A novel k-mer set memory (KSM) motif representation improves regulatory variant prediction
The representation and discovery of transcription factor (TF) sequence binding specificities is critical for understanding gene regulatory networks and interpreting the impact of disease-associated noncoding genetic variants. We present a novel TF binding motif representation, the k-mer set memory (...
Main Authors: | Guo, Yuchun, Tian, Kevin J., Zeng, Haoyang, Guo, Xiaoyun, Gifford, David K |
---|---|
Other Authors: | Massachusetts Institute of Technology. Computational and Systems Biology Program |
Format: | Article |
Published: |
Cold Spring Harbor Laboratory
2018
|
Online Access: | http://hdl.handle.net/1721.1/119653 https://orcid.org/0000-0003-2357-1546 https://orcid.org/0000-0003-1057-2865 https://orcid.org/0000-0003-1709-4034 |
Similar Items
-
Simultaneous computational discovery of DNA regulatory motifs and transcription factor binding constraints at high spatial resolution
by: Guo, Yuchun
Published: (2013) -
Predicting the impact of non-coding variants on DNA methylation
by: Zeng, Haoyang, et al.
Published: (2017) -
Classification Of Cpg Island And Promoter Regions Using Rare K-Mer Motifs
by: Mohamed Hashim, Ezzeddin Kamil
Published: (2015) -
Whole genome regulatory variant evaluation for transcription factor binding
by: Zeng, Haoyang, Ph.D. Massachusetts Institute of Technology
Published: (2015) -
High Resolution Genome Wide Binding Event Finding and Motif Discovery Reveals Transcription Factor Spatial Binding Constraints
by: Guo, Yuchun, et al.
Published: (2012)