered from a modified STARR-

seq assay will be used to determine the allelic effects of modifications

around a known risk SNP within +/- 10 base pairs of the risk allele to

better understand the specific differences in transcription caused by

personal mutations around risk SNPs. Specifically, count data from the

assay will be used to detect subtle differences in transcription by

counting the number of transcripts generated by each enhancer within

the input library. After initial QC, the creation of position weight matrices

will help highlight the allelic binding preferences of transcription factors

to improve the understanding of the significance of specific base pairs

in TF binding. Further statistical analyses will then adjust the counts of

transcripts to account for random variations and more accurately reflect

the transcription levels induced by the enhancers. The project will

advance understanding of genomic regulation by helping explore the

binding preferences of transcription factors through the analysis of

transcript count data and statistical association of transcription levels

with allelic differences within the count data.

Bibliography Buenrostro, Jason D., et al. "Transposition of native chromatin for fast

and sensitive epigenomic profiling of open chromatin, DNA-binding

proteins and nucleosome position." Nature Methods, vol. 10, no. 12,

2013, doi:10.1038/nmeth.2770. Accessed 17 January 2017.

Raj, Anil, and Graham McVicker. "The Genome Shows Its Sensitive

Side." Nature Methods, vol. 11, no. 1, Jan. 2014,

doi:10.1038/nmeth.2770. Accessed 17 January 2017.
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