scispace - formally typeset
S

Shaun Mahony

Researcher at Pennsylvania State University

Publications -  92
Citations -  5172

Shaun Mahony is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Transcription factor & Chromatin. The author has an hindex of 32, co-authored 85 publications receiving 4397 citations. Previous affiliations of Shaun Mahony include Brigham and Women's Hospital & University of Pittsburgh.

Papers
More filters
Journal ArticleDOI

Genome of the marsupial Monodelphis domestica reveals innovation in non-coding sequences

Tarjei S. Mikkelsen, +238 more
- 10 May 2007 - 
TL;DR: A high-quality draft of the genome sequence of the grey, short-tailed opossum is reported, indicating a strong influence of biased gene conversion on nucleotide sequence composition, and a relationship between chromosomal characteristics and X chromosome inactivation.
Journal ArticleDOI

STAMP: a web tool for exploring DNA-binding motif similarities

TL;DR: STAMP is a newly developed web server that is designed to support the study of DNA-binding motifs, and is a highly flexible alignment platform, allowing users to ‘mix-and-match’ between various implemented comparison metrics, alignment methods, multiple alignment strategies and tree-building methods.
Journal ArticleDOI

The Pioneer Transcription Factor FoxA Maintains an Accessible Nucleosome Configuration at Enhancers for Tissue-Specific Gene Activation.

TL;DR: It is found that MNase-accessible nucleosomes, bound by transcription factors, are retained more at liver-specific enhancers than at promoters and ubiquitous enhancers, suggesting nucleosome are not exclusively repressive to gene regulation when they are retained with, and exposed by, pioneer factors.
Journal ArticleDOI

High Resolution Genome Wide Binding Event Finding and Motif Discovery Reveals Transcription Factor Spatial Binding Constraints

TL;DR: In this paper, a new integrative computational method, genome wide event finding and motif discovery (GEM), was developed to discover novel transcription factor spatial binding constraints in vivo. But the method is not suitable for the analysis of large-scale data sets.