Algorithms and software for support of gene identification experiments

Genomic shotgun array: a procedure linking large-scale DNA sequencing with regional transcript mapping

To facilitate transcript mapping and to investigate alterations in genomic structure and gene expression in a defined genomic target, we developed a novel microarray-based method to detect transcriptional activity of the human chromosome 4q22-24 region. Loss of heterozygosity of human 4q22-24 is frequently observed in hepatocellular carcinoma (HCC). One hundred and eighteen well-characterized genes have been identified from this region. We took previously sequenced shotgun subclones as templates to amplify overlapping sequences for the genomic segment and constructed a chromosome-region-specific microarray. Using genomic DNA fragments as probes, we detected transcriptional activity from within this region among five different tissues. The hybridization results indicate that there are new transcripts that have not yet been identified by other methods. The existence of new transcripts encoded by genes in this region was confirmed by PCR cloning or cDNA library screening. The procedure reported here allows coupling of shotgun sequencing with transcript mapping and, potentially, detailed analysis of gene expression and chromosomal copy of the genomic sequence for the putative HCC tumor suppressor gene(s) in the 4q candidate region.

The evolving role of information technology in the drug discovery process

Information technologies for chemical structure prediction, heterogeneous database access, pattern discovery, and systems and molecular modeling have evolved to become core components of the modern drug discovery process. As this evolution continues, the balance between in silico modeling and 'wet' chemistry will continue to shift and it might eventually be possible to step through the discovery pipeline without the aid of traditional laboratory techniques. Rapid advances in the industrialization of gene sequencing combined with databases of protein sequence and structure have created a target-rich but lead-poor environment. During the next decade, newer information technologies that facilitate the molecular modeling of drug-target interactions are likely to shift this balance towards molecular-based personalized medicine -- the ultimate goal of the drug discovery process.

Gene structure prediction by spliced alignment of genomic DNA with protein sequences: increased accuracy by differential splice site scoring

Gene identification in genomic DNA from eukaryotes is complicated by the vast combinatorial possibilities of potential exon assemblies. If the gene encodes a protein that is closely related to known proteins, gene identification is aided by matching similarity of potential translation products to those target proteins. The genomic DNA and protein sequences can be aligned directly by scoring the implied residues of in-frame nucleotide triplets against the protein residues in conventional ways, while allowing for long gaps in the alignment corresponding to introns in the genomic DNA. We describe a novel method for such spliced alignment. The method derives an optimal alignment based on scoring for both sequence similarity of the predicted gene product to the protein sequence and intrinsic splice site strength of the predicted introns. Application of the method to a representative set of 50 known genes from Arabidopsis thaliana showed significant improvement in prediction accuracy compared to previous spliced alignment methods. The method is also more accurate than ab initio gene prediction methods, provided sufficiently close target proteins are available. In view of the fast growth of public sequence repositories, we argue that close targets will be available for the majority of novel genes, making spliced alignment an excellent practical tool for high-throughput automated genome annotation.

SST versus EST in gene recognition

The expressed sequence tag (EST) data provide a powerful tool for identification of transcribed DNA sequences. However, as EST are relatively short, many exons are poorly covered by EST, thus reducing the utility of EST data. Recently, signature sequence tag (SST) fingerprints were proposed as an alternative to EST fingerprints. Given a fingerprint set of probes, SST of a clone is a subset of probes from the fingerprint set that hybridize with the clone. We demonstrate that besides being a powerful technique for screening cDNA libraries, SST technology provides for very accurate gene predictions. Even with a small fingerprint set (600-800 probes), SST-based gene recognition outperforms many conventional and EST-based methods. The increase in the size of the fingerprint set to 1500 probes provides almost perfect gene recognition. Even more importantly, SST-based gene predictions miss very few exons and, therefore, provide an opportunity to bypass the cDNA sequencing step on the way from finished genomic sequence to mutation detection in gene-hunting projects. Because SST data can be obtained in a highly parallel and inexpensive way, SST technology has a potential of complementing EST technology for gene hunting.