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Timothy L. Bailey

Researcher at University of Nevada, Reno

Publications -  165
Citations -  42616

Timothy L. Bailey is an academic researcher from University of Nevada, Reno. The author has contributed to research in topics: Sequence motif & Internal medicine. The author has an hindex of 59, co-authored 147 publications receiving 35797 citations. Previous affiliations of Timothy L. Bailey include University of Washington & University of California, San Diego.

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Journal ArticleDOI

MEME Suite: tools for motif discovery and searching

TL;DR: The popular MEME motif discovery algorithm is now complemented by the GLAM2 algorithm which allows discovery of motifs containing gaps, and all of the motif-based tools are now implemented as web services via Opal.
Proceedings Article

Fitting a mixture model by expectation maximization to discover motifs in biopolymers.

TL;DR: The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expectation maximization to fit a two-component finite mixture model to the set of sequences.
Journal ArticleDOI

The Transcriptional Landscape of the Mammalian Genome

Piero Carninci, +197 more
- 02 Sep 2005 - 
TL;DR: Detailed polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.
Journal ArticleDOI

FIMO: scanning for occurrences of a given motif.

TL;DR: Find Individual Motif Occurrences (FIMO), a software tool for scanning DNA or protein sequences with motifs described as position-specific scoring matrices, and provides output in a variety of formats, including HTML, XML and several Santa Cruz Genome Browser formats.
Journal ArticleDOI

MEME: discovering and analyzing DNA and protein sequence motifs

TL;DR: The freely accessible web server and its architecture are described, and ways to use MEME effectively to find new sequence patterns in biological sequences and analyze their significance are discussed.