MAIN

 


1996-2019 All Rights Reserved. Online Journal of Bioinformatics. You may not store these pages in any form except for your own personal use. All other usage or distribution is illegal under international copyright treaties. Permission to use any of these pages in any other way besides the  before mentioned must be gained in writing from the publisher. This article is exclusively copyrighted in its entirety to OJB publications. This article may be copied once but may not be, reproduced or  re-transmitted without the express permission of the editors. This journal satisfies the refereeing requirements (DEST) for the Higher Education Research Data Collection (Australia). Linking:To link to this page or any pages linking to this page you must link directly to this page only here rather than put up your own page.


OJBTM

Online Journal of Bioinformatics

 

Volume 8 (2):139-153, 2007.


Shape-to-String Mapping: A Novel Approach To Clustering Time-Index Biomics Data

 

Antoine W1, Miernyk JA1,2,3

 

1Department of Biochemistry 2USDA, Agricultural Research Service, Plant Genetics Research Unit and 3Interdisciplinary Plant Group, University of Missouri, Columbia, USA

 

ABSTRACT

 

Antoine W, Miernyk JA, Shape-to-String Mapping: A Novel Approach To Clustering Time-Index Biomics Data, Onl J Bioinform., 8 (2):139-153, 2007. Herein we describe a qualitative approach for clustering time-index biomics data. The data are transformed into angles from the intensity-ratios between adjacent time-points.  A code is used to map a qualitative representation of the numerical time-index data which captures the features in the data that define the shape of the pattern expression as a function of time.  The problem of clustering time-index biomics data is then either solved directly or reduced to a problem similar to the well-studied task of clustering protein sequence data.  For datasets with few time points, the words derived from the transformation are adequate to define clusters.  Dissimilarities between the newly defined objects can be estimated, and the distance matrix can be used for further clustering.  The results from transcript profiling of developing soybean embryo have been used to illustrate the utility of the method.  Comparative mapping of the intensity-ratios and the angles by multidimensional scaling and Procrustes analysis revealed otherwise cryptic information within the data.  The Euclidian distance matrices were calculated from the words and corresponding gene list using the PHYLogeny Inference Package (PHYLIP) algorithms and the Point of Accepted Mutation (PAM) scores matrix to compare the effectiveness of the code in clustering the data.

 

Key words:  String Map, Cluster, Biomics


MAIN

 

FULL-TEXT (SUBSCRIPTION OR PURCHASE TITLE $25USD)