scholarly journals Metagenome fragment classification based on multiple motif-occurrence profiles

PeerJ ◽  
2014 ◽  
Vol 2 ◽  
pp. e559 ◽  
Author(s):  
Naoki Matsushita ◽  
Shigeto Seno ◽  
Yoichi Takenaka ◽  
Hideo Matsuda
Keyword(s):  
Motif Occurrence ◽  
Multiple Motif

scholarly journals Greedy mixture learning for multiple motif discovery in biological sequences

2003 ◽  
Vol 19 (5) ◽  
pp. 607-617 ◽  
Author(s):  
K. Blekas ◽  
D. I. Fotiadis ◽  
A. Likas
Keyword(s):  
Motif Discovery ◽  
Biological Sequences ◽  
Multiple Motif

scholarly journals Texture Fusion for Batik Motif Retrieval System

2016 ◽  
Vol 6 (6) ◽  
pp. 3174 ◽  
Author(s):  
Ida Nurhaida ◽  
Hong Wei ◽  
Remmy A. M. Zen ◽  
Ruli Manurung ◽  
Aniati M. Arymurthy
Keyword(s):  
Feature Fusion ◽  
Texture Features ◽  
Feature Space ◽  
Image Texture ◽  
Query Image ◽  
Gabor Features ◽  
Multiple Motif ◽  
Individual Features ◽  
The Individual ◽  
Log Gabor

<p>This paper systematically investigates the effect of image texture features on batik motif retrieval performance. The retrieval process uses a query motif image to find matching motif images in a database. In this study, feature fusion of various image texture features such as Gabor, Log-Gabor, Grey Level Co-Occurrence Matrices (GLCM), and Local Binary Pattern (LBP) features are attempted in motif image retrieval. With regards to performance evaluation, both individual features and fused feature sets are applied. Experimental results show that optimal feature fusion outperforms individual features in batik motif retrieval. Among the individual features tested, Log-Gabor features provide the best result. The proposed approach is best used in a scenario where a query image containing multiple basic motif objects is applied to a dataset in which retrieved images also contain multiple motif objects. The retrieval rate achieves 84.54% for the rank 3 precision when the feature space is fused with Gabor, GLCM and Log-Gabor features. The investigation also shows that the proposed method does not work well for a retrieval scenario where the query image contains multiple basic motif objects being applied to a dataset in which the retrieved images only contain one basic motif object.</p>

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.

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