Timothy L. Bailey

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The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein se quences by using the technique of expectation maxi mization to t a two component nite mixture model to the set of sequences Multiple motifs are found by tting a mixture model to the data probabilistically erasing the occurrences of the motif thus found(More)
The MEME Suite web server provides a unified portal for online discovery and analysis of sequence motifs representing features such as DNA binding sites and protein interaction domains. The popular MEME motif discovery algorithm is now complemented by the GLAM2 algorithm which allows discovery of motifs containing gaps. Three sequence scanning(More)
MEME (Multiple EM for Motif Elicitation) is one of the most widely used tools for searching for novel 'signals' in sets of biological sequences. Applications include the discovery of new transcription factor binding sites and protein domains. MEME works by searching for repeated, ungapped sequence patterns that occur in the DNA or protein sequences provided(More)
The MEME algorithm extends the expectation maximization (EM) algorithm for identifying motifs in unaligned biopolymer sequences. The aim of MEME is to discover new motifs in a set of biopolymer sequences where little or nothing is known in advance about any motifs that may be present. MEME innovations expand the range of problems which can be solved using(More)
UNLABELLED A motif is a short DNA or protein sequence that contributes to the biological function of the sequence in which it resides. Over the past several decades, many computational methods have been described for identifying, characterizing and searching with sequence motifs. Critical to nearly any motif-based sequence analysis pipeline is the ability(More)
MOTIVATION To illustrate an intuitive and statistically valid method for combining independent sources of evidence that yields a p-value for the complete evidence, and to apply it to the problem of detecting simultaneous matches to multiple patterns in sequence homology searches. RESULTS In sequence analysis, two or more (approximately) independent(More)
The prediction of regulatory elements is a problem where computational methods offer great hope. Over the past few years, numerous tools have become available for this task. The purpose of the current assessment is twofold: to provide some guidance to users regarding the accuracy of currently available tools in various settings, and to provide a benchmark(More)
MEME is a tool for discovering motifs in sets of protein or DNA sequences. This paper describes several extensions to MEME which increase its ability to find motifs in a totally unsupervised fashion, but which also allow it to benefit when prior knowledge is available. When no background knowledge is asserted. MEME obtains increased robustness from a method(More)
A common question within the context of de novo motif discovery is whether a newly discovered, putative motif resembles any previously discovered motif in an existing database. To answer this question, we define a statistical measure of motif-motif similarity, and we describe an algorithm, called Tomtom, for searching a database of motifs with a given query(More)