Database Commons

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ECgene provides annotation for gene structure, function and expression, taking alternative splicing events into consideration. The gene-modeling algorithm combines the genome-based expressed sequence tag (EST) clustering and graph-theoretic transcript assembly procedures. The website provides several viewers and applications that have many unique features useful for the analysis of the transcript structure and gene expression. The summary viewer shows the gene summary and the essence of other annotation programs. The genome browser and the transcript viewer are available for comparing the gene structure of splice variants. Changes in the functional domains by alternative splicing can be seen at a glance in the transcript viewer. We also provide two unique ways of analyzing gene expression. The SAGE tags deduced from the assembled transcripts are used to delineate quantitative expression patterns from SAGE libraries available publically. Furthermore, the cDNA libraries of EST sequences in each cluster are used to infer qualitative expression patterns. It should be noted that the ECgene website provides annotation for the whole transcriptome, not just the alternatively spliced genes. Currently, ECgene supports the human, mouse and rat genomes. The ECgene suite of tools and programs is available at http://genome.ewha.ac.kr/ECgene/.

With the availability of the human genome map and fast algorithms for sequence alignment, genome-based EST clustering became a viable method for gene modeling. We developed a novel gene-modeling method, ECgene (Gene modeling by EST Clustering), which combines genome-based EST clustering and the transcript assembly procedure in a coherent and consistent fashion. Specifically, ECgene takes alternative splicing events into consideration. The position of splice sites (i.e., exon-intron boundaries) in the genome map is utilized as the critical information in the whole procedure. Sequences that share any splice sites are grouped together to define an EST cluster in a manner similar to that of the genome-based version of the UniGene algorithm. Transcript assembly is achieved using graph theory that represents the exon connectivity in each cluster as a directed acyclic graph (DAG). Distinct paths along exons correspond to possible gene models encompassing all alternative splicing events. EST sequences in each cluster are subclustered further according to the compatibility with gene structure of each splice variant, and they can be regarded as clone evidence for the corresponding isoform. The reliability of each isoform is assessed from the nature of cluster members and from the minimum number of clones required to reconstruct all exons in the transcript.