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Identifying DNA Sequence Motifs of Pdx-1 and NeuroD1 Transcription Factors

9 pagesPublished: March 18, 2019

Abstract

Diabetes is a disease reported to be the 8th leading cause of death across the world. Nearly 38 million people worldwide have Type I diabetes caused by a dysfunction of beta cells that impairs insulin production. A better understanding of mechanisms related to gene expression in beta cells might help in the development of novel strategies for the effective treatment of diabetes. Two known transcription factors, Pdx-1 and NeuroD1, are shown to regulate gene expression in beta cells. Recently gene targets that are regulated by both Pdx-1 and NeuroD1 have been identified experimentally [7]. However, the motifs for this set of genes have not been found yet. Here we undertake the task of finding statistically overrepresented motifs in genes regulated by Pdx-1 and NeuroD1. The challenge of this project is to identify statistically significant pairs of motifs: one motif of each pair is for Pdx-1 and the other for NeuroD1. Commonly known motif-finding methods are usually restricted to finding a set of potential candidates, each of which is a single motif.

Keyphrases: motif discovery, pattern search, sequence analysis

In: Oliver Eulenstein, Hisham Al-Mubaid and Qin Ding (editors). Proceedings of 11th International Conference on Bioinformatics and Computational Biology, vol 60, pages 27-35.

BibTeX entry
@inproceedings{BiCOB2019:Identifying_DNA_Sequence_Motifs,
  author    = {Hassan Aldarwish and David Keller and Elena Harris},
  title     = {Identifying DNA Sequence Motifs of Pdx-1 and NeuroD1 Transcription Factors},
  booktitle = {Proceedings of 11th International Conference on Bioinformatics and Computational Biology},
  editor    = {Oliver Eulenstein and Hisham Al-Mubaid and Qin Ding},
  series    = {EPiC Series in Computing},
  volume    = {60},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/W7XF},
  doi       = {10.29007/sfxr},
  pages     = {27-35},
  year      = {2019}}
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