Abstract
BackgroundExpressed sequence tags (ESTs) are single pass reads from randomly selected cDNA clones. They provide a highly cost-effective method to access and identify expressed genes. However, they are often prone to sequencing errors and typically define incomplete transcripts. To increase the amount of information obtainable from ESTs and reduce sequencing errors, it is necessary to cluster ESTs into groups sharing significant sequence similarity.ResultsAs part of our ongoing EST programs investigating 'orphan' genomes, we have developed a clustering algorithm, CLOBB (Cluster on the basis of BLAST similarity) to identify and cluster ESTs. CLOBB may be used incrementally, preserving original cluster designations. It tracks cluster-specific events such as merging, identifies 'superclusters' of related clusters and avoids the expansion of chimeric clusters. Based on the Perl scripting language, CLOBB is highly portable relying only on a local installation of NCBI's freely available BLAST executable and can be usefully applied to > 95 % of the current EST datasets. Analysis of the Danio rerio EST dataset demonstrates that CLOBB compares favourably with two less portable systems, UniGene and TIGR Gene Indices.ConclusionsCLOBB provides a highly portable EST clustering solution and is freely downloaded from: http://www.nematodes.org/CLOBB
Highlights
Expressed sequence tags (ESTs) are single pass reads from randomly selected cDNA clones
Expressed sequence tags (EST) are single pass sequence reads from randomly selected cDNA clones that sample the diversity of genes expressed by an organism [1]
To maximise the information derived from these ESTs, for each species of nematode we study a gene index based on the ESTs must be generated
Summary
Expressed sequence tags (ESTs) are single pass reads from randomly selected cDNA clones. They provide a highly cost-effective method to access and identify expressed genes. They are often prone to sequencing errors and typically define incomplete transcripts. Expressed sequence tags (EST) are single pass sequence reads from randomly selected cDNA clones that sample the diversity of genes expressed by an organism [1]. For organisms where whole genome sequencing is a distant goal, EST analysis is a highly cost-effective gene discovery method. Unlike whole genome sequencing, where multiple sequencing of each segment is the norm, ESTs are single pass reads of unverified quality that may contain base-calling and other errors. Analysis of EST datasets can be overwhelming due to the sheer number of sequences involved
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