Predicting Target DNA Sequences of DNA-Binding Proteins Based on Unbound Structures

Predicting Target DNA Sequences of DNA-Binding Proteins Based on Unbound Structures

DNA-binding proteins such as transcription factors use DNA-binding domains (DBDs) to bind to specific sequences in the genome to initiate many important biological functions. Accurate prediction of such target sequences, often represented by position weight matrices (PWMs), is an important step to understand many biological processes. Recent studies have shown that knowledge-based potential fun...

Kernel-based machine learning protocol for predicting DNA-binding proteins

DNA-binding proteins (DNA-BPs) play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Attempts have been made to identify DNA-BPs based on their sequence and structural information with moderate accuracy. Here we develop a machine learning protocol for the prediction of DNA-BPs where the classifier is Support Vector Machines ...

DNA Target Sequences

BCRANK: predicting binding site consensus from ranked DNA sequences

This document describes the BCRANK R package. BCRANK[1] is a method that takes a ranked list of genomic regions as input and outputs short DNA sequences that are overrepresented in some part of the list. The algorithm was developed for detecting transcription factor (TF) binding sites in a large number of enriched regions from high-throughput ChIP-chip or ChIP-seq experiments, but it can be app...

Predicting demographic group structures based on DNA sequence data.

The ability to infer relationships between groups of sequences, either by searching for their evolutionary history or by comparing their sequence similarity, can be a crucial step in hypothesis testing. Interpreting relationships of human immunodeficiency virus type 1 (HIV-1) sequences can be challenging because of their rapidly evolving genomes, but it may also lead to a better understanding o...