Improved benchmarks for computational motif discovery
نویسندگان
چکیده
منابع مشابه
Introduction to Computational Biology Lecture # 10: Motif Discovery
In the previous lessons we learned about a probabilistic model which describes biological sequences Hidden Markov Model (HMM).We have also learnt how to evaluate the parameters of a specific HMMby an expectation-maximization algorithm (EM). In this lesson we will use these methods to solve the specific problem of motif discovery. Our goal is to find a word that appears in a non-conserved place ...
متن کاملDevelopment of an Efficient Hybrid Method for Motif Discovery in DNA Sequences
This work presents a hybrid method for motif discovery in DNA sequences. The proposed method called SPSO-Lk, borrows the concept of Chebyshev polynomials and uses the stochastic local search to improve the performance of the basic PSO algorithm as a motif finder. The Chebyshev polynomial concept encourages us to use a linear combination of previously discovered velocities beyond that proposed b...
متن کاملFoldMiner: structural motif discovery using an improved superposition algorithm.
We report an unsupervised structural motif discovery algorithm, FoldMiner, which is able to detect global and local motifs in a database of proteins without the need for multiple structure or sequence alignments and without relying on prior classification of proteins into families. Motifs, which are discovered from pairwise superpositions of a query structure to a database of targets, are descr...
متن کاملNegative Information for Motif Discovery
We discuss a method of combining genome-wide transcription factor binding data, gene expression data, and genome sequence data for the purpose of motif discovery in S. cerevisiae. Within the word-counting algorithmic approach to motif discovery, we present a method of incorporating information from negative intergenic regions where a transcription factor is thought not to bind, and a statistica...
متن کاملBiological sequence motif discovery using motif-x.
The Web-based motif-x program provides a simple interface to extract statistically significant motifs from large data sets, such as MS/MS post-translational modification data and groups of proteins that share a common biological function. Users upload data files and download results using common Web browsers on essentially any Web-compatible computer. Once submitted, data analyses are performed...
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: BMC Bioinformatics
سال: 2007
ISSN: 1471-2105
DOI: 10.1186/1471-2105-8-193