Analysis of EST-Driven Gene Annotation in Human Genomic Sequence

Abstract

We have performed a systematic analysis of gene identification in genomic sequence by similarity search against expressed sequence tags (ESTs) to assess the suitability of this method for automated annotation of the human genome. A BLAST-based strategy was constructed to examine the potential of this approach, and was applied to test sets containing all human genomic sequences longer than 5 kb in public databases, plus 300 kb of exhaustively characterized benchmark sequence. At high stringency, 70%–90% of all annotated genes are detected by near-identity to EST sequence; >95% of ESTs aligning with well-annotated sequences overlap a gene. These ESTs provide immediate access to the corresponding cDNA clones for follow-up laboratory verification and subsequent biologic analysis. At lower stringency, up to 97% of annotated genes were identified by similarity to ESTs. The apparent false-positive rate rose to 55% of ESTs among all sequences and 20% among benchmark sequences at the lowest stringency, indicating that many genes in public database entries are unannotated. Approximately half of the alignments span multiple exons, and thus aid in the construction of gene predictions and elucidation of alternative splicing. In addition, ESTs from multiple cDNA libraries frequently cluster over genes, providing a starting point for crude expression profiles. Clone IDs may be used to form EST pairs, and particularly to extend models by associating alignments of lower stringency with high-quality alignments. These results demonstrate that EST similarity search is a practical general-purpose annotation technique that complements pattern recognition methods as a tool for gene characterization.