scholarly journals Transcription factor and microRNA motif discovery: The Amadeus platform and a compendium of metazoan target sets

2008 ◽  
Vol 18 (7) ◽  
pp. 1180-1189 ◽  
Author(s):  
C. Linhart ◽  
Y. Halperin ◽  
R. Shamir
Keyword(s):  
Transcription Factor ◽  
Motif Discovery ◽  
Target Sets

scholarly journals MD-SVM: a novel SVM-based algorithm for the motif discovery of transcription factor binding sites

2019 ◽  
Vol 20 (S7) ◽  
Author(s):  
Jialu Hu ◽  
Jingru Wang ◽  
Jianan Lin ◽  
Tianwei Liu ◽  
Yuanke Zhong ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Motif Discovery ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Factor Binding

scholarly journals DREME: motif discovery in transcription factor ChIP-seq data

2011 ◽  
Vol 27 (12) ◽  
pp. 1653-1659 ◽  
Author(s):  
Timothy L. Bailey
Keyword(s):  
Transcription Factor ◽  
Motif Discovery

scholarly journals MARS: Motif Assessment and Ranking Suite for transcription factor binding motifs

2016 ◽  
Author(s):  
Caleb Kipkurui Kibet ◽  
Philip Machanick
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Motif Discovery ◽  
High Throughput Sequencing ◽  
Rank Correlation ◽  
Transcription Factor Binding ◽  
Factor Binding ◽  
Sequencing Technologies ◽  
Benchmark Database ◽  
On Chip

AbstractWe describe MARS (Motif Assessment and Ranking Suite), a web-based suite of tools used to evaluate and rank PWM-based motifs. The increased number of learned motif models that are spread across databases and in different PWM formats, leading to a choice dilemma among the users, is our motivation. This increase has been driven by the difficulty of modelling transcription factor binding sites and the advance in high-throughput sequencing technologies at a continually reducing cost. Therefore, several experimental techniques have been developed resulting in diverse motif-finding algorithms and databases. We collate a wide variety of available motifs into a benchmark database, including the corresponding experimental ChIP-seq and PBM data obtained from ENCODE and UniPROBE databases, respectively. The implemented tools include: a data-independent consistency-based motif assessment and ranking (CB-MAR), which is based on the idea that ‘correct motifs’ are more similar to each other while incorrect motifs will differ from each other; and a scoring and classification-based algorithms, which rank binding models by their ability to discriminate sequences known to contain binding sites from those without. The CB-MAR and scoring techniques have a 0.86 and 0.73 median rank correlation using ChIP-seq and PBM respectively. Best motifs selected by CB-MAR achieve a mean AUC of 0.75, comparable to those ranked by held out data at 0.76 – this is based on ChIP-seq motif discovery using five algorithms on 110 transcription factors. We have demonstrated the benefit of this web server in motif choice and ranking, as well as in motif discovery. It can be accessed at http://www.bioinf.ict.ru.ac.za/.

scholarly journals RSAT matrix-clustering: dynamic exploration and redundancy reduction of transcription factor binding motif collections

2016 ◽  
Author(s):  
Jaime Abraham Castro-Mondragon ◽  
Sébastien Jaeger ◽  
Denis Thieffry ◽  
Morgane Thomas-Chollier ◽  
Jacques van Helden
Keyword(s):  
Transcription Factor ◽  
Motif Discovery ◽  
Binding Motif ◽  
Data Sets ◽  
Transcription Factor Binding Motif ◽  
Biologically Relevant ◽  
Manual Curation ◽  
Versatile Tool ◽  
Multiple Motif ◽  
Multiple Trees

ABSTRACTTranscription Factor (TF) databases contain multitudes of motifs from various sources, from which non-redundant collections are derived by manual curation. The advent of high-throughput methods stimulated the production of novel collections with increasing numbers of motifs. Meta-databases, built by merging these collections, contain redundant versions, because available tools are not suited to automatically identify and explore biologically relevant clusters among thousands of motifs. Motif discovery from genome-scale data sets (e.g. ChIP-seq peaks) also produces redundant motifs, hampering the interpretation of results. We present matrix-clustering, a versatile tool that clusters similar TFBMs into multiple trees, and automatically creates non-redundant collections of motifs. A feature unique to matrix-clustering is its dynamic visualisation of aligned TFBMs, and its capability to simultaneously treat multiple collections from various sources. We demonstrate that matrix-clustering considerably simplifies the interpretation of combined results from multiple motif discovery tools and highlights biologically relevant variations of similar motifs. By clustering 24 entire databases (>7,500 motifs), we show that matrix-clustering correctly groups motifs belonging to the same TF families, and can drastically reduce motif redundancy. matrix-clustering is integrated within the RSAT suite (http://rsat.eu/), accessible through a user-friendly web interface or command-line for its integration in pipelines.

scholarly journals Bayesian multiple-instance motif discovery with BAMBI: inference of recombinase and transcription factor binding sites

2011 ◽  
Vol 39 (21) ◽  
pp. e146-e146 ◽  
Author(s):  
Guido H. Jajamovich ◽  
Xiaodong Wang ◽  
Adam P. Arkin ◽  
Michael S. Samoilov
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Motif Discovery ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Factor Binding
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