Computational and experimental analysis identifies many novel human genes.
Because of advances in automation, human genomic sequences are being deposited in public databases at a dramatic rate. However, the process of detecting genes in these sequences is still something of an art. Here we describe the implementation and testing of a relatively straightforward computational approach, the Virtual Transcribed Sequence project, which analyzes their gene content using the gene prediction program GENSCAN (GENSCAN 1.0 1,2) in combination with similarity-based methods. This approach identifies many novel human genes not found even in EST databases.
Miyajima
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Department of Genome Informatics, Kazusa DNA Research Institute, Chiba, Japan.
Burge
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CB
Saito
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D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D020224:Expressed Sequence Tags; D005796:Genes / Q000235:genetics*; D015894:Genome, Human; D006801:Humans; D008969:Molecular Sequence Data; D005810:Multigene Family / Q000235:genetics; D016366:Open Reading Frames / Q000235:genetics*; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry / Q000235:genetics; D012333:RNA, Messenger / Q000032:analysis / Q000235:genetics; D015203:Reproducibility of Results; D020133:Reverse Transcriptase Polymerase Chain Reaction; D016415:Sequence Alignment; D017385:Sequence Homology; D012984:Software
D011506:Proteins; D012333:RNA, Messenger