Paper Info
Title
CLU: a new algorithm for EST clustering.
Abstract
The continuous flow of EST data remains one of the richest sources for discoveries in modern biology. The first step in EST data mining is usually associated with EST clustering, the process of grouping of original fragments according to their annotation, similarity to known genomic DNA or each other. Clustered EST data, accumulated in databases such as UniGene, STACK and TIGR Gene Indices have proven to be crucial in research areas from gene discovery to regulation of gene expression.We have developed a new nucleotide sequence matching algorithm and its implementation for clustering EST sequences. The program is based on the original CLU match detection algorithm, which has improved performance over the widely used d2_cluster. The CLU algorithm automatically ignores low-complexity regions like poly-tracts and short tandem repeats.CLU represents a new generation of EST clustering algorithm with improved performance over current approaches. An early implementation can be applied in small and medium-size projects. The CLU program is available on an open source basis free of charge. It can be downloaded from http://compbio.pbrc.edu/pti.
Year
DOI
Venue
2005
10.1186/1471-2105-6-S2-S3
BMC Bioinformatics
Keywords
Field
DocType
data mining,short tandem repeat,genomic dna,cluster analysis,bioinformatics,algorithms,expressed sequence tags,nucleotide sequence,microarrays,regulation of gene expression
Annotation,Expressed sequence tag,Biology,Regulation of gene expression,UniGene,Cancer Genome Anatomy Project,Bioinformatics,Cluster analysis,Genetics,genomic DNA,DNA microarray
Journal
Volume
Issue
ISSN
6 Suppl 2
S-2
1471-2105
Citations 
PageRank 
References 
41
0.97
5
Authors
2
Name
Order
Citations
PageRank
Andrey A Ptitsyn11619.32
Winston Hide214017.36