Stochastic EM-based TFBS motif discovery with MITSU
نویسندگان
چکیده
منابع مشابه
Stochastic EM-based TFBS motif discovery with MITSU
MOTIVATION The Expectation-Maximization (EM) algorithm has been successfully applied to the problem of transcription factor binding site (TFBS) motif discovery and underlies the most widely used motif discovery algorithms. In the wider field of probabilistic modelling, the stochastic EM (sEM) algorithm has been used to overcome some of the limitations of the EM algorithm; however, the applicati...
متن کاملEXTREME: an online EM algorithm for motif discovery
MOTIVATION Identifying regulatory elements is a fundamental problem in the field of gene transcription. Motif discovery-the task of identifying the sequence preference of transcription factor proteins, which bind to these elements-is an important step in this challenge. MEME is a popular motif discovery algorithm. Unfortunately, MEME's running time scales poorly with the size of the dataset. Ex...
متن کاملEigenvector-based Relational Motif Discovery
The development of novel analytical tools to investigate the structure of music works is central in current music information retrieval research. In particular, music summarization aims at finding the most representative parts of a music piece (motifs) that can be exploited for an efficient music database indexing system. Here we present a novel approach for motif discovery in music pieces base...
متن کاملMotif Tool Manager: a web-based framework for motif discovery
MOTIVATION Motif Tool Manager is a web-based framework for comparing and combining different approaches to discover novel DNA motifs. It comes with a set of five well-known approaches to motif discovery. It provides an easy mechanism for adding new motif finding tools to the framework through a web-interface and a minimal setup of the tools on the server. Users can execute the tools through the...
متن کاملNeighbourhood Thresholding for Projection-Based Motif Discovery
The PROJECTION algorithm by Buhler and Tompa is one of the best existing methods for solving hard motif discovery problems for monad motifs of fixed length l. In this paper we introduce the AGGREGATION algorithm, which like PROJECTION projects all l-mers from the given input sequences into buckets, but uses a different scheme for selecting buckets for subsequent refinement search. This new neig...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bioinformatics
سال: 2014
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/btu286